ArticlePolymers2022
Application of Soft Computing Techniques to Predict the Strength of Geopolymer Composites.
Article in Polymers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
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The trial behind it
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Who cites it
25 citing papers in PubMed, 73 citations in OpenAlex.
- Evaluate the compressive strength of fiber-reinforced geopolymer concrete incorporating ground granulated blast furnace slag with machine learning approach.Scientific reports · 2026Article
- Machine Learning-Guided Design of High-Performance Antifungal Polymers againstACS polymers Au · 2025Article
- Review
- A study of gene expression programming algorithm for dynamically adjusting the parameters of genetic operators.PloS one · 2025Article
- A new approach to prediction riboflavin absorbance using imprinted polymer and ensemble machine learning algorithms.Heliyon · 2023Article
- Article
- Knowledge Mapping of the Literature on Fiber-Reinforced Geopolymers: A Scientometric Review.Polymers · 2022Review
- 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
- Geopolymer: A Systematic Review of Methodologies.Materials (Basel, Switzerland) · 2022Review
- Article
- Article
- Compressive Strength Estimation of Steel-Fiber-Reinforced Concrete and Raw Material Interactions Using Advanced Algorithms.Polymers · 2022Article
- Predicting the Rheological Properties of Super-Plasticized Concrete Using Modeling Techniques.Materials (Basel, Switzerland) · 2022Article
- Article
- A Systematic Review of the Research Development on the Application of Machine Learning for Concrete.Materials (Basel, Switzerland) · 2022Review
- Using Machine Learning Algorithms to Estimate the Compressive Property of High Strength Fiber Reinforced Concrete.Materials (Basel, Switzerland) · 2022Article
- Assessment of Artificial Intelligence Strategies to Estimate the Strength of Geopolymer Composites and Influence of Input Parameters.Polymers · 2022Article
- Compressive Strength of Steel Fiber-Reinforced Concrete Employing Supervised Machine Learning Techniques.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
Corrections and comments
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Authors and funding
6 authors at 5 institutions in 6 countries.
Funding
Abstract
Geopolymers may be the best alternative to ordinary Portland cement because they are manufactured using waste materials enriched in aluminosilicate. Research on geopolymer composites is accelerating. However, considerable work, expense, and time are needed to cast, cure, and test specimens. The application of computational methods to the stated objective is critical for speedy and cost-effective research. In this study, supervised machine learning approaches were employed to predict the compressive strength of geopolymer composites. One individual machine learning approach, decision tree, and two ensembled machine learning approaches, AdaBoost and random forest, were used. The coefficient correlation (R
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.