ArticleMaterials (Basel, Switzerland)2022
Machine Learning Prediction Models to Evaluate the Strength of 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. An erratum has been issued. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Assessing machine learning prediction of recycled aggregate concrete strength with a tabular foundation model.iScience · 2026Article
- Effect of Various Recycled Construction and Demolition Fines on Cementitious Mortars Rheological and Mechanical Performance.Materials (Basel, Switzerland) · 2026Article
- Correction: Yuan et al. Machine Learning Prediction Models to Evaluate the Strength of Recycled Aggregate Concrete.Materials (Basel, Switzerland) · 2025Article
- Influences of Combined Treatment by Cement Slurry and Methyl Sodium Silicate Solution on Recycled Coarse Aggregate and Recycled Aggregate Concrete.Materials (Basel, Switzerland) · 2025Article
- Optimizing flexural strength of RC beams with recycled aggregates and CFRP using machine learning models.Scientific reports · 2024Article
- Article
- Ensemble Machine-Learning-Based Prediction Models for the Compressive Strength of Recycled Powder Mortar.Materials (Basel, Switzerland) · 2023Article
- Article
- Application of Soft-Computing Methods to Evaluate the Compressive Strength of Self-Compacting Concrete.Materials (Basel, Switzerland) · 2022Article
- Evaluating the Strength and Impact of Raw Ingredients of Cement Mortar Incorporating Waste Glass Powder Using Machine Learning and SHapley Additive ExPlanations (SHAP) Methods.Materials (Basel, Switzerland) · 2022Article
- Article
- Compressive Strength Estimation of Steel-Fiber-Reinforced Concrete and Raw Material Interactions Using Advanced Algorithms.Polymers · 2022Article
- Flexural Strength Prediction of Steel Fiber-Reinforced Concrete Using Artificial Intelligence.Materials (Basel, Switzerland) · 2022Article
- A Systematic Review of the Research Development on the Application of Machine Learning for Concrete.Materials (Basel, Switzerland) · 2022Review
- Influence of Paste Strength on the Strength of Expanded Polystyrene (EPS) Concrete with Different Densities.Polymers · 2022Article
- Assessment of Artificial Intelligence Strategies to Estimate the Strength of Geopolymer Composites and Influence of Input Parameters.Polymers · 2022Article
- Split Tensile Strength Prediction of Recycled Aggregate-Based Sustainable Concrete Using Artificial Intelligence Methods.Materials (Basel, Switzerland) · 2022Article
- Use of Artificial Intelligence Methods for Predicting the Strength of Recycled Aggregate Concrete and the Influence of Raw Ingredients.Materials (Basel, Switzerland) · 2022Article
- Exploring the Use of Waste Marble Powder in Concrete and Predicting Its Strength with Different Advanced Algorithms.Materials (Basel, Switzerland) · 2022Article
- Compressive Strength Evaluation of Ultra-High-Strength Concrete by Machine Learning.Materials (Basel, Switzerland) · 2022Article
Corrections and comments
- Erratum issued
Authors and funding
7 authors.
Funding
Abstract
Compressive and flexural strength are the crucial properties of a material. The strength of recycled aggregate concrete (RAC) is comparatively lower than that of natural aggregate concrete. Several factors, including the recycled aggregate replacement ratio, parent concrete strength, water-cement ratio, water absorption, density of the recycled aggregate, etc., affect the RAC's strength. Several studies have been performed to study the impact of these factors individually. However, it is challenging to examine their combined impact on the strength of RAC through experimental investigations. Experimental studies involve casting, curing, and testing samples, for which substantial effort, price, and time are needed. For rapid and cost-effective research, it is critical to apply new methods to the stated purpose. In this research, the compressive and flexural strengths of RAC were predicted using ensemble machine learning methods, including gradient boosting and random forest. Twelve input factors were used in the dataset, and their influence on the strength of RAC was analyzed. The models were validated and compared using correlation coefficients (R
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