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Reetz, S., Najeh, T., Lundberg, J. & Groos, J. (2024). Analysis of Local Track Discontinuities and Defects in Railway Switches Based on Track-Side Accelerations. Sensors, 24(2), Article ID 477.
Open this publication in new window or tab >>Analysis of Local Track Discontinuities and Defects in Railway Switches Based on Track-Side Accelerations
2024 (English)In: Sensors, E-ISSN 1424-8220, Vol. 24, no 2, article id 477Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
MDPI, 2024
Keywords
acceleration, crossing, fault diagnosis, joint, railway, squat, switch, track superstructure, track-side, way-side
National Category
Vehicle Engineering
Research subject
Operation and Maintenance Engineering
Identifiers
urn:nbn:se:ltu:diva-104172 (URN)10.3390/s24020477 (DOI)001151217700001 ()38257569 (PubMedID)2-s2.0-85183276058 (Scopus ID)
Funder
EU, Horizon Europe, 101101966
Note

Validerad;2024;Nivå 2;2024-04-08 (hanlid);

Full text license: CC BY 4.0

Available from: 2024-02-05 Created: 2024-02-05 Last updated: 2024-04-08Bibliographically approved
Alyami, M., Khan, M., Javed, M. F., Ali, M., Alabduljabbar, H., Najeh, T. & Gamil, Y. (2024). Application of metaheuristic optimization algorithms in predicting the compressive strength of 3D-printed fiber-reinforced concrete. Developments in the Built Environment, 17, Article ID 100307.
Open this publication in new window or tab >>Application of metaheuristic optimization algorithms in predicting the compressive strength of 3D-printed fiber-reinforced concrete
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2024 (English)In: Developments in the Built Environment, E-ISSN 2666-1659, Vol. 17, article id 100307Article in journal (Refereed) Published
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.

Keywords
Additive manufacturing, 3D-printed concrete, Compressive strength, Fiber-reinforced concrete, Metaheuristic algorithms, Random forest
National Category
Other Civil Engineering
Research subject
Operation and Maintenance Engineering
Identifiers
urn:nbn:se:ltu:diva-103741 (URN)10.1016/j.dibe.2023.100307 (DOI)001145128600001 ()2-s2.0-85180983178 (Scopus ID)
Note

Validerad;2024;Nivå 2;2024-01-25 (signyg);

Funder: Najran University (NU/DRP/SERC/12/10);

Full text license: CC BY-NC-ND

Available from: 2024-01-16 Created: 2024-01-16 Last updated: 2024-03-07Bibliographically approved
Wang, D., Amin, M. N., Khan, K., Nazar, S., Gamil, Y. & Najeh, T. (2024). Comparing the efficacy of GEP and MEP algorithms in predicting concrete strength incorporating waste eggshell and waste glass powder. Developments in the Built Environment, 17, Article ID 100361.
Open this publication in new window or tab >>Comparing the efficacy of GEP and MEP algorithms in predicting concrete strength incorporating waste eggshell and waste glass powder
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2024 (English)In: Developments in the Built Environment, E-ISSN 2666-1659, Vol. 17, article id 100361Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Elsevier Ltd, 2024
Keywords
Cement mortar, Eggshell powder, Glass powder, Machine learning, Prediction models
National Category
Materials Engineering Other Materials Engineering
Research subject
Operation and Maintenance Engineering
Identifiers
urn:nbn:se:ltu:diva-104397 (URN)10.1016/j.dibe.2024.100361 (DOI)2-s2.0-85185002238 (Scopus ID)
Note

Validerad;2024;Nivå 2;2024-04-09 (joosat);

Funder: King Faisal University (GRANT5577);

