ArticleScientific reports2025
Predicting compressive and splitting tensile strength of high volume fly ash roller compacted concrete using ANN and ANN-biogeography based optimization models.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Article
- AI-based modeling of COScientific reports · 2025Article
- Data-driven formulation of steel fiber pull-out force in cementitious composites using genetic programming.Scientific reports · 2025Article
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6 authors.
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Abstract
Roller compacted concrete (RCC) has gained prominence in the construction industry due to its durability, cost-effectiveness, and environmental benefits, particularly with the incorporation of high-volume fly ash (HVFA). However, traditional experimental approaches to evaluating RCC's mechanical properties, such as compressive strength (CS) and splitting tensile strength (STS), are resource-intensive and time-consuming. To address these challenges, this study explores the application of artificial intelligence (AI), specifically artificial neural networks (ANN) and a hybrid ANN-Biogeography-Based Optimization (ANN-BBO) model, to predict the CS and STS of RCC. A dataset comprising 168 RCC mixtures, incorporating various material and process parameters, was analyzed. The ANN-BBO model demonstrated superior predictive accuracy compared to a standalone ANN, with R
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