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  • 1.
    Abdullah, Gamil M. S.
    et al.
    Department of Civil Engineering, College of Engineering, Najran University, Najran, Saudi Arabia.
    Chohan, Imran Mir
    Department of Mechanical Engineering, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Malaysia.
    Ali, Mohsin
    Graduate School of Urban Innovation, Department of Civil Engineering, Yokohama National University, Kanagawa, Japan.
    Bheel, Naraindas
    Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Malaysia.
    Ahmad, Mahmood
    Institute of Energy Infrastructure, Universiti Tenaga Nasional, Kajang, Malaysia; Department of Civil Engineering, University of Engineering and Technology Peshawar (Bannu Campus), Bannu, Pakistan.
    Najeh, Taoufik
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Gamil, Yaser
    Department of Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, Selangor, Malaysia.
    Almujibah, Hamad R.
    Department of Civil Engineering, College of Engineering, Taif University, Taif, Saudi Arabia.
    Effect of titanium dioxide as nanomaterials on mechanical and durability properties of rubberised concrete by applying RSM modelling and optimizations2024Inngår i: Frontiers in Materials, E-ISSN 2296-8016, Vol. 11, artikkel-id 1357094Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The use of rubber aggregates derived from discarded rubber tyres in concrete is a pioneering approach to replacing natural aggregate (NA) and promoting sustainable building practices. Recycled aggregate in concrete serves the dual purpose of alleviating the accumulation of discarded rubber tyres on the planet and providing a more sustainable alternative to decreasing natural aggregate. Due to fact that the crumb rubber (CR) decreases the strength when used in concrete, incorporating titanium dioxide (TiO2) as a nanomaterial to counteract the decrease in strength of crumb rubber concrete is a potential solution. Response Surface Methodology was developed to generate sixteen RUNs which contains different mix design by providing two input parameters like TiO2 at 1%, 1.5%, and 2% by cement weight and CR at 10%, 20%, and 30% as substitutions for volume of sand. These mixtures underwent testing for 28 days to evaluate their mechanical, deformation, and durability properties. Moreover, the compressive strength, tensile strength, flexural strength and elastic modulus were recorded by 51.40 MPa, 4.47 MPa, 5.91 MPa, and 40.15 GPa when 1.5% TiO2 and 10% CR were added in rubberised concrete after 28 days respectively. Furthermore, the incorporation of TiO2 led to reduced drying shrinkage and sorptivity in rubberized concrete, especially with increased TiO2 content. The study highlights that TiO2 inclusion refines pore size and densifies the interface between cement matrix and aggregate in hardened rubberized concrete. This transformative effect results in rubberized concrete demonstrating a commendable compressive strength comparable to normal concrete.

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  • 2.
    Abuhussain, Mohammed Awad
    et al.
    Architectural Engineering Department, College of Engineering, Najran University, Najran, Saudi Arabia.
    Ahmad, Ayaz
    Department of Civil Engineering, COMSATS University Islamabad, Abbottabad 22060, Pakistan.
    Amin, Muhammad Nasir
    Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi Arabia.
    Althoey, Fadi
    Department of Civil Engineering, College of Engineering, Najran University, Najran, Saudi Arabia.
    Gamil, Yaser
    Department of Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500 Bandar Sunway, Selangor, Malaysia.
    Najeh, Taoufik
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Data-driven approaches for strength prediction of alkali-activated composites2024Inngår i: Case Studies in Construction Materials, E-ISSN 2214-5095, Vol. 20, artikkel-id e02920Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Alkali-activated composites (AACs) have attracted considerable interest as a promising alternative to reduce CO2 emissions from Portland cement production and advance the decarbonisation of concrete construction. This study describes the data-driven predictive modelling to anticipate the compressive strength (CS) of AACs. Four different modelling techniques have been chosen to forecast the CS of AACs using the selected data set. The decision tree (DT), multi-layer perceptron (MLP), bagging regressor (BR), and AdaBoost regressor (AR) were employed to investigate the precision level of each model. When it comes to predicting the CS of AACs, the results show that the AR model performs better than the BR model, the MLP model, and the DT model by providing a higher value for the coefficient of determination, which is equal to 0.91, and a lower MAPE value, which is equal to 13.35%. However, the accuracy level of the BR model was very near to that of the AR model, with the R2 value suggesting a value of 0.90 and the MAPE value indicating a value of 14.43%. Moreover, the graphical user interface has also been developed for the strength prediction of alkali-activated composites, making it easy to get the required output from the selected inputs.

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  • 3.
    Ahmad, Hilal
    et al.
    School of Civil and Resource Engineering, University of Science and Technology Beijing, 100083, Beijing, China.
    Alam, Mehtab
    Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, District Swabi, 23640, Topi, Khyber Pakhtunkhwa, Pakistan.
    Yinghua, Zhang
    School of Civil and Resource Engineering, University of Science and Technology Beijing, 100083, Beijing, China.
    Najeh, Taoufik
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Gamil, Yaser
    Department of Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500, Bandar Sunway, Selangor, Malaysia.
    Hameed, Sajid
    Dasu Hydropower Consultant, Dasu, District Kohistan, Pakistan.
    Landslide risk assessment integrating susceptibility, hazard, and vulnerability analysis in Northern Pakistan2024Inngår i: Discover Applied Sciences, E-ISSN 3004-9261, Vol. 6, nr 1, artikkel-id 7Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The purpose of this study is to assess the landslide risk for Hunza–Nagar Valley (Northern Pakistan). In this study, different conditioning factors, e.g., topographical, geomorphological, climatic, and geological factors were considered. Two machine learning approaches, i.e., logistic regression and artificial neural network were used to develop landslide susceptibility maps. The accuracy test was carried out using the receiving operative characteristic (ROC) curve. Which showed that the success and prediction rates of LR model is 82.60 and 81.60%, while 77.90 and 75.40%, for the ANN model. Due to the physiographic condition of the area, the rainfall density was considered as the primary triggering factor and landslide index map was generated. Moreover, using the Aster data the land cover (LC) map was developed. The settlements were extracted from the LC map and used as the elements at risk and hence, the vulnerability index was developed. Finally, the landslide risk map (LRM) for the Hunza–Nagar valley was developed. The LRM indicated that 37.25 (20.21 km2) and 47.64% (25.84 km2) of the total settlements lie in low and very high-risk zones. This landslide risk map can help decision-makers for potential land development and landslide countermeasures.

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  • 4.
    Ajeel, Raheem K.
    et al.
    Solar Energy Research Institute, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia.
    Fayyadh, Saba N.
    Center of Advanced Materials and Renewable Resources, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia.
    Ibrahim, Adnan
    Solar Energy Research Institute, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia.
    Sultan, Sakhr M.
    Solar Energy Research Institute, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia.
    Najeh, Taoufik
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Comprehensive analysis of heat transfer and pressure drop in square multiple impingement jets employing innovative hybrid nanofluids2024Inngår i: Results in Engineering (RINENG), ISSN 2590-1230, Vol. 21, artikkel-id 101858Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This study presents a comprehensive analysis of heat transfer and pressure drop characteristics in square multiple impingement jets utilising a novel class of hybrid nanofluids. This study goes beyond the usual vertical impingement method by looking at the use of oblique impingement in a multiple impinging jet configuration with a hybrid nanofluid. Al2O3–Cu/water with different volume fractions () such as 0.1%, 0.33%, 0.75%, and 1.0% are employed as a working fluid. The purpose of the study is to clarify the impact of the jet angle (β), the jet Reynolds number (Re), extended jet height ), and different volume fraction () on the heat transfer behaviours of the curved target surface. The jet Reynolds number varies from 8000 to 24,000, and five different jet angles (β = 15 , 30°, 45°, 60°, 90 ) and three extended jet heights  = 0.2H, 0.4H, and 0.6H) are adopted. Outcomes disclosed that the highest values of Re and  greatly led to an increase in heat transfer rate and pressure drop of the system. It is uncovered that the heat transfer rate of binary hybrid nanofluids enhances with increasing volume fraction from for all jet angles and Re. Results also exposed that the angle of jet, which is 45°, gives a higher Nusselt number compared to other angles proposed in this study, and the maximum boost reaches 35%. Besides, despite the fact that reducing the height of the extended jet yields enhanced heat transfer rates in comparison to other methods, it concurrently results in an elevation in pressure drop. Finally, this research yielding insights that can be applied to improve the efficiency of heat transfer systems in practical applications.

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  • 5.
    Alyami, Mana
    et al.
    Department of Civil Engineering, College of Engineering, Najran University, Najran, Saudi Arabia.
    Khan, Majid
    Department of Civil Engineering, COMSATS University Islamabad, Abbottabad Campus, 22060, Pakistan.
    Fawad, Muhammad
    Silesian University of Technology Poland; Budapest University of Technology and Economics Hungary.
    Nawaz, R.
    Center for Applied Mathematics and Bioinformatics (CAMB), Gulf University for Science and Technology, 32093 Hawally, Kuwait.
    Hammad, Ahmed WA
    Macroview Projects, Sydney, Australia.
    Najeh, Taoufik
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Gamil, Yaser
    Department of Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500 Bandar Sunway, Selangor, Malaysia.
    Predictive modeling for compressive strength of 3D printed fiber-reinforced concrete using machine learning algorithms2024Inngår i: Case Studies in Construction Materials, E-ISSN 2214-5095, Vol. 20, artikkel-id e02728Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Three-dimensional (3D) printing in the construction industry is growing rapidly due to its inherent advantages, including intricate geometries, reduced waste, accelerated construction, cost-effectiveness, eco-friendliness, and improved safety. However, optimizing the mixture composition for 3D-printed concrete remains a formidable task, encompassing multiple variables and requiring a comprehensive trial-and-error experimentation process. Accordingly, this study used seven machine learning (ML) algorithms, including support vector regression (SVR), decision tree (DT), SVR-Bagging, SVR-Boosting, random forest (RF), gradient boosting (GB), and gene expression programming (GEP) for forecasting the compressive strength (CS) of 3D printed fiber-reinforced concrete (3DP-FRC). For model development, 299 data points were collected from experimental studies and split into two portions: 70% for model training and 30% for model validation. Various statistical metrics were employed to examine the accuracy and generalizability of the established models. The DT, RF, GB, and GEP models demonstrated higher accuracy in the validation set, achieving correlation (R) values of 0.987, 0.986, 0.986, and 0.98, respectively. The DT, RF, GB, and GEP models exhibited mean absolute error (MAE) scores of 4.644, 3.989, 3.90, and 5.691, respectively. Furthermore, the combination of SVR with boosting and bagging techniques slightly improved the accuracy compared to the individual SVR model. Additionally, the SHapley Additive exPlanations (SHAP) approach unveils the proportional significance of parameters in influencing the CS of 3DP-FRC. The SHAP technique revealed that water, silica fume, superplasticizer, sand content, and loading directions are the dominant parameters in estimating the CS of 3DP-FRC. The SHAP local interpretability unveils the intrinsic relationship between diverse input variables and their impacts on the strength of 3DP-FRC. The SHAP interpretability offers significant insights into the optimum mix proportion of 3DP-FRC.

