Predictive modeling of chloride ion penetration resistance in self-compacting concrete using ensemble machine learning algorithms

This study presents a state-of-the-art ensemble machine learning (ML) model-based comprehensive evaluation of chloride ion penetration resistance of Self-Compacting Concrete (SCC) durability, simulated on an extensive dataset of 396 Rapid Chloride Penetration Test (RCPT), incorporating variations in cement, fly ash (FA), silica fume (SF), aggregate content, and temperature. Descriptive statistical analysis and visualizations revealed that moderate cement content, higher SF levels, and optimized FA replacement significantly enhance durability by reducing RCPT values. Elevated temperatures lead to increased permeability, although their impact can be mitigated in the mixes with higher SCM content. Four ensemble-based ML algorithms, Random Forest (RF), Gradient Boosting Machine (GBM), XGBoost, and CATBoost, were developed and compared for predictive modelling of RCPT values. The comparative analysis reveals XGBoost as the top performer with an R2 of 0.9989, followed by RF (R2 = 0.987), with CATBoost (R2 = 0.963) and GBM (R2 = 0.956) also performing satisfactorily. XGBoost excels in the validation with an R2 of 0.9799. The study also presents a GUI framework for user-friendly, rapid, and cost-effective preliminary assessments. The study highlights the combined role of SCMs and ML-driven predictive modelling in developing durable, sustainable, and cost-effective SCCs for modern infrastructure.