ArticleMaterials (Basel, Switzerland)2022
Application of Machine Learning Approaches to Predict the Strength Property of Geopolymer 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 12 papers.
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Who cites it
12 citing papers in PubMed.
- Machine learning-driven evaluation of mechanical and microstructural properties of agro-waste-derived geopolymer concrete.Scientific reports · 2026Article
- Evaluate the compressive strength of fiber-reinforced geopolymer concrete incorporating ground granulated blast furnace slag with machine learning approach.Scientific reports · 2026Article
- Two stage AI framework for strength prediction and generative LLM for geopolymer concrete.Scientific reports · 2026Article
- A Hybrid Clustering-Classification Approach for Predicting Strength and Analyzing Material Composition of Geopolymers.Polymers · 2026Article
- Assessment of thermally induced strength loss in alkali-activated concrete through ensemble regression models.Scientific reports · 2026Article
- Prediction of the Unconfined Compressive Strength of One-Part Geopolymer-Stabilized Soil Under Acidic Erosion: Comparison of Multiple Machine Learning Models.Materials (Basel, Switzerland) · 2026Article
- Artificial intelligence prediction of the mechanical properties of banana peel-ash and bagasse blended geopolymer concrete.Scientific reports · 2024Article
- A comprehensive quantitative bottom-up analysis of fiber-reinforced recycled-aggregate concrete behavior.Scientific reports · 2023Article
- Geopolymer: A Systematic Review of Methodologies.Materials (Basel, Switzerland) · 2022Review
- Article
- A Systematic Review of the Research Development on the Application of Machine Learning for Concrete.Materials (Basel, Switzerland) · 2022Review
- Assessment of Artificial Intelligence Strategies to Estimate the Strength of Geopolymer Composites and Influence of Input Parameters.Polymers · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Geopolymer concrete (GPC) based on fly ash (FA) is being studied as a possible alternative solution with a lower environmental impact than Portland cement mixtures. However, the accuracy of the strength prediction still needs to be improved. This study was based on the investigation of various types of machine learning (ML) approaches to predict the compressive strength (C-S) of GPC. The support vector machine (SVM), multilayer perceptron (MLP), and XGBoost (XGB) techniques have been employed to check the difference between the experimental and predicted results of the C-S for the GPC. The coefficient of determination (R
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