Similar papers
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
· Engineering Research Express · 0 citations
Open access
Aug 2026
Dominant-learner adaptive mixing for concrete compressive strength prediction
Accurate prediction of concrete compressive strength is essential for mixture design, quality control, and the broader use of supplementary cementitious materials in low-carbon construction. Fly ash concrete is particularly challenging to model because its strength development is affected by nonlinear interactions amon...
Jinjin Wang, Zhi-Hao Zhao, Mingjie Han
· Frontiers in Materials · 0 citations
Open access
2026
Machine Learning Approaches for Predicting Compressive Strength of Concrete: A Comparative Performance Analysis
Investigation of machine learning models for predicting the compressive strength of concrete using a publicly available experimental dataset reveals that curing age and cement content are the most influential parameters affecting compressive strength, followed by water content, which is consistent with established conc...
S. Rouabah
· ITEGAM- Journal of Engineeri... · 0 citations
Review
Open access
Aug 2026
Machine Learning for Performance Prediction of Ultra-High Performance Concrete
This paper provides a comprehensive review of ML applications in UHPC, focusing on the prediction of compressive strength, flexural strength, workability, and durability properties.
Xue-Zhi Zhang, Dan-Xiang Ma
· Journal of Computer Science... · 0 citations
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...
Yousif J. Bas, Jamal I. Kakrasul, Kamaran S. Ismail et al.
· Engineering Research Express · 0 citations
Open access
Aug 2026
Interpretable Machine Learning for Predicting Residual Drift Ratios in Reinforced Concrete Bridge Piers
Accurate prediction of the residual drift ratio of reinforced concrete bridge piers is challenging because conventional methods are computationally expensive, time-consuming, and unable to effectively capture complex nonlinear interactions among multiple influencing factors. To address these limitations, this study pro...
Min Zhang, Xuefeng Zhang, Liang-Jun Li et al.
· Buildings · 0 citations