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  • 6.
    Alyami, Mana
    et al.
    Department of Civil Engineering, College of Engineering, Najran University, Najran, Saudi Arabia.
    Khan, Majid
    Civil Engineering Department, COMSATS University Islamabad, Abbottabad Campus, 22060, Pakistan.
    Javed, Muhammad Faisal
    Civil Engineering Department, COMSATS University Islamabad, Abbottabad Campus, 22060, Pakistan.
    Ali, Mujahid
    Department of Transport Systems, Traffic Engineering and Logistics, Faculty of Transport and Aviation Engineering, Silesian University of Technology, Krasińskiego 8 Street, 40-019, Katowice, Poland.
    Alabduljabbar, Hisham
    Department of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia.
    Najeh, Taoufik
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Gamil, Yaser
    Department of Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500, Bandar Sunway, Selangor, Malaysia.
    Application of metaheuristic optimization algorithms in predicting the compressive strength of 3D-printed fiber-reinforced concrete2024Inngår i: Developments in the Built Environment, E-ISSN 2666-1659, Vol. 17, artikkel-id 100307Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    In recent years, the construction industry has been striving to make production faster and handle more complex architectural designs. Waste reduction, geometric freedom, lower construction costs, and speedy construction make the 3D-printed fiber-reinforced concrete (3DPFRC) alternative for future construction. However, achieving the optimum mixture composition for 3DPFRC remains a daunting task, entailing the consideration of multiple variables and necessitating an extensive trial-and-error experimental process. Therefore, this study investigated the application of different metaheuristic optimization algorithms to predict the compressive strength (CS) of 3DPFRC. A database of 299 data samples with 16 different input features was compiled from the experimental studies in the literature. Six metaheuristic algorithms, such as human felicity algorithm (HFA), differential evolution algorithm (DEA), nuclear reaction optimization (NRO), Harris hawks optimization (HHO), lightning search algorithm (LSA), and tunicate swarm algorithm (TSA) were applied to identify the optimal hyperparameter combination for the random forest (RF) model in predicting the CS of 3DPFRC. Different statistical metrics and 10-fold cross-validation were used to evaluate the accuracy of the models. The TSA-RF model exhibited superior performance compared to other models, achieving correlation (R), mean absolute error (MAE), and root mean square error (RMSE) values of 0.99, 2.10 MPa, and 3.59 MPa, respectively. The LSA-RF model also performed well, with R, MAE, and RMSE values of 0.99, 2.93 MPa, and 6.23 MPa, respectively. SHapley Additive exPlanation (SHAP) interpretability elucidates the intricate relationships between features and their effects on the CS, thereby offering invaluable insights for the performance-based mix proportion design of 3DPFRC.

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  • 7.
    Alyousef, Rayed
    et al.
    Department of Civil Engineering, College of Engineering, Prince Sattam Bin Abdulaziz University, 11942, Alkharj, Saudi Arabia.
    Nassar, Roz-Ud-Din
    Department of Civil and Infrastructure Engineering, American University of Ras Al Khaimah, United Arab Emirates.
    Fawad, Muhammad
    Silesian University of Technology Poland, Poland; Budapest University of Technology and Economics Hungary, Hungary.
    Farooq, Furqan
    NUST Institute of Civil Engineering (NICE), School of Civil and Environmental Engineering (SCEE), National University of Sciences and Technology (NUST), Sector H-12, Islamabad 44000, Pakistan.
    Gamil, Yaser
    Department of Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500 Bandar Sunway, Selangor, Malaysia.
    Najeh, Taoufik
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Predicting the properties of concrete incorporating graphene nano platelets by experimental and machine learning approaches2024Inngår i: Case Studies in Construction Materials, E-ISSN 2214-5095, Vol. 20, artikkel-id e03018Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Modern infrastructure requirements necessitate structural components with improved durability and strength properties. The incorporation of nanomaterials (NMs) into concrete emerges as a viable strategy to enhance both the durability and strength of the concrete. Nevertheless, the complexities inherent in these nanoscale cementitious composites are notably intricate. Traditional regression models face constraints in comprehensively capturing these intricate compositions. Thus, posing challenges in delivering precise and dependable estimations. Therefore, the current study utilized three machine learning (ML) methods, including artificial neural network (ANN), gene expression programming (GEP), and adaptive neuro-fuzzy inference system (ANFIS), in conjunction with experimental investigation to study the effect of the integration of graphene nanoplatelets (GNPs) on the electrical resistivity (ER) and compressive strength (CS) of concrete containing GNPs. Concrete containing GNPs demonstrated an improved fractional change in resistivity (FCR) and strength. The experimental measures depict that strength enhancement was notable at GNP concentrations of 0.05% and 0.1%, showcasing increases of 13.23% and 16.58%, respectively. Simultaneously, the highest observed FCR change reached −12.19% and −13%, respectively. The prediction efficacy of the three models proved to be outstanding in forecasting the characteristics of concrete containing GNPs. For CS, the GEP, ANN, and ANFIS models demonstrated impressive correlation coefficient (R) values of 0.974, 0.963, and 0.954, respectively. For electrical resistivity, the GEP, ANN, and ANFIS models exhibited high R-values of 0.999, 0.995, and 0.987, respectively. The comparative analysis of the models revealed that the GEP model delivered precise predictions for both ER and CS. The mean absolute error (MAE) of the GEP-CS model demonstrated a 14.51% reduction compared to the ANN-CS model and a substantial 48.15% improvement over the ANFIS-CS model. Similarly, the ANN-CS model displayed an MAE that was 38.14% lower compared to the ANFIS-CS model. Moreover, the MAE of the GEP-ER model demonstrated a 56.80% reduction compared to the ANN-CS model and a substantial 82.47% improvement over the ANFIS-CS model. The Shapley Additive explanation (SHAP) analysis provided that curing age exhibited the highest SHAP score. Thus, indicating its predominant contribution to CS prediction. In predicting ER, the graphene content exhibited the highest SHAP score, signifying its predominant contribution to ER estimation. This study highlights ML's accuracy in predicting the properties of concrete with graphene nanoplatelets, offering a fast and cost-effective alternative to time-consuming experiments.

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  • 8.
    Amin, Muhammad
    et al.
    Interdisciplinary Research Center for Hydrogen Technologies and Carbon Management (IRC-HTCM), King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia.
    Shah, Hamad Hussain
    Department of Engineering, University of Sannio, Benevento, Italy.
    Naveed, Abdul Basit
    Department of Chemistry, University of Louisville, Louisville, KY, United States.
    Iqbal, Amjad
    Faculty of Materials Engineering, Silesian University of Technology, Gliwice, Poland.
    Gamil, Yaser
    Department of Civil Engineering, School of Engineering, Monash University Malaysia, Subang Jaya, Selangor, Malaysia.
    Najeh, Taoufik
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Life cycle assessment of iron-biomass supported catalyst for Fischer Tropsch synthesis2024Inngår i: Frontiers in Chemistry, E-ISSN 2296-2646, Vol. 12, artikkel-id 1374739Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The iron-based biomass-supported catalyst has been used for Fischer-Tropsch synthesis (FTS). However, there is no study regarding the life cycle assessment (LCA) of biomass-supported iron catalysts published in the literature. This study discusses a biomass-supported iron catalyst’s LCA for the conversion of syngas into a liquid fuel product. The waste biomass is one of the source of activated carbon (AC), and it has been used as a support for the catalyst. The FTS reactions are carried out in the fixed-bed reactor at low or high temperatures. The use of promoters in the preparation of catalysts usually enhances C5+ production. In this study, the collection of precise data from on-site laboratory conditions is of utmost importance to ensure the credibility and validity of the study’s outcomes. The environmental impact assessment modeling was carried out using the OpenLCA 1.10.3 software. The LCA results reveals that the synthesis process of iron-based biomass supported catalyst yields a total impact score in terms of global warming potential (GWP) of 1.235E + 01 kg CO2 equivalent. Within this process, the AC stage contributes 52% to the overall GWP, while the preparation stage for the catalyst precursor contributes 48%. The comprehensive evaluation of the iron-based biomass supported catalyst’s impact score in terms of human toxicity reveals a total score of 1.98E−02 kg 1,4-dichlorobenzene (1,4-DB) equivalent.

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  • 9.
    Asghar, Raheel
    et al.
    College of Civil Engineering and Architecture, Shandong University of Science and Technology, 266590, Qingdao, China; Department of Civil Engineering, COMSATS University Islamabad, Abbottabad Campus, 22060, Abbottabad, Pakistan.
    Javed, Muhammad Faisal
    Department of Civil Engineering, GIK Institute of Engineering Sciences and Technology, 23640, Swabi, Pakistan.
    Ali, Mujahid
    Department of Transport Systems, Traffic Engineering and Logistics, Faculty of Transport and Aviation Engineering, Silesian University of Technology, Krasińskiego 8 Street, 40-019, Katowice, Poland.
    Najeh, Taoufik
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Gamil, Yaser
    Department of Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500, Bandar Sunway, Selangor, Malaysia.
    Numerical and artificial intelligence based investigation on the development of design guidelines for pultruded GFRP RHS profiles subjected to web crippling2024Inngår i: Scientific Reports, E-ISSN 2045-2322, Vol. 14, artikkel-id 10135Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This article presents a numerical and artificial intelligence (AI) based investigation on the web crippling performance of pultruded glass fiber reinforced polymers’ (GFRP) rectangular hollow section (RHS) profiles subjected to interior-one-flange (IOF) loading conditions. To achieve the desired research objectives, a finite element based computational model was developed using one of the popular simulating software ABAQUS CAE. This model was then validated by utilizing the results reported in experimental investigation-based article of Chen and Wang. Once the finite element model was validated, an extensive parametric study was conducted to investigate the aforementioned phenomenon on the basis of which a comprehensive, universal, and coherent database was assembled. This database was then used to formulate the design guidelines for the web crippling design of pultruded GFRP RHS profiles by employing AI based gene expression programming (GEP). Based on the findings of numerical investigation, the web crippling capacity of abovementioned structural profiles subjected to IOF loading conditions was found to be directly related to that of section thickness and bearing length whereas inversely related to that of section width, section height, section’s corner radii, and profile length. On the basis of the findings of AI based investigation, the modified design rules proposed by this research were found to be accurately predicting the web crippling capacity of aforesaid structural profiles. This research is a significant contribution to the literature on the development of design guidelines for pultruded GFRP RHS profiles subjected to web crippling, however, there is still a lot to be done in this regard before getting to the ultimate conclusions.

