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Open access Aug 2026

Novel interpretable machine learning models for predicting compressive strength of nano-silica concrete

This study provides a robust, interpretable, and generalizable ML framework for optimizing nano-silica concrete mix design and highlights the strong potential of ML, particularly ensemble models combined with explainable AI techniques, to improve prediction reliability, reduce trial-and-error experimentation, and suppo...

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Sep 2026

Machine-learning prediction and multi-objective optimization of concrete sulfate resistance

Sulfate attack progressively deteriorates concrete in marine, saline-soil, and sulfate-rich environments. This study developed an interpretable machine-learning and multi-objective optimization framework for sulfate-resistance-oriented concrete design. A literature-based dataset containing 744 records from 21 publicati...

Yi-Hang Guo, Jia-Yu Li, Li Li et al. · 0 citations
Open access Aug 2026

Bayesian-optimised machine learning for predicting aggressive environment resistance and service life of recycled aggregate geopolymer concrete

Developing reliable computational tools for durability and service-life assessment of concrete structures in aggressive environments is essential for advancing predictive modeling in structural engineering. This study introduces machine learning (ML)–based models for forecasting the sulfate and acid resistance of recyc...

P. Singh, Puja Rajhans · 0 citations
Open access Sep 2026

Compressive Strength Prediction of Red Mud Concrete Using Explainable and Uncertainty-Aware Artificial Intelligence Models

This study develops and compares four machine learning and deep learning models, namely the Deep Gradient Boosting Machine, the Differentiable Neural Decision Tree, Long Short-Term Memory, and the Monte Carlo Dropout Neural Network, for the accurate and uncertainty-aware prediction of the compressive strength of red mu...

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Open access Aug 2026

Innovative sustainable concrete with waste glass materials: an explainable machine learning for compressive strength prediction

Sustainable concrete incorporating waste glass materials has emerged as a promising solution to reduce environmental impacts associated with cement production and natural aggregate depletion. Accurate prediction of compressive strength (CS) is essential for optimizing such mixtures and ensuring structural reliability....

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Comparative and Interpretable Machine Learning for Fly Ash Geopolymer Strength: Prediction, Validation and Optimization

An interpretable machine learning framework integrating Extreme Gradient Boosting with Shapley Additive Explanations to predict the 28-day compressive strength of fly ash-based geopolymer concrete (FA-GPC) is developed and experimentally validates.

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