Cutting-Edge Hybrid Machine Learning Models for Forecasting the Acid Resistance of Cementitious Composites Incorporating Eggshell and Glass Powders

This research introduced advanced hybrid machine learning (ML) techniques to create an efficient model for estimating the compressive strength after acid attack (CSAA). The models were developed based on mixtures containing eggshell powder (ESP) and glass powder (GP). Support vector regression (SVR) was integrated with sophisticated metaheuristic optimization techniques, namely the particle swarm optimization (PSO), firefly algorithm (FFA), and gray wolf optimization (GWO), to develop advanced forecasting models for the CSAA of cementitious composites. Additionally, conventional ML models, including random forest (RF) and decision tree (DT), were utilized for comparison. All three hybrid models demonstrated strong predictive capabilities, with SVR-PSO proving to be the most reliable method, attaining the maximum coefficient of determination (R²) score of 0.984, surpassing SVR-GWO (0.981) and SVR-FFA (0.980). In contrast, the RF model recorded an R² value of 0.974, while the DT model revealed a significantly reduced R² of 0.649. The partial dependence analyses and SHapley Additive exPlanations and partial dependence plots analyses highlighted the substantial impact of various parameters, revealing that compressive strength (CS) was the most influential factor, followed by GP and ESP. CS and GP had positive effects, while ESP negatively impacted CSAA. A user-friendly interface was developed to efficiently predict CSAA.