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  • 10.
    Azad, Md. Alquamar
    et al.
    Department of Geology (Centre for Advanced Studies), University of Delhi, 110007, Delhi, India.
    Najeh, Taoufik
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Raina, Autar K.
    CSIR-Central Institute of Mining and Fuel Research (Ministry of Science & Technology, Govt. of India), Nagpur Research Center 17/C, Telangkhedi Area, Civil Lines, 440001, Nagpur, Maharashtra, India; Academy of Scientific and Innovative Research (AcSIR), CSIR-Human Resource Development Centre (CSIR-HRDC) Campus, Postal Staff College Area, Sector 19, Kamla Nehru Nagar, 201 002, Ghaziabad, Uttar Pradesh, India.
    Singh, Neelratan
    CSIR-Central Institute of Mining and Fuel Research (Ministry of Science & Technology, Govt. of India), Nagpur Research Center 17/C, Telangkhedi Area, Civil Lines, 440001, Nagpur, Maharashtra, India.
    Ansari, Abdullah
    Earthquake Monitoring Center, Sultan Qaboos University, 123, Muscat, Oman; Department of Civil Engineering, Inha University, 22212, Incheon, South Korea.
    Ali, Mujahid
    Department of Transport Systems, Traffic Engineering and Logistics, Silesian University of Technology, Krasińskiego 8 Street, Katowice, Poland.
    Fissha, Yewuhalashet
    Department of Geosciences, Geotechnology and Materials Engineering for Resources, Graduate School of International Resource Sciences, Akita University, 010-8502, Akita, Japan.
    Gamil, Yaser
    Department of Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500, Bandar Sunway, Selangor, Malaysia.
    Singh, S. K.
    Department of Geology (Centre for Advanced Studies), University of Delhi, 110007, Delhi, India.
    Development of correlations between various engineering rockmass classification systems using railway tunnel data in Garhwal Himalaya, India2024Inngår i: Scientific Reports, E-ISSN 2045-2322, Vol. 14, artikkel-id 10716Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Engineering rockmass classifications are an integral part of design, support and excavation procedures of tunnels, mines, and other underground structures. These classifications are directly linked to ground reaction and support requirements. Various classification systems are in practice and are still evolving. As different classifications serve different purposes, it is imperative to establish inter-correlatability between them. The rating systems and engineering judgements influence the assignment of ratings owing to cognition. To understand the existing correlation between different classification systems, the existing correlations were evaluated with the help of data of 34 locations along a 618-m-long railway tunnel in the Garhwal Himalaya of India and new correlations were developed between different rock classifications. The analysis indicates that certain correlations, such as RMR-Q, RMR-RMi, RMi-Q, and RSR-Q, are comparable to the previously established relationships, while others, such as RSR-RMR, RCR-Qn, and GSI-RMR, show weak correlations. These deviations in published correlations may be due to individual parameters of estimation or measurement errors. Further, incompatible classification systems exhibited low correlations. Thus, the study highlights a need to revisit existing correlations, particularly for rockmass conditions that are extremely complex, and the predictability of existing correlations exhibit high variations. In addition to augmenting the existing database, new correlations for metamorphic rocks in the Himalayan region have been developed and presented that can serve as a guide for future rock engineering projects in such formations and aid in developing appropriate excavation and rock support methodologies.

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  • 11.
    Bheel, Naraindas
    et al.
    Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS, Bandar Seri Iskandar Tronoh, 32610, Perak, Malaysia.
    Shams, Muhammad Alamgeer
    Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS, Bandar Seri Iskandar Tronoh, 32610, Perak, Malaysia.
    Sohu, Samiullah
    Department of Civil Engineering, The University of Larkano, Larkana, Sindh, Pakistan.
    Buller, Abdul Salam
    Department of Civil Engineering (TIEST), NED University of Engineering and Technology, Karachi, Sindh, Pakistan.
    Najeh, Taoufik
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Ismail, Fouad Ismail
    Department of Civil and Environmental Engineering, University of Nebraska-Lincoln, 1110 67Th Street, 68182-0178, Omaha, NE, USA.
    Benjeddou, Omrane
    Department of Civil Engineering, College of Engineering, Prince Sattam Bin Abdulaziz University, 11942, Alkharj, Saudi Arabia.
    A comprehensive study on the impact of human hair fiber and millet husk ash on concrete properties: response surface modeling and optimization2024Inngår i: Scientific Reports, E-ISSN 2045-2322, Vol. 14, nr 1, artikkel-id 13569Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Revolutionizing construction, the concrete blend seamlessly integrates human hair (HH) fibers and millet husk ash (MHA) as a sustainable alternative. By repurposing human hair for enhanced tensile strength and utilizing millet husk ash to replace sand, these materials not only reduce waste but also create a durable, eco-friendly solution. This groundbreaking methodology not only adheres to established structural criteria but also advances the concepts of the circular economy, representing a significant advancement towards environmentally sustainable and resilient building practices. The main purpose of the research is to investigate the fresh and mechanical characteristics of concrete blended with 10–40% MHA as a sand substitute and 0.5–2% HH fibers by applying response surface methodology modeling and optimization. A comprehensive study involved preparing 225 concrete specimens using a mix ratio of 1:1.5:3 with a water-to-cement ratio of 0.52, followed by a 28 day curing period. It was found that a blend of 30% MHA and 1% HH fibers gave the best compressive and splitting tensile strengths at 28 days, which were 33.88 MPa and 3.47 MPa, respectively. Additionally, the incorporation of increased proportions of MHA and HH fibers led to reductions in both the dry density and workability of the concrete. In addition, utilizing analysis of variance (ANOVA), response prediction models were created and verified with a significance level of 95%. The models' R2 values ranged from 72 to 99%. The study validated multi-objective optimization, showing 1% HH fiber and 30% MHA in concrete enhances strength, reduces waste, and promotes environmental sustainability, making it recommended for construction.

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  • 12.
    Bhutto, Shahzeb
    et al.
    Department of Civil Engineering, Mehran University of Engineering and Technology, Jamshoro 76090, Sindh, Pakistan.
    Abro, Fahad-ul-Rehman
    Department of Civil Engineering, Mehran University of Engineering and Technology, Jamshoro 76090, Sindh, Pakistan.
    Ali, Mohsin
    Graduate School of Urban Innovation, Department of Civil Engineering, Yokohama National University, Kanagawa, 240-8501, Japan.
    Buller, Abdul Salam
    Department of Civil Engineering, NED University Constitute College Thar Institute of Engineering, Science and Technology, 69230, Mithi, Tharparkar, Sindh, Pakistan.
    Bheel, Naraindas
    Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Tronoh, Perak, 32610, Malaysia.
    Gamil, Yaser
    Department of Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500 Bandar Sunway, Selangor, Malaysia.
    Najeh, Taoufik
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Deifalla, Ahmed Farouk
    Structural Engineering and Construction Management Department, Future University in Egypt, 11835, New Cairo, Egypt.
    Ragab, Adham E.
    Department of Industrial Engineering, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia.
    Almujibah, Hamad R.
    Department of Civil Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia.
    Effect of banana tree leaves ash as cementitious material on the durability of concrete against sulphate and acid attacks2024Inngår i: Heliyon, E-ISSN 2405-8440, Vol. 10, nr 7, artikkel-id e29236Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The construction industry's rapid growth poses challenges tied to raw material depletion and increased greenhouse gas emissions. To address this, alternative materials like agricultural residues are gaining prominence due to their potential to reduce carbon emissions and waste generation. In this context this research optimizes the use of banana leaves ash as a partial cement substitution, focusing on durability, and identifying the ideal cement-to-ash ratio for sustainable concrete. For this purpose, concrete mixes were prepared with BLA replacing cement partially in different proportions i.e. (0 %, 5 %, 10 %, 15 %, & 20 %) and were analyzed for their physical, mechanical and Durability (Acid and Sulphate resistance) properties. Compressive strength, acid resistance and sulphate resistance testing continued for 90 days with the intervals of 7, 28 and 90 days. The results revealed that up to 10 % incorporation of BLA improved compressive strength by 10 %, while higher BLA proportions (up to 20 %) displayed superior performance in durability tests as compared to the conventional mix. The results reveal the potentials of banana leave ash to refine the concrete matrix by formation of addition C–S–H gel which leads towards a better performance specially in terms of durability aspect. Hence, banana leaf ash (BLA) is an efficient concrete ingredient, particularly up to 10 % of the mix. Beyond this threshold, it's still suitable for applications where extreme strength isn't the primary concern, because there may be a slight reduction in compressive strength.

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  • 13.
    Dodo, Yakubu
    et al.
    Architectural Engineering Department, College of Engineering, Najran University, Najran, Kingdom of Saudi Arabia.
    Arif, Kiran
    Department of Computer Science, COMSATS University Islamabad, Wah Campus, 47040, Islamabad, Pakistan.
    Alyami, Mana
    Department of Civil Engineering, College of Engineering, Najran University, Najran, Saudi Arabia.
    Ali, Mujahid
    Department of Transport Systems, Traffic Engineering and Logistics, Faculty of Transport and Aviation Engineering, Silesian University of Technology, Krasińskiego 8 Street, 40-019, Katowice, Poland.
    Najeh, Taoufik
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Gamil, Yaser
    Department of Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500, Bandar Sunway, Selangor, Malaysia.
    Estimation of compressive strength of waste concrete utilizing fly ash/slag in concrete with interpretable approaches: optimization and graphical user interface (GUI)2024Inngår i: Scientific Reports, E-ISSN 2045-2322, Vol. 14, nr 1, artikkel-id 4598Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Geo-polymer concrete has a significant influence on the environmental condition and thus its use in the civil industry leads to a decrease in carbon dioxide (CO2) emission. However, problems lie with its mixed design and casting in the field. This study utilizes supervised artificial-based machine learning algorithms (MLAs) to anticipate the mechanical characteristic of fly ash/slag-based geopolymer concrete (FASBGPC) by utilizing AdaBoost and Bagging on MLPNN to make an ensemble model with 156 data points. The data consist of GGBS (kg/m3), Alkaline activator (kg/m3), Fly ash (kg/m3), SP dosage (kg/m3), NaOH Molarity, Aggregate (kg/m3), Temperature (°C) and compressive strength as output parameter. Python programming is utilized in Anaconda Navigator using Spyder version 5.0 to predict the mechanical response. Statistical measures and validation of data are done by splitting the dataset into 80/20 percent and K-Fold CV is employed to check the accurateness of the model by using MAE, RMSE, and R2. Statistical analysis relies on errors, and tests against external indicators help determine how well models function in terms of robustness. The most important factor in compressive strength measurements is examined using permutation characteristics. The result reveals that ANN with AdaBoost is outclassed by giving maximum enhancement with R2 = 0.914 and shows the least error with statistical and external validations. Shapley analysis shows that GGBS, NaOH Molarity, and temperature are the most influential parameter that has significant content in making FASBGPC. Thus, ensemble methods are suitable for constructing prediction models because of their strong and reliable performance. Furthermore, the graphical user interface (GUI) is generated through the process of training a model that forecasts the desired outcome values when the corresponding inputs are provided. It streamlines the process and provides a useful tool for applying the model's abilities in the field of civil engineering.

