ArticleScientific reports2026
Hybrid machine learning approach for predicting compressive strength of sustainable concrete incorporating palm oil fuel ash.
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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Abstract
Accurate prediction of compressive strength is important for the efficient design of sustainable concrete incorporating palm oil fuel ash (POFA). This study developed a hybrid artificial neural network model optimized using biogeography based optimization (ANN-BBO) to predict the compressive strength of POFA-based concrete using a compiled literature database of 469 mixtures collected from 22 independent studies. The proposed framework was intended to improve predictive performance relative to a conventional standalone ANN by combining nonlinear learning capability with metaheuristic parameter optimization. The models were developed using six mixture design variables: cement content, POFA content, superplasticizer dosage, coarse-to-fine aggregate ratio, water-to-binder ratio, and curing age. Model performance was evaluated using multiple statistical measures, including the coefficient of determination (R
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