Full text license: CC BY

Available from: 2024-03-12 Created: 2024-03-12 Last updated: 2024-04-09Bibliographically approved
Ajeel, R. K., Fayyadh, S. N., Ibrahim, A., Sultan, S. M. & Najeh, T. (2024). Comprehensive analysis of heat transfer and pressure drop in square multiple impingement jets employing innovative hybrid nanofluids. Results in Engineering (RINENG), 21, Article ID 101858.
Open this publication in new window or tab >>Comprehensive analysis of heat transfer and pressure drop in square multiple impingement jets employing innovative hybrid nanofluids
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2024 (English)In: Results in Engineering (RINENG), ISSN 2590-1230, Vol. 21, article id 101858Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Elsevier B.V., 2024
Keywords
Binary hybrid nanofluid, Iso-surface, Multiple impingement jet, Pressure drop, Square profile
National Category
Energy Engineering
Research subject
Operation and Maintenance Engineering
Identifiers
urn:nbn:se:ltu:diva-104629 (URN)10.1016/j.rineng.2024.101858 (DOI)2-s2.0-85186489306 (Scopus ID)
Note

Validerad;2024;Nivå 1;2024-04-09 (joosat);

Full text license: CC BY-NC-ND

Available from: 2024-03-18 Created: 2024-03-18 Last updated: 2024-04-09Bibliographically approved
Khan, M., Ali, M., Najeh, T. & Gamil, Y. (2024). Computational prediction of workability and mechanical properties of bentonite plastic concrete using multi-expression programming. Scientific Reports, 14(1), Article ID 6105.
Open this publication in new window or tab >>Computational prediction of workability and mechanical properties of bentonite plastic concrete using multi-expression programming
2024 (English)In: Scientific Reports, E-ISSN 2045-2322, Vol. 14, no 1, article id 6105Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Nature Research, 2024
Keywords
Bentonite plastic concrete, Machine learning, Mechanical properties, MEP, Slump
National Category
Materials Engineering
Research subject
Operation and Maintenance Engineering
Identifiers
urn:nbn:se:ltu:diva-104889 (URN)10.1038/s41598-024-56088-0 (DOI)38480772 (PubMedID)2-s2.0-85187738302 (Scopus ID)
Note

Validerad;2024;Nivå 2;2024-04-05 (marisr);

Full text license: CC BY

Available from: 2024-03-26 Created: 2024-03-26 Last updated: 2024-04-05Bibliographically approved
Abuhussain, M. A., Ahmad, A., Amin, M. N., Althoey, F., Gamil, Y. & Najeh, T. (2024). Data-driven approaches for strength prediction of alkali-activated composites. Case Studies in Construction Materials, 20, Article ID e02920.
Open this publication in new window or tab >>Data-driven approaches for strength prediction of alkali-activated composites
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2024 (English)In: Case Studies in Construction Materials, E-ISSN 2214-5095, Vol. 20, article id e02920Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Elsevier, 2024
Keywords
Alkali-activated composites, Input parameters, Compressive strength, Prediction, Machine learning
National Category
Composite Science and Engineering Other Materials Engineering
Research subject
Operation and Maintenance Engineering
Identifiers
urn:nbn:se:ltu:diva-104311 (URN)10.1016/j.cscm.2024.e02920 (DOI)2-s2.0-85184141025 (Scopus ID)
Note

Validerad;2024;Nivå 2;2024-04-09 (joosat);

Funder: Najran University (NU/NRP/SERC/12/7); King Faisal University (GRANT4500);

Full text license: CC BY 4.0;

Available from: 2024-02-20 Created: 2024-02-20 Last updated: 2024-04-09Bibliographically approved
Azad, M. A. A., Najeh, T., Raina, A. K., Singh, N., Ansari, A., Ali, M., . . . Singh, S. K. (2024). Development of correlations between various engineering rockmass classification systems using railway tunnel data in Garhwal Himalaya, India. Scientific Reports, 14, Article ID 10716.
Open this publication in new window or tab >>Development of correlations between various engineering rockmass classification systems using railway tunnel data in Garhwal Himalaya, India
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2024 (English)In: Scientific Reports, E-ISSN 2045-2322, Vol. 14, article id 10716Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Springer Nature, 2024
Keywords
Rockmass classifcation, Metamorphic rock, Tunnelling, Garhwal Himalaya, Engineering geology
National Category
Other Civil Engineering
Research subject
Operation and Maintenance Engineering
Identifiers
urn:nbn:se:ltu:diva-105618 (URN)10.1038/s41598-024-60289-y (DOI)38729957 (PubMedID)2-s2.0-85192895686 (Scopus ID)
Note