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  • 14.
    Fawad, Muhammad
    et al.
    Department of Structural Engineering, Faculty of Civil Engineering, Silesian University of Technology, Akademicka 2, 44-100, Gliwice, Poland; Budapest University of Technology and Economics Hungary, Budapest, Hungary.
    Alabduljabbar, Hisham
    Department of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, 11942, Al-Kharj, Saudi Arabia.
    Farooq, Furqan
    NUST Institute of Civil Engineering (NICE), School of Civil and Environmental Engineering (SCEE), National University of Sciences and Technology (NUST), Sector H-12, 44000, Islamabad, Pakistan; Western Caspian University, Baku, Azerbaijan.
    Najeh, Taoufik
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Gamil, Yaser
    Department of Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500, Bandar Sunway, Selangor, Malaysia.
    Ahmed, Bilal
    Department of Structural Engineering, Faculty of Civil Engineering, Silesian University of Technology, Akademicka 2, 44-100, Gliwice, Poland.
    Indirect prediction of graphene nanoplatelets-reinforced cementitious composites compressive strength by using machine learning approaches2024Inngår i: Scientific Reports, E-ISSN 2045-2322, Vol. 14, artikkel-id 14252Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Graphene nanoplatelets (GrNs) emerge as promising conductive fillers to significantly enhance the electrical conductivity and strength of cementitious composites, contributing to the development of highly efficient composites and the advancement of non-destructive structural health monitoring techniques. However, the complexities involved in these nanoscale cementitious composites are markedly intricate. Conventional regression models encounter limitations in fully understanding these intricate compositions. Thus, the current study employed four machine learning (ML) methods such as decision tree (DT), categorical boosting machine (CatBoost), adaptive neuro-fuzzy inference system (ANFIS), and light gradient boosting machine (LightGBM) to establish strong prediction models for compressive strength (CS) of graphene nanoplatelets-based materials. An extensive dataset containing 172 data points was gathered from published literature for model development. The majority portion (70%) of the database was utilized for training the model while 30% was used for validating the model efficacy on unseen data. Different metrics were employed to assess the performance of the established ML models. In addition, SHapley Additve explanation (SHAP) for model interpretability. The DT, CatBoost, LightGBM, and ANFIS models exhibited excellent prediction efficacy with R-values of 0.8708, 0.9999, 0.9043, and 0.8662, respectively. While all the suggested models demonstrated acceptable accuracy in predicting compressive strength, the CatBoost model exhibited exceptional prediction efficiency. Furthermore, the SHAP analysis provided that the thickness of GrN plays a pivotal role in GrNCC, significantly influencing CS and consequently exhibiting the highest SHAP value of + 9.39. The diameter of GrN, curing age, and w/c ratio are also prominent features in estimating the strength of graphene nanoplatelets-based cementitious materials. This research underscores the efficacy of ML methods in accurately forecasting the characteristics of concrete reinforced with graphene nanoplatelets, providing a swift and economical substitute for laborious experimental procedures. It is suggested that to improve the generalization of the study, more inputs with increased datasets should be considered in future studies.

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  • 15.
    Gamil, Yaser
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Byggkonstruktion och brand.
    Al-Sarafi, A.H.
    Department of Civil Engineering, Faculty of Engineering, Universiti Putra Malaysia, Seri Kembangan, Malaysia.
    Najeh, Taoufik
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Post COVID-19 pandemic possible business continuity strategies for construction industry revival a preliminary study in the Malaysian construction industry2023Inngår i: International Journal of Disaster Resilience in the Built Environment, ISSN 1759-5908, E-ISSN 1759-5916, Vol. 14, nr 5, s. 640-654Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Purpose - COVID-19 pandemic has unprecedentedly shattered the entire world economy and development. Without exclusion, the construction industry has undergone very extreme disruption. Many projects have been suspended, many employees lost their jobs and many construction companies bankrupted. This study aims to explore the possible business continuity plans, a roadmap to recovery and strategies to revive the construction industry after COVID-19.

    Design/methodology/approach - Mix mode method approach was used to address the research problem, and that includes interviews with 16 selected construction experts who have been working in the Malaysian industry for more than 10 years and a questionnaire with 187 construction practitioners. The aim of conducting the interviews is to get an insight into the current impact of the pandemic on the construction industry, and the questionnaire aims to statistically rank the importance of revival strategies using a Likert-type scale. Further, the data were analysed using a univariate approach by calculating the relative importance index to assess the importance of each strategy.

    Findings - The findings showed that the pandemic has severely affected the Malaysian construction industry in many aspects and effective restoration strategies are necessary to cope with the changes. The strategies were categorized into four different aspects includes health and practice, technology, operational, legal and governmental strategies. The finding shows that the topmost ranked strategy in terms of importance is introducing COVID-compliant operating procedures and protocols on-site by adjusting current working procedures, urgent government stimuli (loan, financial aid to the affected firms) and other financial incentives, leveraging digital and online technology for virtual meeting and communication, comprehensive and revision study of the health guidelines to suit construction activities and digital transformation of work. The study suggests a more in-depth study to evaluate the impact and assess the success of strategies for the betterment of the future of the Malaysian construction industry.

    Practical implications - The study presented a better understanding of the possible business continuity strategies for construction industry revival, which are important for decision makers and the government to reconsider for the revival of the industry. The findings also are of interest to the construction stakeholders.

    Originality/value - There have been many research addressing the impact of the pandemic on the construction industry, but less are available on the possible strategies for continual and revival of construction industry amid and after the pandemic. It is, therefore, crucial to address this topic, especially the assessment of these strategies based on their importance.

  • 16.
    Gamil, Yaser
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Byggkonstruktion och brand.
    Nilimaa, Jonny
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Byggkonstruktion och brand.
    Najeh, Taufik
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Cwirzen, Andrzej
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Byggkonstruktion och brand.
    Formwork pressure prediction in cast-in-place self-compacting concrete using deep learning2023Inngår i: Automation in Construction, ISSN 0926-5805, E-ISSN 1872-7891, Vol. 151, artikkel-id 104869Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The prediction of formwork pressure exerted by self-compacting concrete (SCC) remains a challenge not only to researchers but also to engineers and contractors on the construction site. This article aims to utilize shallow neural networks (SNN) and deep neural networks (DNN) using Long Short-Term Memory (LSTM) approach to develop a prediction model based on real-time data acquitted from controllable laboratory testing series. A test setup consisting of a two-meter-high column, ø160 mm, was prepared and tested in the laboratory. A digital pressure monitoring system was used to collect and transfer the data to the cloud on a real-time basis. The pressure was monitored during- and after casting, following the pressure build-up and reduction, respectively. The two main parameters affecting the form pressure, i.e., casting rate and slump flow, were varied to collect a wide range of input data for the analysis. The proposed model by DNN was able to accurately predict the pressure behavior based on the input data from the laboratory tests with high-performance indicators and multiple hidden layers. The results showed that the pressure is significantly affected by the casting rate, while the slump flow had rather lower impact. The proposed model can be a useful and reliable tool at the construction site to closely predict the pressure development and the effects of variations in casting rate and slump flow. The model provides the opportunity to increase safety and speeding up construction while avoiding costly and time-consuming effects of oversized formwork.

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  • 17.
    Habib, Nudrat
    et al.
    Department of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, Pakistan.
    Saqib, Muhammad
    Department of Civil Engineering, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, Pakistan.
    Najeh, Taoufik
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Gamil, Yaser
    Department of Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500 Bandar Sunway, Selangor, Malaysia.
    Eco-Transformation of construction: Harnessing machine learning and SHAP for crumb rubber concrete sustainability2024Inngår i: Heliyon, E-ISSN 2405-8440, Vol. 10, nr 5, artikkel-id e26927Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Researchers have focused their efforts on investigating the integration of crumb rubber as a substitute for conventional aggregates and cement in concrete. Nevertheless, the manufacture of crumb rubber concrete (CRC) has been linked to the release of noxious pollutants, hence presenting potential environmental hazards. Rather than developing novel CRC formulations, the primary objective of this work is to construct an extensive database by leveraging prior research efforts. The study places particular emphasis on two crucial concrete properties: compressive strength (fc') and tensile strength (fts). The database includes a total of 456 data points for fc' and 358 data points for fts, focusing on nine essential characteristics that have a substantial impact on both attributes. The research employs several machine learning algorithms, including both individual and ensemble methods, to undertake a comprehensive analysis of the created databases for fc' and fts. In order to ascertain the correctness of the models, a comparative analysis of machine learning techniques, namely decision tree (DT) and random forest (RF), is conducted using statistical evaluation. Cross-validation approaches are used in order to address the possible issues of overfitting. Furthermore, the Shapley additive explanations (SHAP) approach is used to investigate the influence of input parameters and their interrelationships. The findings demonstrate that the RF methodology has superior performance compared to other ensemble techniques, as shown by its lower error rates and higher coefficient of determination (R2) of 0.87 and 0.85 for fc'; and fts respectively. When comparing ensemble approaches, it can be seen that AdaBoost outperforms bagging by 6 % for both outcome models and individual decision tree learners by 17% and 21% for fc'; and fts respectively in terms of performance. The average accuracy of AdaBoost algorithm for both the models is 84%. Significantly, the age and the inclusion of crumb rubber in CRC are identified as the primary criteria that have a substantial influence on the mechanical properties of this particular kind of concrete.

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  • 18.
    Inqiad, Waleed Bin
    et al.
    Military College of Engineering (MCE), National University of Science and Technology (NUST), Islamabad 44000, Pakistan.
    Siddique, Muhammad Shahid
    Military College of Engineering (MCE), National University of Science and Technology (NUST), Islamabad 44000, Pakistan.
    Alarifi, Saad S.
    Department of Geology and Geophysics, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia.
    Butt, Muhammad Jamal
    COMSATS University Islamabad, Abbottabad Campus, Pakistan.
    Najeh, Taoufik
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Gamil, Yaser
    Department of Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500 Bandar Sunway, Selangor, Malaysia.
    Comparative analysis of various machine learning algorithms to predict 28-day compressive strength of Self-compacting concrete2023Inngår i: Heliyon, E-ISSN 2405-8440, Vol. 9, nr 11, artikkel-id e22036Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Construction industry is indirectly the largest source of CO2 emissions in the atmosphere, due to the use of cement in concrete. These emissions can be reduced by using industrial waste materials in place of cement. Self-Compacting Concrete (SCC) is a promising material to enhance the use of industrial wastes in concrete. However, there are very few methods available for accurate prediction of its strength, therefore, reliable models for estimating 28-day Compressive Strength (C–S) of SCC are developed in current study by using three Machine Learning (ML) algorithms including Multi Expression Programming (MEP), Extreme Gradient Boosting (XGB), and Random Forest (RF). The ML models were meticulously developed using a dataset of 231 points collected from internationally published literature considering seven most influential parameters including cement content, quantities of fly ash and silica fume, water content, coarse aggregate, fine aggregate, and superplasticizer dosage to predict C–S. The developed models were evaluated using different statistical errors including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), coefficient of determination (R2) etc. The results showed that the XGB model outperformed the MEP and RF model in terms of accuracy with a correlation R2 = 0.998 compared to 0.923 for MEP and 0.986 for RF. Similar trend was observed for other error metrices. Thus, XGB is the most accurate model for estimating C–S of SCC. However, it is pertinent to mention here that it does not give its output in the form of an empirical equation like MEP model. The construction of these empirical models will help to efficiently estimate C–S of SCC for practical purposes.

