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<p>In this study, a comprehensive data set comprising 360 rapid chloride penetration test (RCPT) and 360 sorptivity measurements from 60 self-compacting concrete (SCC) mixtures with varying fly ash (FA) and silica fume (SF) contents and different temperature exposures was analyzed. To reduce reliance on labor-intensive experiments, four hybrid predictive models were developed by integrating eXtreme gradient boosting (XGBoost) with metaheuristic optimization algorithms, namely Particle Swarm Optimization, Whale Optimization Algorithm (WOA), and African Vultures Optimization Algorithm AVOA. While the primary focus is on enhancing predictive accuracy, with the XGBoost-WOA model achieving the best performance, the modeling framework also provides a foundation for future exploration of the influence of supplementary cementitious materials and curing conditions on SCC durability. Feature importance analysis identified temperature as the most critical variable influencing both RCPT (permutation score: 0.649, SHapley Additive exPlanations (SHAP): 110.626) and sorptivity (permutation score: 0.993, SHAP: 2.694). Furthermore, Monte Carlo simulations incorporating ±5% input noise confirmed the accuracy under uncertain input variable. To enhance practical utility, a Python-based Graphical User Interface was developed using Tkinter, enabling users to predict RCPT and sorptivity values for SCC mixes containing FA and SF Beyond offering an efficient alternative to traditional laboratory testing, the developed artificial intelligence (AI) models have revealed new correlations between mix composition and durability performance.</p>

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Hybrid explainable machine learning models for predicting rapid chloride penetration test and sorptivity of self-compacting concrete with fly ash and silica fume under thermal exposure

  • 期刊论文

  • 发表日期：

    2026年1月30日

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  • Divesh Ranjan Kumar,
  • Shashikant Kumar,
  • Teerapong Senjuntichai,
  • Sakdirat Kaewunruen

摘要

In this study, a comprehensive data set comprising 360 rapid chloride penetration test (RCPT) and 360 sorptivity measurements from 60 self-compacting concrete (SCC) mixtures with varying fly ash (FA) and silica fume (SF) contents and different temperature exposures was analyzed. To reduce reliance on labor-intensive experiments, four hybrid predictive models were developed by integrating eXtreme gradient boosting (XGBoost) with metaheuristic optimization algorithms, namely Particle Swarm Optimization, Whale Optimization Algorithm (WOA), and African Vultures Optimization Algorithm AVOA. While the primary focus is on enhancing predictive accuracy, with the XGBoost-WOA model achieving the best performance, the modeling framework also provides a foundation for future exploration of the influence of supplementary cementitious materials and curing conditions on SCC durability. Feature importance analysis identified temperature as the most critical variable influencing both RCPT (permutation score: 0.649, SHapley Additive exPlanations (SHAP): 110.626) and sorptivity (permutation score: 0.993, SHAP: 2.694). Furthermore, Monte Carlo simulations incorporating ±5% input noise confirmed the accuracy under uncertain input variable. To enhance practical utility, a Python-based Graphical User Interface was developed using Tkinter, enabling users to predict RCPT and sorptivity values for SCC mixes containing FA and SF Beyond offering an efficient alternative to traditional laboratory testing, the developed artificial intelligence (AI) models have revealed new correlations between mix composition and durability performance.