ArticleMaterials (Basel, Switzerland)2022
Prediction of Mechanical Properties of Fly-Ash/Slag-Based Geopolymer Concrete Using Ensemble and Non-Ensemble Machine-Learning Techniques.
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 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Explainable machine learning based optimization of strength, durability, and carbon efficiency of fly ash-GGBS geopolymer concrete.Scientific reports · 2026Article
- Ensemble machine learning models for predicting strength of concrete with foundry sand and coal bottom ash as fine aggregate replacements.Scientific reports · 2025Article
- Comparison of deep LSTM and machine learning models for predicting compressive strength of fly ash/slag-based geopolymer concrete.Scientific reports · 2025Article
- Predicting workability and mechanical properties of bentonite plastic concrete using hybrid ensemble learning.Scientific reports · 2025Article
- Prediction and validation of mechanical properties of self-compacting geopolymer concrete using combined machine learning methods a comparative and suitability assessment of the best analysis.Scientific reports · 2025Article
- Special Issue: "Recent Developments in Geopolymers and Alkali-Activated Materials".Materials (Basel, Switzerland) · 2024Article
- Knowledge Mapping of the Literature on Fiber-Reinforced Geopolymers: A Scientometric Review.Polymers · 2022Review
- A Systematic Review of the Research Development on the Application of Machine Learning for Concrete.Materials (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
The emission of greenhouse gases and natural-resource depletion caused by the production of ordinary Portland cement (OPC) have a detrimental effect on the environment. Thus, an alternative means is required to produce eco-friendly concrete such as geopolymer concrete (GPC). However, GPC has a complex cementitious matrix and an ambiguous mix design. Aside from that, the composition and proportions of materials utilized may have an impact on the compressive strength. Similarly, the use of robust and efficient machine-learning (ML) approaches is now required to forecast the strength of such a composite cementitious matrix. As a result, this study anticipated the compressive strength of GPC with waste resources using ensemble and non-ensemble ML algorithms. This was accomplished through the use of Anaconda (Python). To build a strong ensemble learner by integrating weak learners, adaptive boosting, random forest (RF), and ensemble learner bagging were employed. Furthermore, ensemble learners were utilized on non-ensemble or weak learners, such as decision trees (DT) and support vector machines (SVM) via regression. The data encompassed 156 statistical samples in which nine variables, namely superplasticizer (kg/m
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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.