ArticleMaterials (Basel, Switzerland)2021
Modeling of Compressive Strength of Self-Compacting Rubberized Concrete Using Machine Learning.
Article in Materials (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- Modeling mechanical properties of rubberized concrete using gene expression programming (GEP) and random forest: a comparative study.Scientific reports · 2026Article
- Methodology for the Early Detection of Damage Using CEEMDAN-Hilbert Spectral Analysis of Ultrasonic Wave Attenuation.Materials (Basel, Switzerland) · 2025Article
- Prediction of compressive strength of concrete based on improved artificial bee colony-multilayer perceptron algorithm.Scientific reports · 2024Article
- Article
- Systematic literature review on the application of machine learning for the prediction of properties of different types of concrete.PeerJ. Computer science · 2024Article
- Machine Learning-Based Predictive Model for Tensile and Flexural Strength of 3D-Printed Concrete.Materials (Basel, Switzerland) · 2023Article
- Application of the Improved POA-RF Model in Predicting the Strength and Energy Absorption Property of a Novel Aseismic Rubber-Concrete Material.Materials (Basel, Switzerland) · 2023Article
- Application of Soft-Computing Methods to Evaluate the Compressive Strength of Self-Compacting Concrete.Materials (Basel, Switzerland) · 2022Article
- Design of a machine learning model for the precise manufacturing of green cementitious composites modified with waste granite powder.Scientific reports · 2022Article
- Application of Artificial Intelligence Methods for Predicting the Compressive Strength of Self-Compacting Concrete with Class F Fly Ash.Materials (Basel, Switzerland) · 2022Article
- A Comparison of Machine Learning Tools That Model the Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete.Materials (Basel, Switzerland) · 2022Article
- Prediction of Bearing Capacity of the Square Concrete-Filled Steel Tube Columns: An Application of Metaheuristic-Based Neural Network Models.Materials (Basel, Switzerland) · 2022Article
- A Comparative Study for the Prediction of the Compressive Strength of Self-Compacting Concrete Modified with Fly Ash.Materials (Basel, Switzerland) · 2021Article
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4 authors.
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Abstract
This paper gives a comprehensive overview of the state-of-the-art machine learning methods that can be used for estimating self-compacting rubberized concrete (SCRC) compressive strength, including multilayered perceptron artificial neural network (MLP-ANN), ensembles of MLP-ANNs, regression tree ensembles (random forests, boosted and bagged regression trees), support vector regression (SVR) and Gaussian process regression (GPR). As a basis for the development of the forecast model, a database was obtained from an experimental study containing a total of 166 samples of SCRC. Ensembles of MLP-ANNs showed the best performance in forecasting with a mean absolute error (MAE) of 2.81 MPa and Pearson's linear correlation coefficient (R) of 0.96. The significantly simpler GPR model had almost the same accuracy criterion values as the most accurate model; furthermore, feature reduction is easy to combine with GPR using automatic relevance determination (ARD), leading to models with better performance and lower complexity.
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