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.

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.

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.

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.

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.

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.

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.

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.