Soft computing models for prediction of bentonite plastic concrete strength

• Published in: Scientific reports 2024-08, Vol.14 (1), p.18145-24, Article 18145
• Main Authors: Inqiad, Waleed Bin, Javed, Muhammad Faisal, Onyelowe, Kennedy, Siddique, Muhammad Shahid, Asif, Usama, Alkhattabi, Loai, Aslam, Fahid
• Format: Article
• Language: English
• Subjects: 639/166/986; 704/172/4081; AdaBoost; Algorithms; Bentonite; Civil engineering; Compressive strength; Concrete; Concrete mixes; Correlation coefficient; Gene expression; Genetic programming; Humanities and Social Sciences; Metals; multidisciplinary; Objective function; Plastic concrete; Plasticity; Predictions; Science; Science (multidisciplinary); Shapley additive explanation
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-69271-0

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.