ArticleMaterials (Basel, Switzerland)2022
A Comparison of Machine Learning Tools That Model the Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete.
Article in Materials (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Predicting compressive strength of mortars containing recycled CRT glass using GMDH and GEP methods.Scientific reports · 2026Article
- Machine learning-based prediction of the axial load capacity of UHPC strengthened reinforced concrete columns: A comparative analysis.PloS one · 2026Article
- Self compacting concrete with recycled aggregate compressive strength prediction based on gradient boosting regression tree with Bayesian optimization hybrid model.Scientific reports · 2025Article
- Assessment of compressive strength of eco-concrete reinforced using machine learning tools.Scientific reports · 2025Article
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- A robust LightGBM model for concrete tensile strength forecast to aid in resilience-based structure strategies.Heliyon · 2024Article
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- Predicting 28-day compressive strength of fibre-reinforced self-compacting concrete (FR-SCC) using MEP and GEP.Scientific reports · 2024Article
- Article
- Investigating the Effect of Processing and Material Parameters of Alginate Dialdehyde-Gelatin (ADA-GEL)-Based Hydrogels on Stiffness by XGB Machine Learning Model.Bioengineering (Basel, Switzerland) · 2024Article
- XGB Modeling Reveals Improvement of Compressive Strength of Cement-Based Composites with Addition of HPMC and Chitosan.Materials (Basel, Switzerland) · 2024Article
- Article
- Theoretical Investigation on Indirect Tensile Strength of Concrete with Rectangular Cross-Section under Locally Distributed Load.Materials (Basel, Switzerland) · 2023Article
- Article
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Authors and funding
4 authors.
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Abstract
Several types of research currently use machine learning (ML) methods to estimate the mechanical characteristics of concrete. This study aimed to compare the capacities of four ML methods: eXtreme gradient boosting (XG Boost), gradient boosting (GB), Cat boosting (CB), and extra trees regressor (ETR), to predict the splitting tensile strength of 28-day-old self-compacting concrete (SCC) made from recycled aggregates (RA), using data obtained from the literature. A database of 381 samples from literature published in scientific journals was used to develop the models. The samples were randomly divided into three sets: training, validation, and test, with each having 267 (70%), 57 (15%), and 57 (15%) samples, respectively. The coefficient of determination (R
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Registered trials
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