Full text license: CC BY 4.0; 

Funder: Lulea University of Technology; University of Delhi;

Available from: 2024-05-27 Created: 2024-05-27 Last updated: 2024-05-27
Mahmood, S., Khan, A. U., Babur, M., Ghanim, A. A. J., Al-Areeq, A. M., Khan, D., . . . Gamil, Y. (2024). Divergent path: isolating land use and climate change impact on river runoff. Frontiers in Environmental Science, 12, Article ID 1338512.
Open this publication in new window or tab >>Divergent path: isolating land use and climate change impact on river runoff
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2024 (English)In: Frontiers in Environmental Science, E-ISSN 2296-665X, Vol. 12, article id 1338512Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Frontiers Media Sa, 2024
Keywords
streamflow, climate change, land-use change, CMIP6, prediction
National Category
Environmental Sciences related to Agriculture and Land-use
Research subject
Operation and Maintenance Engineering
Identifiers
urn:nbn:se:ltu:diva-104394 (URN)10.3389/fenvs.2024.1338512 (DOI)001161535500001 ()2-s2.0-85185106979 (Scopus ID)
Note

Validerad;2024;Nivå 2;2024-04-04 (joosat);

Funder: Institutional Funding Committee at Najran University;

Full text license: CC BY

Available from: 2024-03-12 Created: 2024-03-12 Last updated: 2024-04-04Bibliographically approved
Habib, N., Saqib, M., Najeh, T. & Gamil, Y. (2024). Eco-Transformation of construction: Harnessing machine learning and SHAP for crumb rubber concrete sustainability. Heliyon, 10(5), Article ID e26927.
Open this publication in new window or tab >>Eco-Transformation of construction: Harnessing machine learning and SHAP for crumb rubber concrete sustainability
2024 (English)In: Heliyon, E-ISSN 2405-8440, Vol. 10, no 5, article id e26927Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Elsevier, 2024
Keywords
Compressive strength (fc’), Crumb rubber concrete (CRC), Decision tree (DT), Random forest (RF), Shapley additive explanations (SHAP), Tensile strength (fst)
National Category
Civil Engineering
Research subject
Operation and Maintenance Engineering
Identifiers
urn:nbn:se:ltu:diva-104863 (URN)10.1016/j.heliyon.2024.e26927 (DOI)2-s2.0-85188221338 (Scopus ID)
Note

Validerad;2024;Nivå 2;2024-03-26 (hanlid);

Full text license: CC BY

Available from: 2024-03-26 Created: 2024-03-26 Last updated: 2024-03-26Bibliographically approved
Bhutto, S., Abro, F.-u., Ali, M., Buller, A. S., Bheel, N., Gamil, Y., . . . Almujibah, H. R. (2024). Effect of banana tree leaves ash as cementitious material on the durability of concrete against sulphate and acid attacks. Heliyon, 10(7), Article ID e29236.
Open this publication in new window or tab >>Effect of banana tree leaves ash as cementitious material on the durability of concrete against sulphate and acid attacks
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2024 (English)In: Heliyon, E-ISSN 2405-8440, Vol. 10, no 7, article id e29236Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Elsevier, 2024
Keywords
Banana tree leaves ash, Cementitious materials, Concrete, Durability properties, Sulphate attack, Acid attack
National Category
Materials Engineering Civil Engineering
Research subject
Operation and Maintenance Engineering
Identifiers
urn:nbn:se:ltu:diva-105175 (URN)10.1016/j.heliyon.2024.e29236 (DOI)2-s2.0-85189918832 (Scopus ID)
Note

Validerad;2024;Nivå 2;2024-04-23 (signyg);

Funder: Taif University (TU-DSPP-2024-33);

Full text license: CC BY-NC 4.0

Available from: 2024-04-22 Created: 2024-04-22 Last updated: 2024-04-23Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0003-4895-5300

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