[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126471-en":3,"doc-seo-126471-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126471,962084925290,"Ophelia","https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Predicting Bentonite Plastic Concrete Performance Using Machine Learning","This study develops an interpretable machine learning framework to predict the mechanical properties of bentonite plastic concrete (BPC), a key material for low-permeability geotechnical structures. Traditional BPC testing is costly and time-intensive, while empirical equations often miss nonlinear effects of bentonite and curing conditions. Four optimized ensemble models are trained on curated experimental datasets to predict slump, tensile strength, and elastic modulus using the FBIO algorithm. SHAP identifies curing time, cement, and water content as dominant drivers. Results show XGB–FBIO achieves the best slump and tensile performance, and GBRT–FBIO leads for elastic modulus, enabling explainable, resource-efficient mix optimization for environmental and geotechnical applications.","PREDICTING BENTONITE PLASTIC CONCRETE PERFORMANCE USING MACHINE LEARNING  \nSameh Fuqaha1, Ahmad Zaki2*  \nMaster Program of Civil Engineering, Universitas Muhammadiyah Yogyakarta, Yogyakarta, 55183, Indonesia 12  \nDepartment of Civil Engineering, Universitas Muhammadiyah Yogyakarta, 55183 Yogyakarta, Indonesia2  \n[ahmad.zaki@umy.ac.id](ahmad.zaki@umy.ac.id)  \nReceived: 31 May 2025, Revised: 08 October 2025, Accepted: 27 October 2025  \n*Corresponding Author  \nABSTRACT  \nThis study develops an interpretable machine learning framework to predict the mechanical properties of bentonite plastic concrete (BPC), an essential material for low-permeability geotechnical structures. Traditional testing of BPC is time-and cost-intensive, while empirical equations often fail to capture the nonlinear effects of bentonite and curing conditions. To address these limitations, four ensemble learning models were optimized using the Forensic-Based Investigation Optimization (FBIO) algorithm, a parameter-free metaheuristic inspired by investigative search processes. The models were trained on three curated experimental datasets to predict slump, tensile strength, and elastic modules. Among all, XGB– FBIO achieved the highest accuracy for slump (R² = 0.98) and tensile strength (R² = 0.99), while GBRT– FBIO performed best for elastic modulus (R² = 0.97). SHapley Additive exPlanations (SHAP) analysis revealed curing time, cement, and water content as the most influential variables. The results demonstrate that the proposed framework can replace repetitive laboratory trials with data-driven insights, providing engineers with a reliable, explainable, and resource-efficient tool for optimizing BPC mix designs in environmental and geotechnical applications.  \nKeywords : Bentonite Plastic Concrete (BPC), Ensemble Learning, Forensic-Based Investigation Optimization (FBIO), Mechanical Property Prediction, SHAP Analysis.  \n1. Introduction  \nThe management of industrial wastewater presents a significant challenge in the global sustainability agenda, particularly due to its high content of toxic heavy metals such as chromium (Cr), mercury (Hg), copper (Cu), lead (Pb), cadmium (Cd), zinc (Zn), and nickel (Ni) (Keramati et al., 2019) . These contaminants are persistent, bio accumulative, and pose severe ecological risks due to their non-biodegradable nature. Among the various treatment strategies available, adsorption has gained prominence for its cost-effectiveness and efficiency in removing metal ions from aqueous environments (Liu et al., 2020) . Natural clay minerals, especially bentonite, have attracted increasing attention due to their favorable physicochemical properties, including high surface area, strong ion exchange capacity, environmental safety, and widespread availability (Barakan & Aghazadeh, 2021) .  \nBentonite, primarily consisting of montmorillonite along with minor constituents such as calcite and quartz, has been extensively studied for heavy metal adsorption (Dhar et al., 2023; Shubber & Kebria, 2023) . Recent advancements have explored the integration of bentonite into concrete matrices to form bentonite plastic concrete (BPC) , a composite material that inherits the mechanical strength of conventional concrete and the low-permeability, self-sealing nature of bentonite. BPC is particularly suitable for geotechnical and environmental applications, such as cut-off walls in dam foundations, due to its viscoelasticity, hydraulic impermeability, and deformation tolerance (Bahrami & Mir Mohammad Hosseini, 2022) . Abbaslou et al. (2016) illustrated the dual functionality of BPC in removing cadmium ions from water while also enhancing durability and reducing structural cracking.  \nTraditional experimental and empirical approaches used to evaluate the mechanical and durability properties of bentonite plastic concrete (BPC) are constrained by high cost, time consumption, and limited adaptability to diverse mix proportions and curing environments (Ni et","cbCaisyApMrsrtQP","https://ap.wps.com/l/cbCaisyApMrsrtQP","pdf",2076334,10,1,29,"English","en",105,"# Introduction\n## Challenges in predicting BPC mechanical performance\n## Machine learning for concrete and composite materials","[{\"question\":\"What is the main goal of this study on bentonite plastic concrete (BPC)?\",\"answer\":\"To build an interpretable machine learning framework that predicts key mechanical properties of BPC and supports mix design optimization with reduced reliance on repeated laboratory testing.\"},{\"question\":\"Which models and optimization method are used to predict BPC properties?\",\"answer\":\"Four ensemble learning models are optimized using the Forensic-Based Investigation Optimization (FBIO) algorithm, and they are trained on three curated experimental datasets.\"},{\"question\":\"Which variables most influence the predictions according to SHAP analysis?\",\"answer\":\"Curing time, cement, and water content are identified as the most influential variables affecting BPC performance.\"}]","Predicting Bentonite Plastic Concrete Performance Using Machine Learning | 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is the main goal of this study on bentonite plastic concrete (BPC)?","Question",{"text":77,"@type":78},"To build an interpretable machine learning framework that predicts key mechanical properties of BPC and supports mix design optimization with reduced reliance on repeated laboratory testing.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Which models and optimization method are used to predict BPC properties?",{"text":82,"@type":78},"Four ensemble learning models are optimized using the Forensic-Based Investigation Optimization (FBIO) algorithm, and they are trained on three curated experimental datasets.",{"name":84,"@type":75,"acceptedAnswer":85},"Which variables most influence the predictions according to SHAP analysis?",{"text":86,"@type":78},"Curing time, cement, and water content are identified as the most influential variables affecting BPC 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