Predicting the mechanical properties of plastic concrete: An optimization method by using genetic programming and ensemble learners
Asif U. Javed M.F. Abuhussain M. Ali M. Khan W.A. Mohamed A.
July 2024Elsevier Ltd
Case Studies in Construction Materials
2024#20
This study presents a comparative analysis of individual and ensemble learning algorithms (ELAs) to predict the compressive strength (CS) and flexural strength (FS) of plastic concrete. Multilayer perceptron neuron network (MLPNN), Support vector machine (SVM), random forest (RF), and decision tree (DT) were used as base learners, which were then combined with bagging and Adaboost methods to improve the predictive performance. In addition, gene expression programming (GEP) was used to develop computational equations that can be used to predict the CS and FS of plastic concrete. An extensive database containing 357 and 125 data points was obtained from the literature, and the eight most impactful ingredients were used in the models development. The accuracy of all models was assessed using several statistical measures, including an error matrix, Akaike information criterion (AIC), K-fold cross-validation, and other external validation equations. Furthermore, sensitivity and SHAP analysis were performed to evaluate input variables relative significance and impact on the anticipated CS and FS. Based on statistical measures and other validation criteria, GEP outpaces all other individual models, whereas, in ELAs, the SVR ensemble with Adaboost and RF modified with the Bagging technique demonstrated superior performance. SHapley Additive exPlanations (SHAP) and sensitivity analysis reveal that plastic, cement, water, and the age of the specimens have the highest influence, while superplasticizer has the lowest impact, which is consistent with experimental studies. Moreover, GUI and GEP-based simple mathematical correlation can enhance the practical scope of this study and be an effective tool for the pre-mix design of plastic concrete.
Compressive strength , Ensemble learning algorithms , Flexural strength , Gene expression programming , Machine learning , Plastic concrete , Sustainability
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Department of Civil Engineering, Nazarbayev University, Kazakhstan
Department of Civil Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi, Pakistan
Department of Civil Engineering, University of Louisiana at Lafayette, Lafayette, 70503, LA, United States
Department of Transport Systems, Traffic Engineering and Logistics, Faculty of Transport and Aviation Engineering, Silesian University of Technology, Krasińskiego 8 Street, Katowice, 40-019, Poland
Department of Civil and Environmental Engineering, College of Engineering and Computing in Al-Qunfudah, Umm Al-Qura University, Mecca, Saudi Arabia
Research Centre, Future University in Egypt, New Cairo, 11835, Egypt
Department of Civil Engineering
Department of Civil Engineering
Department of Civil Engineering
Department of Transport Systems
Department of Civil and Environmental Engineering
Research Centre
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