ArticleMaterials (Basel, Switzerland)2022
Prediction of the Compressive Strength for Cement-Based Materials with Metakaolin Based on the Hybrid Machine Learning Method.
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 12 papers.
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Who cites it
12 citing papers in PubMed.
- Hybrid machine learning for SCC strength prediction using metaheuristic optimization.Scientific reports · 2026Article
- Predicting concrete compressive strength using optimized deep learning and large language models.Scientific reports · 2026Article
- Performance Analysis of Boosting-Based Machine Learning Models for Predicting the Compressive Strength of Biochar-Cementitious Composites.Materials (Basel, Switzerland) · 2026Article
- Article
- Enhanced Gaussian Process Model for Predicting Compressive Strength of Ultra-High-Performance Concrete (UHPC).Materials (Basel, Switzerland) · 2024Article
- Splitting tensile strength prediction of Metakaolin concrete using machine learning techniques.Scientific reports · 2023Article
- A Novel MBAS-RF Approach to Predict Mechanical Properties of Geopolymer-Based Compositions.Gels (Basel, Switzerland) · 2023Article
- Effect and Mechanism of Metakaolin Powder (MP) on Rheological and Mechanical Properties of Cementitious Suspension.Materials (Basel, Switzerland) · 2022Article
- A Comparative Study of AI-Based International Roughness Index (IRI) Prediction Models for Jointed Plain Concrete Pavement (JPCP).Materials (Basel, Switzerland) · 2022Article
- Intelligent Design of Construction Materials: A Comparative Study of AI Approaches for Predicting the Strength of Concrete with Blast Furnace Slag.Materials (Basel, Switzerland) · 2022Article
- Predicting the Compressive Strength of the Cement-Fly Ash-Slag Ternary Concrete Using the Firefly Algorithm (FA) and Random Forest (RF) Hybrid Machine-Learning Method.Materials (Basel, Switzerland) · 2022Article
- Intelligent Design of Building Materials: Development of an AI-Based Method for Cement-Slag Concrete Design.Materials (Basel, Switzerland) · 2022Article
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5 authors.
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Abstract
Cement-based materials are widely used in construction engineering because of their excellent properties. With the continuous improvement of the functional requirements of building infrastructure, the performance requirements of cement-based materials are becoming higher and higher. As an important property of cement-based materials, compressive strength is of great significance to its research. In this study, a Random Forests (RF) and Firefly Algorithm (FA) hybrid machine learning model was proposed to predict the compressive strength of metakaolin cement-based materials. The database containing five input parameters (cement grade, water to binder ratio, cement-sand ratio, metakaolin to binder ratio, and superplasticizer) based on 361 samples was employed for the prediction. In this model, FA was used to optimize the hyperparameters, and RF was used to predict the compressive strength of metakaolin cement-based materials. The reliability of the hybrid model was verified by comparing the predicted and actual values of the dataset. The importance of five variables was also evaluated, and the results showed the cement grade has the greatest influence on the compressive strength of metakaolin cement-based materials, followed by the water-binder ratio.
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