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  • 19. Javed, Muhammad Faisal
    et al.
    Fawad, Muhammad
    Silesian University of Technology Poland, Gliwice, Poland; Budapest University of Technology and Economics Hungary, Budapest, Hungary.
    Lodhi, Rida
    Department of Urban and Regional Planning, National University of Sciences and Technology (NUST), Islamabad, Pakistan.
    Najeh, Taoufik
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Gamil, Yaser
    Department of Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500, Bandar Sunway, Selangor, Malaysia.
    Forecasting the strength of preplaced aggregate concrete using interpretable machine learning approaches2024Inngår i: Scientific Reports, E-ISSN 2045-2322, Vol. 14, artikkel-id 8381Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Preplaced aggregate concrete (PAC) also known as two-stage concrete (TSC) is widely used in construction engineering for various applications. To produce PAC, a mixture of Portland cement, sand, and admixtures is injected into a mold subsequent to the deposition of coarse aggregate. This process complicates the prediction of compressive strength (CS), demanding thorough investigation. Consequently, the emphasis of this study is on enhancing the comprehension of PAC compressive strength using machine learning models. Thirteen models are evaluated with 261 data points and eleven input variables. The result depicts that xgboost demonstrates exceptional accuracy with a correlation coefficient of 0.9791 and a normalized coefficient of determination (R2) of 0.9583. Moreover, Gradient boosting (GB) and Cat boost (CB) also perform well due to its robust performance. In addition, Adaboost, Voting regressor, and Random forest yield precise predictions with low mean absolute error (MAE) and root mean square error (RMSE) values. The sensitivity analysis (SA) reveals the significant impact of key input parameters on overall model sensitivity. Notably, gravel takes the lead with a substantial 44.7% contribution, followed by sand at 19.5%, cement at 15.6%, and Fly ash and GGBS at 5.9% and 5.1%, respectively. The best fit model i.e., XG-Boost model, was employed for SHAP analysis to assess the relative importance of contributing attributes and optimize input variables. The SHAP analysis unveiled the water-to-binder (W/B) ratio, superplasticizer, and gravel as the most significant factors influencing the CS of PAC. Furthermore, graphical user interface (GUI) have been developed for practical applications in predicting concrete strength. This simplifies the process and offers a valuable tool for leveraging the model's potential in the field of civil engineering. This comprehensive evaluation provides valuable insights to researchers and practitioners, empowering them to make informed choices in predicting PAC compressive strength in construction projects. By enhancing the reliability and applicability of predictive models, this study contributes to the field of preplaced aggregate concrete strength prediction.

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  • 20.
    Javed, Muhammad Faisal
    et al.
    Department of Civil Engineering, GIK Institute of Engineering Sciences and Technology, 23640, Swabi, Pakistan; Western Caspian University, Baku, Azerbaijan.
    Khan, Majid
    Civil Engineering Department, COMSATS University Islamabad, 22060, Abbottabad Campus, Pakistan.
    Fawad, Muhammad
    Silesian University of Technology, Gliwice, Poland; Budapest University of Technology and Economics Hungary, Budapest, Hungary.
    Alabduljabbar, Hisham
    Department of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam bin Abdulaziz University, 11942, Al-Kharj, Saudi Arabia.
    Najeh, Taoufik
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Gamil, Yaser
    Department of Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500, Bandar Sunway, Selangor, Malaysia.
    Comparative analysis of various machine learning algorithms to predict strength properties of sustainable green concrete containing waste foundry sand2024Inngår i: Scientific Reports, E-ISSN 2045-2322, Vol. 14, artikkel-id 14617Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The use of waste foundry sand (WFS) in concrete production has gained attention as an eco-friendly approach to waste reduction and enhancing cementitious materials. However, testing the impact of WFS in concrete through experiments is costly and time-consuming. Therefore, this study employs machine learning (ML) models, including support vector regression (SVR), decision tree (DT), and AdaBoost regressor (AR) ensemble model to predict concrete properties accurately. Moreover, SVR was employed in conjunction with three robust optimization algorithms: the firefly algorithm (FFA), particle swarm optimization (PSO), and grey wolf optimization (GWO), to construct hybrid models. Using 397 experimental data points for compressive strength (CS), 146 for elastic modulus (E), and 242 for split tensile strength (STS), the models were evaluated with statistical metrics and interpreted using the SHapley Additive exPlanation (SHAP) technique. The SVR-GWO hybrid model demonstrated exceptional accuracy in predicting waste foundry sand concrete (WFSC) strength characteristics. The SVR-GWO hybrid model exhibited correlation coefficient values (R) of 0.999 for CS and E, and 0.998 for STS. Age was found to be a significant factor influencing WFSC properties. The ensemble model (AR) also exhibited comparable prediction accuracy to the SVR-GWO model. In addition, SHAP analysis revealed an optimal content of input variables in the concrete mix. Overall, the hybrid and ensemble models showed exceptional prediction accuracy compared to individual models. The application of these sophisticated soft computing prediction techniques holds the potential to stimulate the widespread adoption of WFS in sustainable concrete production, thereby fostering waste reduction and bolstering the adoption of environmentally conscious construction practices.

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  • 21.
    Javed, Muhammad Faisal
    et al.
    Department of Civil Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi, Pakistan; National Institute of Transportation, National University of Sciences and Technology (NUST), Islamabad, Pakistan.
    Shahab, Muhammad Zubair
    Western Caspian University, Baku, Azerbaijan.
    Asif, Usama
    COMSATS University Islamabad, Abbottabad Campus, Abbottabad, Pakistan.
    Najeh, Taoufik
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Aslam, Fahid
    Department of Civil Engineering, Nazarbayev University, Astana, Kazakhstan.
    Ali, Mujahid
    Department of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, 11942, Al-Kharj, Saudi Arabia.
    Khan, Inamullah
    Department of Transport Systems, Traffic Engineering and Logistics, Faculty of Transport and Aviation Engineering, Silesian University of Technology, Krasińskiego 8 Street, 40-019, Katowice, Poland.
    Evaluation of machine learning models for predicting TiO2 photocatalytic degradation of air contaminants2024Inngår i: Scientific Reports, E-ISSN 2045-2322, Vol. 14, nr 1, artikkel-id 13688Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The escalation of global urbanization and industrial expansion has resulted in an increase in the emission of harmful substances into the atmosphere. Evaluating the effectiveness of titanium dioxide (TiO2) in photocatalytic degradation through traditional methods is resource-intensive and complex due to the detailed photocatalyst structures and the wide range of contaminants. Therefore in this study, recent advancements in machine learning (ML) are used to offer data-driven approach using thirteen machine learning techniques namely XG Boost (XGB), decision tree (DT), lasso Regression (LR2), support vector regression (SVR), adaBoost (AB), voting Regressor (VR), CatBoost (CB), K-Nearest Neighbors (KNN), gradient boost (GB), random Forest (RF), artificial neural network (ANN), ridge regression (RR), linear regression (LR1) to address the problem of estimation of TiO2 photocatalytic degradation rate of air contaminants. The models are developed using literature data and different methodical tools are used to evaluate the developed ML models. XGB, DT and LR2 models have high R2 values of 0.93, 0.926 and 0.926 in training and 0.936, 0.924 and 0.924 in test phase. While ANN, RR and LR models have lowest R2 values of 0.70, 0.56 and 0.40 in training and 0.62, 0.63 and 0.31 in test phase respectively. XGB, DT and LR2 have low MAE and RMSE values of 0.450 min-1/cm2, 0.494 min-1/cm2 and 0.49 min-1/cm2 for RMSE and 0.263 min-1/cm2, 0.285 min-1/cm2 and 0.29 min-1/cm2 for MAE in test stage. XGB, DT, and LR2 have 93% percent errors within 20% error range in training phase. XGB has 92% and DT, and LR2 have 94% errors with 20% range in test phase. XGB, DT, LR2 models remained the highest performing models and XGB is the most robust and effective in predictions. Feature importances reveal the role of input parameters in prediction made by developed ML models. Dosage, humidity, UV light intensity remain important experimental factors. This study will impact positively in providing efficient models to estimate photocatalytic degradation rate of air contaminants using TiO2.

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  • 22.
    Kerouche, Abdelfateh
    et al.
    Edinburgh Napier University, 10 Colinton Road Edinburgh EH10 5DT, United Kingdom.
    Najeh, Taoufik
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Jaen-Sola, Pablo
    Edinburgh Napier University, 10 Colinton Road Edinburgh EH10 5DT, United Kingdom.
    Fiber Bragg Grating Sensors for Monitoring Railway Switches and Crossings2022Inngår i: Proceedings 27th International Conference on Optical Fiber Sensors, Optica Publishing Group (formerly OSA) , 2022, artikkel-id W4.37Konferansepaper (Fagfellevurdert)
    Abstract [en]

    A solution to monitor excessive wear on railway switches and crossings based on Fiber Brag Grating (FBG) optical fiber sensors is discussed. Results of this pilot study based on simulation and lab tests have shown correlation for static load.

  • 23.
    Kerrouche, Abdelfateh
    et al.
    School of Engineering and the Built Environment, Edinburgh Napier University, 10 Colinton Road, Edinburgh EH10 5DT, UK.
    Najeh, Taoufik
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Jaen-Sola, Pablo
    School of Engineering and the Built Environment, Edinburgh Napier University, 10 Colinton Road, Edinburgh EH10 5DT, UK.
    Experimental Strain Measurement Approach Using Fiber Bragg Grating Sensors for Monitoring of Railway Switches and Crossings2021Inngår i: Sensors, E-ISSN 1424-8220, Vol. 21, nr 11, artikkel-id 3639Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Railway infrastructure plays a major role in providing the most cost-effective way to transport freight and passengers. The increase in train speed, traffic growth, heavier axles, and harsh environments make railway assets susceptible to degradation and failure. Railway switches and crossings (S&C) are a key element in any railway network, providing flexible traffic for trains to switch between tracks (through or turnout direction). S&C systems have complex structures, with many components, such as crossing parts, frogs, switchblades, and point machines. Many technologies (e.g., electrical, mechanical, and electronic devices) are used to operate and control S&C. These S&C systems are subject to failures and malfunctions that can cause delays, traffic disruptions, and even deadly accidents. Suitable field-based monitoring techniques to deal with fault detection in railway S&C systems are sought after. Wear is the major cause of S&C system failures. A novel measuring method to monitor excessive wear on the frog, as part of S&C, based on fiber Bragg grating (FBG) optical fiber sensors, is discussed in this paper. The developed solution is based on FBG sensors measuring the strain profile of the frog of S&C to determine wear size. A numerical model of a 3D prototype was developed through the finite element method, to define loading testing conditions, as well as for comparison with experimental tests. The sensors were examined under periodic and controlled loading tests. Results of this pilot study, based on simulation and laboratory tests, have shown a correlation for the static load. It was shown that the results of the experimental and the numerical studies were in good agreement.

