ArticleScientific reports2026
Explainable AI-based optimization framework for sustainable fly ash concrete design.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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5 authors.
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Abstract
Fly ash is widely recognized as an effective cement replacement material for reducing the environmental impact of concrete production while maintaining acceptable mechanical performance. This study develops an explainable artificial intelligence-based multi-objective framework for the sustainable design of fly ash concrete by combining predictive modeling, feature interpretation, uncertainty assessment, and optimization within a single workflow. A compiled dataset of 1,062 concrete mix samples was used, from which outlier detection was applied only to the training subset, resulting in 58 anomalous samples being removed from the training data while the independent test set remained unchanged. Four artificial intelligence models, namely TabNet, SVR, NGBoost, and SAINT, were trained and compared for predicting 28-day compressive strength. Among them, NGBoost achieved the best testing performance, with RMSE = 4.897, MAE = 3.492, MAPE = 7.844, and R
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