What question did this study set out to answer?

This research aims to develop a machine learning framework for predicting the strength of concrete made with industrial waste while assessing its environmental impact.

June 10, 2026SustainabilityOpen Access

Explainable Machine Learning Framework for Strength Prediction of Sustainable Concrete Incorporating Industrial Waste SCMs with an Embodied Impact Assessment

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Authors

ZTZeeshan TariqUniversity of West London ABAli Bahadori-JahromiUniversity of West London SRShah RoomUniversity of West London

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Implication

Randomized trial evaluates strength and sustainability of concrete mixtures with industrial waste, suggesting low carbon alternatives.

Key Points

This research aims to develop a machine learning framework for predicting the strength of concrete made with industrial waste while assessing its environmental impact.
Conducted an experimental program to analyze compressive and tensile strength of various concrete mixtures.
Employed ensemble machine learning models, optimizing performance using Particle Swarm Optimization and Bayesian Optimization techniques.
Utilized k-fold cross-validation for robustness and assessed model performance with metrics such as R2, RMSE, and MAE.
Hybrid GF4 mix with 40% cement replacement showed an R2 of 0.879 for compressive strength, improved to 0.918 after optimization.
Gradient Boosting model achieved an R2 of 0.840 for tensile strength, optimized to 0.879 post-PSO technique.
The GF4 mix demonstrated a 36% reduction in embodied carbon compared to the control mix.

Cite This Study

Tariq et al. (2026) studied this question.

synapsesocial.com/papers/6a28fff36f82f25be989cb29 https://doi.org/10.3390/su18125848

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Explainable Machine Learning Framework for Strength Prediction of Sustainable Concrete Incorporating Industrial Waste SCMs with an Embodied Impact Assessment

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Key Points

Cite This Study

Explainable Machine Learning Framework for Strength Prediction of Sustainable Concrete Incorporating Industrial Waste SCMs with an Embodied Impact Assessment

Discussion

Member takes

Implication

Key Points

Cite This Study

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