  • 24.
    Khan, Adil
    et al.
    Department of Advanced Civil and Structural Engineering, University of Bradford, Bradford, West Yorkshire BD7 1DP, UK.
    Khan, Majid
    Department of Civil Engineering, COMSATS University, Islamabad, Abbottabad Campus 22060, Pakistan.
    Ali, Mohsin
    School of Civil Engineering, Southeast University Nanjing, China.
    Khan, Murad
    School of Civil Engineering, Tianjin University, Tianjin 300354, China.
    Khan, Asad Ullah
    Department of Civil Engineering, COMSATS University, Islamabad, Abbottabad Campus 22060, Pakistan.
    Shakeel, Muhammad
    Department of Civil Engineering, University of Engineering and Technology, Peshawar 25120, Pakistan.
    Fawad, Muhammad
    Silesian University of Technology Poland, Poland; Budapest University of Technology and Economics Hungary, Hungary.
    Najeh, Taoufik
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Gamil, Yaser
    Department of Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500 Bandar Sunway, Selangor, Malaysia.
    Predictive modeling for depth of wear of concrete modified with fly ash: A comparative analysis of genetic programming-based algorithms2024Inngår i: Case Studies in Construction Materials, E-ISSN 2214-5095, Vol. 20, artikkel-id e02744Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    There has been increasing growth in incorporating fly ash as a supplementary cementitious material in concrete mixtures due to its potential to enhance the durability and strength properties of concrete. However, there is a lack of research on predicting the depth of wear of fly ash-based concrete. The laboratory methods available for estimating the depth of wear often involve destructive and expensive tests. Therefore, to avoid costly and laborious tests, this study utilized two machine learning methods, including multi-expression programming (MEP) and gene expression programming (GEP), to predict the depth of wear of fly ash-modified concrete. A comprehensive dataset of 216 experimental records was compiled from published studies for model training and validation. This extensive dataset encompasses the depth of wear as the target variable, along with nine explanatory parameters, namely fly ash, cement content, fine and coarse aggregate, water content, plasticizer, age of concrete, air-entraining agent, and testing time. The models were trained with 70% of the data, and the remaining 30% of data was used for validating the models. The models were developed by a continuous trial-and-error process and iterative refinement of hyperparameters until optimal results were achieved. The efficacy of the models was assessed via multiple statistical indicators. Furthermore, the SHapley Additive exPlanation (SHAP) was utilized for the interpretability of the model prediction from both global and local perspectives. The GEP model exhibited excellent accuracy with a correlation coefficient (R) of 0.989 (training) and 0.992 (validation). Similarly, the MEP model provided prediction accuracy with R values of 0.965 and 0.968 for training and validation sets, respectively. In addition, the MEP and GEP models outperformed the traditional multi-linear regression model. The SHAP interpretation revealed that testing time and age have a higher contribution in determining the depth of wear. The findings of this study can assist practitioners and designers in avoiding costly and laborious tests for durability assessment and promoting sustainable use of fly ash in the construction sector.

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  • 25.
    Khan, Majid
    et al.
    Department of Civil Engineering, COMSATS University Islamabad, Abbottabad Campus, 22060, Abbottabad, Pakistan.
    Ali, Mujahid
    Department of Transport Systems, Traffic Engineering and Logistics, Faculty of Transport and Aviation Engineering, Silesian University of Technology, Krasińskiego 8 Street, 40-019, Katowice, Poland.
    Najeh, Taoufik
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Gamil, Yaser
    Department of Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500, Bandar Sunway, Selangor, Malaysia.
    Computational prediction of workability and mechanical properties of bentonite plastic concrete using multi-expression programming2024Inngår i: Scientific Reports, E-ISSN 2045-2322, Vol. 14, nr 1, artikkel-id 6105Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Bentonite plastic concrete (BPC) demonstrated promising potential for remedial cut-off wall construction to mitigate dam seepage, as it fulfills essential criteria for strength, stiffness, and permeability. High workability and consistency are essential attributes for BPC because it is poured into trenches using a tremie pipe, emphasizing the importance of accurately predicting the slump of BPC. In addition, prediction models offer valuable tools to estimate various strength parameters, enabling adjustments to BPC mixing designs to optimize project construction, leading to cost and time savings. Therefore, this study explores the multi-expression programming (MEP) technique to predict the key characteristics of BPC, such as slump, compressive strength (fc), and elastic modulus (Ec). In the present study, 158, 169, and 111 data points were collected from the experimental studies for the slump, fc, and Ec, respectively. The dataset was divided into three sets: 70% for training, 15% for testing, and another 15% for model validation. The MEP models exhibited excellent accuracy with a correlation coefficient (R) of 0.9999 for slump, 0.9831 for fc, and 0.9300 for Ec. Furthermore, the comparative analysis between MEP models and conventional linear and non-linear regression models revealed remarkable precision in the predictions of the proposed MEP models, surpassing the accuracy of traditional regression methods. SHapley Additive exPlanation analysis indicated that water, cement, and bentonite exert significant influence on slump, with water having the greatest impact on compressive strength, while curing time and cement exhibit a higher influence on elastic modulus. In summary, the application of machine learning algorithms offers the capability to deliver prompt and precise early estimates of BPC properties, thus optimizing the efficiency of construction and design processes.

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  • 26.
    Khan, Majid
    et al.
    COMSATS University Islamabad, Abbottabad Campus, 22060, Pakistan.
    Khan, Adil
    Department of Civil and Structural Engineering, University of Bradford, Bradford, West Yorkshire, BD7 1DP, UK.
    Khan, Asad Ullah
    Department of Civil Engineering, University of Engineering and Technology, Peshawar, 25120, Pakistan.
    Shakeel, Muhammad
    Department of Civil Engineering, University of Engineering and Technology, Peshawar, 25120, Pakistan.
    Khan, Khalid
    Department of Civil Engineering, University of Engineering and Technology, Peshawar, 25120, Pakistan.
    Alabduljabbar, Hisham
    Department of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia.
    Najeh, Taoufik
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Gamil, Yaser
    Department of Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500 Bandar Sunway, Selangor, Malaysia.
    Intelligent prediction modeling for flexural capacity of FRP-strengthened reinforced concrete beams using machine learning algorithms2023Inngår i: Heliyon, E-ISSN 2405-8440, Vol. 10, artikkel-id e23375Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Fiber-reinforced polymers (FRP) are widely utilized to improve the efficiency and durability of concrete structures, either through external bonding or internal reinforcement. However, the response of FRP-strengthened reinforced concrete (RC) members, both in field applications and experimental settings, often deviates from the estimation based on existing code provisions. This discrepancy can be attributed to the limitations of code provisions in fully capturing the nature of FRP-strengthened RC members. Accordingly, machine learning methods, including gene expression programming (GEP) and multi-expression programming (MEP), were utilized in this study to predict the flexural capacity of the FRP-strengthened RC beam. To develop data-driven estimation models, an extensive collection of experimental data on FRP-strengthened RC beams was compiled from the experimental studies. For the assessment of the accuracy of developed models, various statistical indicators were utilized. The machine learning (ML) based models were compared with empirical and conventional linear regression models to substantiate their superiority, providing evidence of enhanced performance. The GEP model demonstrated outstanding predictive performance with a correlation coefficient (R) of 0.98 for both the training and validation phases, accompanied by minimal mean absolute errors (MAE) of 4.08 and 5.39, respectively. In contrast, the MEP model achieved a slightly lower accuracy, with an R of 0.96 in both the training and validation phases. Moreover, the ML-based models exhibited notably superior performances compared to the empirical models. Hence, the ML-based models presented in this study demonstrated promising prospects for practical implementation in engineering applications. Moreover, the SHapley Additive exPlanation (SHAP) method was used to interpret the feature's importance and influence on the flexural capacity. It was observed that beam width, section effective depth, and the tensile longitudinal bars reinforcement ratio significantly contribute to the prediction of the flexural capacity of the FRP-strengthened reinforced concrete beam.

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  • 27.
    Khan, Majid
    et al.
    Civil Engineering Department, COMSATS University Islamabad, Abbottabad Campus, 22060, Pakistan.
    Nassar, Roz-Ud-Din
    Department of Civil and Infrastructure Engineering at American University of Ras Al Khaimah, United Arab Emirates.
    Anwar, Waqar
    Department of Civil and Environmental Engineering, University of New Hampshire, Durham, NH 03824, USA.
    Rasheed, Mazhar
    College of Engineering and Technology, University of Sargodha, 40100, Pakistan.
    Najeh, Taoufik
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Gamil, Yaser
    Department of Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500 Bandar Sunway, Selangor, Malaysia.
    Farooq, Furqan
    NUST Institute of Civil Engineering (NICE), School of Civil and Environmental Engineering (SCEE), National University of Sciences and Technology (NUST), Sector H-12, Islamabad, 44000, Pakistan; Military Engineer Service (MES), Ministry of Defence (MoD), Rawalpindi, 43600, Pakistan.
    Forecasting the strength of graphene nanoparticles-reinforced cementitious composites using ensemble learning algorithms2024Inngår i: Results in Engineering (RINENG), ISSN 2590-1230, Vol. 21, artikkel-id 101837Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Integrating nanomaterials into concrete is a promising solution to improve concrete strength and durability. However, the intricacies of such nanoscale cementitious composites are highly complex. Traditional regression models encounter limitations in capturing these intricate compositions to provide accurate and reliable estimations. This study focuses on developing robust prediction models for the compressive strength (CS) of graphene nanoparticle-reinforced cementitious composites (GrNCC) through machine learning (ML) algorithms. Three ML models, bagging regressor (BR), decision tree (DT), and AdaBoost regressor (AR), were employed to predict CS based on a comprehensive dataset of 172 experimental values. Seven input parameters, including graphite nanoparticle (GrN) diameter, water-to-cement ratio (wc), GrN content (GC), ultrasonication (US), sand content (SC), curing age (CA), and GrN thickness (GT), were considered. The models were trained with 70 % of the data, and the remaining 30 % of the data was used for testing the models. Statistical metrics such as mean absolute error (MAE), root mean square error (RMSE) and correlation coefficient (R) were employed to assess the predictive accuracy of the models. The DT and AR models demonstrated exceptional accuracy, yielding high correlation coefficients of 0.983 and 0.979 for training, and 0.873 and 0.822 for testing, respectively. Shapley Additive exPlanation (SHAP) analysis highlighted the influential role of curing age and GrN thickness (GT), positively impacting CS, while an increased water-to-cement ratio (w/c) negatively affected CS. This study showcases the efficacy of ML techniques in accurately predicting CS of graphene nanoparticle-modified concrete, offering a swift and cost-effective approach for assessing nanomaterial impact on concrete strength and reducing reliance on time-consuming and expensive experiments.

