ArticleScientific reports2024
Machine learning and interactive GUI for concrete compressive strength prediction.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed.
- Concrete strength prediction using domain-driven feature engineering and explainable LightGBM.Scientific reports · 2026Article
- Optimizing the mechanical performance of sustainable industrial waste modified concrete using supervised machine learning modeling and feature importance analysis.Scientific reports · 2026Article
- Compressive strength prediction of coconut fiber reinforced concrete using PSO optimized explainable machine learning.Scientific reports · 2026Article
- Hybrid Machine learning-based modeling to predict and optimize the compressive strength of electric arc furnace slag-modified concrete.Scientific reports · 2026Article
- Artificial Intelligence in Materials Science and Engineering.Materials (Basel, Switzerland) · 2026Article
- An optimized machine learning approach for reliable design of hybrid FRP-steel-concrete tubular columns.Scientific reports · 2026Article
- Integration of machine learning and microstructural characterization for strength forecasting with silica fume and M-sand for sustainable concrete.Scientific reports · 2026Article
- Predicting concrete compressive strength using optimized deep learning and large language models.Scientific reports · 2026Article
- Predicting compressive strength of mortars containing recycled CRT glass using GMDH and GEP methods.Scientific reports · 2026Article
- Compressive strength prediction of carbonated recycled aggregate concrete using regression based machine learning models.Scientific reports · 2026Article
- Article
- Prediction of Compressive Strength of Concrete Using Explainable Machine Learning Models.Materials (Basel, Switzerland) · 2025Article
- Intelligent low carbon reinforced concrete beam design optimization via deep reinforcement learning.Scientific reports · 2025Article
- A Comprehensive Review of Numerical and Machine Learning Approaches for Predicting Concrete Properties: From Fresh to Long-Term.Materials (Basel, Switzerland) · 2025Review
- Compressive strength modelling of cenosphere and copper slag-based geopolymer concrete using deep learning model.Scientific reports · 2025Article
- An interpretable deep learning model for the accurate prediction of mean fragmentation size in blasting operations.Scientific reports · 2025Article
- Prediction of ultimate strength and strain in FRP wrapped oval shaped concrete columns using machine learning.Scientific reports · 2025Article
- Predicting the compressive strength of polymer-infused bricks: A machine learning approach with SHAP interpretability.Scientific reports · 2025Article
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Concrete compressive strength (CS) is a crucial performance parameter in concrete structure design. Reliable strength prediction reduces costs and time in design and prevents material waste from extensive mixture trials. Machine learning techniques solve structural engineering challenges such as CS prediction. This study used Machine Learning (ML) models to enhance the prediction of CS, analyzing 1030 experimental CS data ranging from 2.33 to 82.60 MPa from previous research databases. The ML models included both non-ensemble and ensemble types. The non-ensemble models were regression-based, evolutionary, neural network, and fuzzy-inference-system. Meanwhile, the ensemble models consisted of adaptive boosting, random forest, and gradient boosting. There were eight input parameters: cement, blast-furnace-slag, aggregates (coarse and fine), fly ash, water, superplasticizer, and curing days, with the CS as the output. Comprehensive performance evaluations include visual and quantitative methods and k-fold cross-validation to assess the study's reliability and accuracy. A sensitivity analysis using Shapley-Additive-exPlanations (SHAP) was conducted to understand better how each input variable affects CS. The findings showed that the Categorical-Gradient-Boosting (CatBoost) model was the most accurate prediction during the testing stage. It had the highest determination-coefficient (R
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