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All Journal JURNAL SAINTIS Journal of Applied Engineering and Technological Science (JAETS)
Zaki , Ahmad
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering

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Review of Fly Ash-Based Zero-Cement Concrete Performance Fuqaha, Sameh; Zaki , Ahmad; Nugroho, Guntur
JURNAL SAINTIS Vol. 25 No. 02 (2025)
Publisher : UIR Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/saintis.2025.vol25(02).21840

Abstract

The urgent need to reduce the environmental impact of construction materials has led to increasing interest in sustainable alternatives to Ordinary Portland Cement (OPC). Among emerging solutions, Zero-Cement Concrete (ZCC) utilizing fly ash (FA) as a primary binder offers a viable pathway for lowering CO₂ emissions and reusing industrial by-products. This review investigates the key components, mixing mechanisms, curing conditions, and mechanical performance of FA-based ZCC. FA, particularly Class F and Class C, in combination with alkaline activators such as sodium hydroxide (NaOH) and sodium silicate (Na₂SiO₃), plays a crucial role in the geopolymerization process that forms the cementitious matrix. The compressive strength, modulus of elasticity, and flexural strength of ZCC are influenced by multiple factors, including activator molarity, SS/SH ratio, binder-aggregate proportions, and curing regime. Experimental studies indicate that with optimized mixing and curing parameters, FA–ZCC can achieve mechanical performance comparable to or exceeding OPC concrete. However, the absence of standardized mix design procedures and field-curing strategies remains a challenge. This study highlights the need for further research on durability, life-cycle assessment, and in-situ applications to fully realize the potential of ZCC as a mainstream, eco-efficient construction material.
Predicting Bentonite Plastic Concrete Performance Using Machine Learning Fuqaha, Sameh; zaki , Ahmad
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 1 (2025): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v7i1.8199

Abstract

This 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.
Co-Authors Fuqaha, Sameh Nugroho, Guntur