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  • 28.
    Mahmood, Saqib
    et al.
    Department of Civil Engineering, University of Engineering and Technology Peshawar (Bannu Campus), Bannu, Pakistan.
    Khan, Afed Ullah
    Department of Civil Engineering, University of Engineering and Technology Peshawar (Bannu Campus), Bannu, Pakistan; National Institute of Urban Infrastructure Planning, University of Engineering and Technology, Peshawar, Pakistan.
    Babur, Muhammad
    Department of Civil Engineering, Faculty of Engineering, University of Central Punjab, Lahore, Pakistan.
    Ghanim, Abdulnoor A. J.
    Civil Engineering Department, College of Engineering, Najran University, Najran, Saudi Arabia.
    Al-Areeq, Ahmed M.
    Interdisciplinary Research Center for Membranes and Water Security, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia.
    Khan, Daud
    Department of Transport Systems, Traffic Engineering and Logistics, Faculty of Transport and Aviation Engineering, Silesian University of Technology, Katowice, Poland.
    Najeh, Taoufik
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Gamil, Yaser
    Department of Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, Subang Jaya, Malaysia.
    Divergent path: isolating land use and climate change impact on river runoff2024Inngår i: Frontiers in Environmental Science, E-ISSN 2296-665X, Vol. 12, artikkel-id 1338512Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Water resource management requires a thorough examination of how land use and climate change affect streamflow; however, the potential impacts of land-use changes are frequently ignored. Therefore, the principal goal of this study is to isolate the effects of anticipated climate and land-use changes on streamflow at the Indus River, Besham, Pakistan, using the Soil and Water Assessment Tool (SWAT). The multimodal ensemble (MME) of 11 general circulation models (GCMs) under two shared socioeconomic pathways (SSPs) 245 and 585 was computed using the Taylor skill score (TSS) and rating metric (RM). Future land use was predicted using the cellular automata artificial neural network (CA-ANN). The impacts of climate change and land-use change were assessed on streamflow under various SSPs and land-use scenarios. To calibrate and validate the SWAT model, the historical record (1991-2013) was divided into the following two parts: calibration (1991-2006) and validation (2007-2013). The SWAT model performed well in simulating streamflow with NSE, R2, and RSR values during the calibration and validation phases, which are 0.77, 0.79, and 0.48 and 0.76, 0.78, and 0.49, respectively. The results show that climate change (97.47%) has a greater effect on river runoff than land-use change (2.53%). Moreover, the impact of SSP585 (5.84%-19.42%) is higher than that of SSP245 (1.58%-4%). The computed impacts of climate and land-use changes are recommended to be incorporated into water policies to bring sustainability to the water environment.

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  • 29.
    Najeh, Taoufik
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Gamil, Yaser
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Byggkonstruktion och brand. Department of Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500 Bandar Sunway, Selangor, Malaysia.
    Nilimaa, Jonny
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Byggkonstruktion och brand.
    Forecasting maximum formwork pressure for self-compacting concrete using ARX-Laguerre machine learning model2024Inngår i: Developments in the Built Environment, ISSN 2666-1659, Vol. 18, artikkel-id 100409Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Forecasting the maximum pressure exerted by cast-in-place self-compacting concrete (SCC) is a major concern for formwork designers, researchers, and site engineers to accurately design the bearing capacity of the formwork and control the casting rate for safe and fast construction. This article aims to utilize the ARX-Laguerre model, which is a data-driven model to forecast the maximum form pressure. A laboratory instrumented setup was used to cast a 2-m column using SCC made with two different types of cement. A pressure system consisting of four sensors was used to document the pressure during casting. The data were sent to the cloud at every 1-min interval for real-time monitoring. The data were used to develop the model. The results demonstrated that forecasting with the ARX-Laguerre model is highly accurate. The model can forecast the maximum pressure exerted by SCC with less complexity. The model performance was also found to be consistent with insignificant differences between actual experimental results and predicted results. With a recursive and straightforward representation, the resulting model, known as the ARX-Laguerre model, ensures the parameter number reduction. Providing fast prediction of the maximum pressure. The model enables formwork designers to forecast the form pressure to design safe formwork and also helps to control the casting rate when SCC is used.

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  • 30.
    Najeh, Taoufik
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Lundberg, Jan
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Degradation state prediction of rolling bearings using ARX-Laguerre model and genetic algorithms2021Inngår i: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015, Vol. 112, nr 3-4, s. 1077-1088Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This study is motivated by the need for a new advanced vibration-based bearing monitoring approach. The ARX-Laguerre model (autoregressive with exogenous) and genetic algorithms (GAs) use collected vibration data to estimate a bearing’s remaining useful life (RUL). The concept is based on the actual running conditions of the bearing combined with a new linear ARX-Laguerre representation. The proposed model exploits the vibration and force measurements to reconstruct the Laguerre filter outputs; the dimensionality reduction of the model is subject to an optimal choice of Laguerre poles which is performed using GAs. The paper explains the test rig, data collection, approach, and results. So far and compared to classic methods, the proposed model is effective in tracking the evolution of the bearing’s health state and accurately estimates the bearing’s RUL. As long as the collected data are relevant to the real health state of the bearing, it is possible to estimate the bearing’s lifetime under different operating conditions.

  • 31.
    Najeh, Taoufik
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Lundberg, Jan
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Kerrouche, Abdelfateh
    School of Engineering and the Built Environment, Edinburgh Napier University, 10 Colinton Road, Edinburgh EH10 5DT, UK.
    Deep-Learning and Vibration-Based System for Wear Size Estimation of Railway Switches and Crossings2021Inngår i: Sensors, E-ISSN 1424-8220, Vol. 21, nr 15, artikkel-id 5217Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The switch and crossing (S&C) is one of the most important parts of the railway infrastructure network due to its significant influence on traffic delays and maintenance costs. Two central questions were investigated in this paper: (I) the first question is related to the feasibility of exploring the vibration data for wear size estimation of railway S&C and (II) the second one is how to take advantage of the Artificial Intelligence (AI)-based framework to design an effective early-warning system at early stage of S&C wear development. The aim of the study was to predict the amount of wear in the entire S&C, using medium-range accelerometer sensors. Vibration data were collected, processed, and used for developing accurate data-driven models. Within this study, AI-based methods and signal-processing techniques were applied and tested in a full-scale S&C test rig at Lulea University of Technology to investigate the effectiveness of the proposed method. A real-scale railway wagon bogie was used to study different relevant types of wear on the switchblades, support rail, middle rail, and crossing part. All the sensors were housed inside the point machine as an optimal location for protection of the data acquisition system from harsh weather conditions such as ice and snow and from the ballast. The vibration data resulting from the measurements were used to feed two different deep-learning architectures, to make it possible to achieve an acceptable correlation between the measured vibration data and the actual amount of wear. The first model is based on the ResNet architecture where the input data are converted to spectrograms. The second model was based on a long short-term memory (LSTM) architecture. The proposed model was tested in terms of its accuracy in wear severity classification. The results show that this machine learning method accurately estimates the amount of wear in different locations in the S&C.

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  • 32.
    Rajpurohit, Sohan Singh
    et al.
    Department of Mining Engineering, Indian Institute of Technology (Indian School of Mines), 826004, Dhanbad, India.
    Fissha, Yewuhalashet
    Department of Geosciences, Geotechnology, and Materials Engineering for Resources, Graduate School of International Resource Sciences, Akita University, Akita, Japan; Department of Mining Engineering, Aksum University, 7080, Aksum, Tigray, Ethiopia.
    Sinha, Rabindra Kumar
    Department of Mining Engineering, Indian Institute of Technology (Indian School of Mines), 826004, Dhanbad, India.
    Ali, Mujahid
    Department of Transport Systems, Traffic Engineering and Logistics, Faculty of Transport and Aviation Engineering, Silesian University of Technology, Krasińskiego 8 Street, 40-019, Katowice, Poland.
    Ikeda, Hajime
    Department of Geosciences, Geotechnology, and Materials Engineering for Resources, Graduate School of International Resource Sciences, Akita University, Akita, Japan.
    Ghribi, Wade
    Department of Computer Engineering, College of Computer Science, King Khalid University, Abha, Saudi Arabia.
    Najeh, Taoufik
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Gamil, Yaser
    Department of Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500, Bandar Sunway, Selangor, Malaysia.
    Kawamura, Youhei
    Faculty of Engineering, Hokkaido University, Kita 8, Nishi 5, Kita-ku, 0608628, Sapporo, Japan.
    Effect of rock properties on wear and cutting performance of multi blade circular saw with iron based multi-layer diamond segments2024Inngår i: Scientific Reports, E-ISSN 2045-2322, Vol. 14, nr 1, artikkel-id 4590Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This study is an attempt for comprehensive, combining experimental data with advanced analytical techniques and machine learning for a thorough understanding of the factors influencing the wear and cutting performance of multi-blade diamond disc cutters on granite blocks. A series of sawing experiments were performed to evaluate the wear and cutting performance of multi blade diamond disc cutters with varying diameters in the processing of large-sized granite blocks. The multi-layer diamond segments comprising the Iron (Fe) based metal matrix were brazed on the sawing blades. The segment’s wear was studied through micrographs and data obtained from the Field Emission Scanning Electron Microscopy (FESEM) and Energy Dispersive X-ray (EDS). Granite rock samples of nine varieties were tested in the laboratory to determine the quantitative rock parameters. The contribution of individual rock parameters and their combined effects on wear and cutting performance of multi blade saw were correlated using statistical machine learning methods. Moreover, predictive models were developed to estimate the wear and cutting rate based on the most significant rock properties. The point load strength index, uniaxial compressive strength, and deformability, Cerchar abrasivity index, and Cerchar hardness index were found to be the significant variables affecting the sawing performance.

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  • 33.
    Reetz, Susanne
    et al.
    Institute of Transportation Systems, German Aerospace Center (DLR), Braunschweig, 38108, Germany.
    Najeh, Taoufik
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Lundberg, Jan
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Groos, Jörn
    Institute of Transportation Systems, German Aerospace Center (DLR), Braunschweig, 38108, Germany.
    Analysis of Local Track Discontinuities and Defects in Railway Switches Based on Track-Side Accelerations2024Inngår i: Sensors, E-ISSN 1424-8220, Vol. 24, nr 2, artikkel-id 477Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Switches are an essential, safety-critical part of the railway infrastructure. Compared to open tracks, their complex geometry leads to increased dynamic loading on the track superstructure from passing trains, resulting in high maintenance costs. To increase efficiency, condition monitoring methods specific to railway switches are required. A common approach to track superstructure monitoring is to measure the acceleration caused by vehicle track interaction. Local interruptions in the wheel–rail contact, caused for example by local defects or track discontinuities, appear in the data as transient impact events. In this paper, such transient events are investigated in an experimental setup of a railway switch with track-side acceleration sensors, using frequency and waveform analysis. The aim is to understand if and how the origins of these impact events can be distinguished in the data of this experiment, and what the implications for condition monitoring of local track discontinuities and defects with wayside acceleration sensors are in practice. For the same experimental configuration, individual impact events are shown to be reproducible in waveform and frequency content. Nevertheless, with this track-side sensor setup, the different types of track discontinuities and defects (squats, joints, crossing) could not be clearly distinguished using characteristic frequencies or waveforms. Other factors, such as the location of impact event origin relative to the sensor, are shown to have a much stronger influence. The experimental data suggest that filtering the data to narrow frequency bands around certain natural track frequencies could be beneficial for impact event detection in practice, but differentiating between individual impact event origins requires broadband signals. A multi-sensor setup with time-synchronized acceleration sensors distributed over the switch is recommended.

