ArticleScientific reports2023
Physics-assisted machine learning methods for predicting the splitting tensile strength of recycled aggregate concrete.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Engineering a Multi-Dimensional Thermally Conductive Network to Enhance the Thermo-Mechanical Stability of Concrete.Materials (Basel, Switzerland) · 2026Article
- Optimization of concrete with human hair using experimental study and artificial neural network via response surface methodology and ANOVA.Scientific reports · 2025Article
- Physics-informed modeling of splitting tensile strength of recycled aggregate concrete using advanced machine learning.Scientific reports · 2025Article
- Ensemble learning based compressive strength prediction of concrete structures through real-time non-destructive testing.Scientific reports · 2024Article
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Authors and funding
5 authors.
Funding
Abstract
Recycled aggregate concrete (RAC) has become a popular building material due to its eco-friendly features, but the difficulty in predicting the crack resistance of RAC is increasingly impeding its application. In this study, splitting tensile strength is adopted to describe the crack resistance ability of RAC, and physics-assisted machine learning (ML) methods are used to construct the predictive models for the splitting tensile strength of RAC. The results show that the AdaBoost model has excellent predictive performance with the help of the Firefly algorithm, and physical assistance plays a remarkable role in selecting features and verifying the ML models. Due to the limit in data size and the generalizability of the model, the dataset should be supplemented with more representative data, and an algorithm for small sample sizes could be studied in the future.
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Registered trials
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