Soft computing models for prediction of bentonite plastic concrete strength
Inqiad W.B. Javed M.F. Onyelowe K. Siddique M.S. Asif U. Alkhattabi L. Aslam F.
December 2024Nature Research
Scientific Reports
2024#14Issue 1
Bentonite plastic concrete (BPC) is extensively used in the construction of water-tight structures like cut-off walls in dams, etc., because it offers high plasticity, improved workability, and homogeneity. Also, bentonite is added to concrete mixes for the adsorption of toxic metals. The modified design of BPC, as compared to normal concrete, requires a reliable tool to predict its strength. Thus, this study presents a novel attempt at the application of two innovative evolutionary techniques known as multi-expression programming (MEP) and gene expression programming (GEP) and a boosting-based algorithm known as AdaBoost to predict the 28-day compressive strength () of BPC based on its mixture composition. The MEP and GEP algorithms expressed their outputs in the form of an empirical equation, while AdaBoost failed to do so. The algorithms were trained using a dataset of 246 points gathered from published literature having six important input factors for predicting. The developed models were subject to error evaluation, and the results revealed that all algorithms satisfied the suggested criteria and had a correlation coefficient (R) greater than 0.9 for both the training and testing phases. However, AdaBoost surpassed both MEP and GEP in terms of accuracy and demonstrated a lower testing RMSE of 1.66 compared to 2.02 for MEP and 2.38 for GEP. Similarly, the objective function value for AdaBoost was 0.10 compared to 0.176 for GEP and 0.16 for MEP, which indicated the overall good performance of AdaBoost compared to the two evolutionary techniques. Also, Shapley additive analysis was done on the AdaBoost model to gain further insights into the prediction process, which revealed that cement, coarse aggregate, and fine aggregate are the most important factors in predicting the strength of BPC. Moreover, an interactive graphical user interface (GUI) has been developed to be practically utilized in the civil engineering industry for prediction of BPC strength.
AdaBoost , Bentonite , Compressive strength , Genetic programming , Plastic concrete , Shapley additive explanation
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Military College of Engineering (MCE), National University of Science and Technology (NUST), Islamabad, 44000, Pakistan
Department of Civil Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi, Pakistan
Western Caspian University, Baku, Azerbaijan
Department of Civil Engineering, Michael Okpara University of Agriculture, Umudike, 440109, Nigeria
Department of Civil Engineering, Kampala International University, Kampala, Uganda
Department of Civil and Environmental Engineering, School of Engineering and Digital Sciences, Nazarbayev University, Nur-Sultan, 010000, Kazakhstan
Department of Civil and Environmental Engineering, College of Engineering, University of Jeddah, Jeddah, 23890, Saudi Arabia
Department of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia
Military College of Engineering (MCE)
Department of Civil Engineering
Western Caspian University
Department of Civil Engineering
Department of Civil Engineering
Department of Civil and Environmental Engineering
Department of Civil and Environmental Engineering
Department of Civil Engineering
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