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  • 34.
    Shams, Muhammad Alamgeer
    et al.
    Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS, Bandar SeriIskandar, Malaysia.
    Bheel, Naraindas
    Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS, Bandar SeriIskandar, Malaysia.
    Ali, Mohsin
    Graduate School of Urban Innovation, Department of Civil Engineering, Yokohama National University, Yokohama, Japan.
    Ahmad, Mahmood
    Institute of Energy Infrastructure, Universiti Tenaga Nasional, Kajang, Malaysia; Department of Civil Engineering, University of Engineering and Technology Peshawar (Bannu Campus), Bannu, Pakistan.
    Najeh, Taoufik
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Gamil, Yaser
    Department of Civil Engineering, School of Engineering, Monash University Malaysia, Subang Jaya, Malaysia.
    Almujibah, Hamad R.
    Department of Civil Engineering, College of Engineering, Taif University, Taif, Saudi Arabia.
    Benjeddou, Omrane
    Department of Civil Engineering, College of Engineering, Prince Sattam Bin Abdulaziz University, Alkharj, Saudi Arabia.
    Fracture analysis of steel fibre-reinforced concrete using Finite element method modeling2024Inngår i: Frontiers in Materials, E-ISSN 2296-8016, Vol. 11, artikkel-id 1355351Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Concrete has a great capacity to withstand compressive strength, but it is rather weak at resisting tensile stresses, which ultimately result in the formation of cracks in concrete buildings. The development of cracks has a significant impact on the durability of concrete because they serve as direct pathways for corrosive substances that harm the concrete’s constituents. Consequently, the reinforced concrete may experience degradation, cracking, weakening, or progressive disintegration. To mitigate such problems, it is advisable to include discrete fibres uniformly throughout the concrete mixture. The fibers function by spanning the voids created by fractures, therefore decelerating the mechanism of fracture initiation and advancement. It is not practical to assess the beginning and spread of cracks when there are uncertainties in the components and geometrical factors through probabilistic methods. This research examines the behaviour of variation of steel fibers in Fiber Reinforced Concrete (FRC) via Finite Element Method (FEM) modeling. In this study also the fracture parameters such as fracture energy, and fracture toughness have been computed through FEM analysis. The FEM constitutive model developed was also validated with the experimental result. The compressive strength of the developed constitutive model was 28.50 MPa which is very close to the 28-day compressive strength obtained through the experiment, i.e., 28.79 MPa. Load carrying capacity obtained through FEM was 7.9 kN, 18 kN, and 24 kN for three FEM models developed for three varying percentages of steel fiber 0.25%, 0.5%, and 0.75% respectively. The study developed a FEM model which can be used for calculating the fracture parameters of Steel Fibre-Reinforced Concrete (SFRC).

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  • 35.
    Wang, Dong
    et al.
    Chongqing Chemical Industry Vocational College, 401220, China.
    Amin, Muhammad Nasir
    Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi Arabia.
    Khan, Kaffayatullah
    Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi Arabia.
    Nazar, Sohaib
    Department of Civil Engineering, Comsats University Islamabad-Abbottabad Campus, Pakistan.
    Gamil, Yaser
    Department of Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500 Bandar Sunway, Selangor, Malaysia.
    Najeh, Taoufik
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Comparing the efficacy of GEP and MEP algorithms in predicting concrete strength incorporating waste eggshell and waste glass powder2024Inngår i: Developments in the Built Environment, E-ISSN 2666-1659, Vol. 17, artikkel-id 100361Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The present study used the techniques of gene expression programming (GEP) and multi-expression programming (MEP) to assess the compressive strength (CS) and flexural strength (FS) and develop predictive models of sustainable mortar modified with waste eggshell powder (WEP) and waste glass powder (WGP) as a replacement of cement. In order to get more insights into the impact and relation of raw components on the CS and FS of a developed sustainable mortar, a comprehensive study using the SHapley Additive exPlanations (SHAP) methodology was performed. When comparing the efficiency of both employed models, it was seen that the MEP model exhibited superior performance with an R2 value of 0.871 and 0.894 for CS and FS, as compared to the GEP model, which had an R2 value of 0.842 and 0.845 for CS and FS respectively.

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  • 36.
    Wang, Jing
    et al.
    School of Civil Engineering, Chongqing Industry Polytechnic College, No.1000 Taoyuan Avenue, Airport, Yubei District, Chongqing 401120, China.
    Qu, Qian
    School of Civil Engineering, Chongqing Industry Polytechnic College, No.1000 Taoyuan Avenue, Airport, Yubei District, Chongqing 401120, China.
    Khan, Suleman Ayub
    Department of Civil Engineering, COMSATS University Islamabad, Abbottabad 22060, Pakistan.
    Alotaibi, Badr Saad
    Architectural Engineering Department, College of Engineering, Najran University, Najran, Saudi Arabia.
    Althoey, Fadi
    Department of Civil Engineering, College of Engineering, Najran University, Najran, Saudi Arabia.
    Gamil, Yaser
    Department of Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500 Bandar Sunway, Selangor, Malaysia.
    Najeh, Taoufik
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Experimenting the influence of corncob ash on the mechanical strength of slag-based geopolymer concrete2024Inngår i: Reviews on Advanced Materials Science, ISSN 1606-5131, E-ISSN 1605-8127, Vol. 63, nr 1, artikkel-id 20230187Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The construction sector has been under growing public attention recently as one of the leading causes of climate change and its detrimental effects on local communities. In this regard, geopolymer concrete (GPC) has been proposed as a replacement for conventional concrete. Predicting the concrete’s strength before pouring is, therefore, quite useful. The mechanical strength of slag and corncob ash (SCA–GPC), a GPC made from slag and corncob ash, was predicted utilizing multi-expression programming (MEP). Modeling parameters’ relative importance was determined using sensitivity analysis. When estimating the compressive, flexural, and split tensile strengths of SCA–GPC with MEP, 0.95, 0.93, and 0.92 R2-values were noted between the target and predicted results. The developed models were validated using statistical tests for error and efficiency. The sensitivity analysis revealed that within the mix proportions, the slag quantity (65%), curing age (25%), and fine aggregate (3.30%) quantity significantly influenced the mechanical strength of SCA–GPC. The MEP models result in distinct empirical equations for the strength characteristics of SCA–GPC, unlike Python-based models, which might aid industry and researchers worldwide in determining optimal mix design proportions, thus eliminating unneeded test repetitions in the laboratory.

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  • 37.
    Zafar, Waqar Ali
    et al.
    DNE, Pakistan Institute of Engineering and Applied Sciences, Islamabad, Pakistan; Centre for Earthquake Studies, National Centre for Physics, Islamabad, Pakistan.
    Javed, Farhan
    Centre for Earthquake Studies, National Centre for Physics, Islamabad, Pakistan.
    Ahmed, Rizwan
    DNE, Pakistan Institute of Engineering and Applied Sciences, Islamabad, Pakistan.
    Shah, Muhammad Ali
    Centre for Earthquake Studies, National Centre for Physics, Islamabad, Pakistan.
    Ahmad, Mahmood
    Department of Civil Engineering, University of Engineering and Technology Peshawar, Bannu Campus, Bannu, Pakistan; Institute of Energy Infrastructure, Universiti Tenaga Nasional, Kajang, Malaysia.
    Khan, Muhammad Younis
    National Centre of Excellence in Geology, University of Peshawar, Peshawar, Pakistan; Department of Earth Sciences, College of Science, Sultan Qaboos University, Muscat, Oman.
    Abdullah, Gamil M. S.
    Department of Civil Engineering, College of Engineering, Najran University, Najran, Saudi Arabia; Science and Engineering Research Center, Najran University, Najran, Saudi Arabia.
    Khan, Daud
    Department of Transport Systems, Traffic Engineering and Logistics, Faculty of Transport and Aviation Engineering, Silesian University of Technology, Katowice, Poland.
    Najeh, Taoufik
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Gamil, Yaser
    Department of Civil Engineering, School of Engineering, Monash University Malaysia, Subang Jaya, Malaysia.
    Time series subsidence evaluation using NSBAS InSAR: a case study of twin megacities (Rawalpindi and Islamabad) in Pakistan2024Inngår i: Frontiers in Earth Science, E-ISSN 2296-6463, Vol. 12, artikkel-id 1336530Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Ground deformation associated with natural and anthropogenic activities can be damaging for infrastructure and can cause enormous economic loss, particularly in developing countries which lack measuring instruments. Remote sensing techniques like interferometric synthetic aperture radar (InSAR) can thus play an important role in investigating deformation and mitigating geohazards. Rawalpindi and Islamabad are twin cities in Pakistan with a population of approximately 5.4 million, along with important government and private entities of national and international interest. In this study, we evaluate rapid paced subsidence in this area using a modified small baseline subset technique with Sentinel-1A imagery acquired between 2015 and 2022. Our results show that approximately 50 mm/year subsidence occurs in the older city of Rawalpindi, the most populated zone. We observed that subsidence in the area is controlled by the buried splays of the Main Boundary Thrust, one of the most destructive active faults in the recent past. We suggest that such rapid subsidence is most probably due to aggressive subsurface water extraction. It has been found that, despite provision of alternate water supplies by the district government, a very alarming number of tube wells are being operated in the area to extract ground water. Over 2017–2021, field data showed that near-surface aquifers up to 50–60 m deep are exhausted, and most of the tube wells are currently extracting water from depths of approximately 150–160 m. The dropping water level is proportional to the increasing number of tube wells. Lying downstream of tributaries originating from the Margalla and Murree hills, this area has a good monsoon season, and its topography supports recharge of the aquifers. However, rapid subsidence indicates a deficit between water extraction and recharge, partly due to the limitations inherent in shale and the low porosity near the surface lithology exposed in the area. Other factors amplifying the impacts are fast urbanization, uncontrolled population growth, and non-cultivation of precipitation in the area.

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  • 38.
    Zuo, Yang
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Lundberg, Jan
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Najeh, Taoufik
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Rantatalo, Matti
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Odelius, Johan
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Squat Detection of Railway Switches and Crossings Using Point Machine Vibration Measurements2023Inngår i: Sensors, E-ISSN 1424-8220, Vol. 23, nr 7, artikkel-id 3666Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Railway switches and crossings (S&C) are among the most important high-value components in a railway network and a failure of such an asset could result in severe network disturbance. Therefore, potential defects need to be detected at an early stage to prevent traffic-disturbing downtime or even severe accidents. A squat is a common defect of S&Cs that has to be monitored and repaired to reduce such risks. In this study, a testbed including a full-scale S&C and a bogie wagon was developed. Vibrations were measured for different squat sizes by an accelerometer mounted at the point machine. A method of processing the vibration data and the speed data is proposed to investigate the possibility of detecting and quantifying the severity of a squat. One key technology used is wavelet denoising. The study shows that it is possible to monitor the development of the squat size on the rail up to around 13 m from the point machine. The relationships between the normalised peak-to-peak amplitude of the vibration signal and the squat depth were also estimated.

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