ArticleMaterials (Basel, Switzerland)2021
Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm.
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 47 papers.
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Who cites it
47 citing papers in PubMed.
- Optimizing the mechanical performance of sustainable industrial waste modified concrete using supervised machine learning modeling and feature importance analysis.Scientific reports · 2026Article
- Assessing the impact of groundwater abstraction and concrete dam fractures on saltwater intrusion using numerical modeling and interpretable machine learning.Scientific reports · 2026Article
- ANN trained by BBO for modeling of fly ash cementitious systems with high range water reducing admixtures.Scientific reports · 2026Article
- Compressive strength modelling of cenosphere and copper slag-based geopolymer concrete using deep learning model.Scientific reports · 2025Article
- Machine learning approaches for forecasting compressive strength of high-strength concrete.Scientific reports · 2025Article
- Optimizing blended cement concrete strength using the Box-Behnken design technique.Scientific reports · 2025Article
- Enhancing the Predictions of Cytomegalovirus Infection in Severe Ulcerative Colitis Using a Deep Learning Ensemble Model: Development and Validation Study.JMIR medical informatics · 2025Article
- Advanced and hybrid machine learning techniques for predicting compressive strength in palm oil fuel ash-modified concrete with SHAP analysis.Scientific reports · 2025Article
- Prediction of Concrete Compressive Strength Based on ISSA-BPNN-AdaBoost.Materials (Basel, Switzerland) · 2024Article
- Presenting a model for estimating the cube compressive strength of self-compacting concrete in cast in-situ piles using GEP.Scientific reports · 2024Article
- Machine learning and interactive GUI for concrete compressive strength prediction.Scientific reports · 2024Article
- Article
- Forecasting the strength of preplaced aggregate concrete using interpretable machine learning approaches.Scientific reports · 2024Article
- Mechanical Framework for Geopolymer Gels Construction: An Optimized LSTM Technique to Predict Compressive Strength of Fly Ash-Based Geopolymer Gels Concrete.Gels (Basel, Switzerland) · 2024Article
- Systematic literature review on the application of machine learning for the prediction of properties of different types of concrete.PeerJ. Computer science · 2024Article
- Article
- An efficient machine learning approach for predicting concrete chloride resistance using a comprehensive dataset.Scientific reports · 2023Article
- Assessment of the Applicability of Selected Data Mining Techniques for the Classification of Mortars Containing Recycled Aggregate.Materials (Basel, Switzerland) · 2022Article
- Prediction of Autogenous Shrinkage of Concrete Incorporating Super Absorbent Polymer and Waste Materials through Individual and Ensemble Machine Learning Approaches.Materials (Basel, Switzerland) · 2022Article
- Data-Driven Techniques for Evaluating the Mechanical Strength and Raw Material Effects of Steel Fiber-Reinforced Concrete.Materials (Basel, Switzerland) · 2022Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning techniques are widely used algorithms for predicting the mechanical properties of concrete. This study is based on the comparison of algorithms between individuals and ensemble approaches, such as bagging. Optimization for bagging is done by making 20 sub-models to depict the accurate one. Variables like cement content, fine and coarse aggregate, water, binder-to-water ratio, fly-ash, and superplasticizer are used for modeling. Model performance is evaluated by various statistical indicators like mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). Individual algorithms show a moderate bias result. However, the ensemble model gives a better result with R
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