ArticleScientific reports2025
Machine learning approaches for forecasting compressive strength of high-strength concrete.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
- An interpretable surrogate modeling framework for rice husk ash concrete using copula-based virtual sampling.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
- Effects of cotton waste fibers on the strength development and flexural toughness of densified high-strength concrete.Scientific reports · 2026Article
- Machine learning-based prediction of compressive and flexural strength of wheat straw reinforced sustainable gypsum composites.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
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- Experimental Investigation on the Performance of Full Tailings Cemented Backfill Material in a Lead-Zinc Mine Based on Mechanical Testing.Materials (Basel, Switzerland) · 2026Article
- Performance Analysis of Boosting-Based Machine Learning Models for Predicting the Compressive Strength of Biochar-Cementitious Composites.Materials (Basel, Switzerland) · 2026Article
- Machine learning driven optimization of biomedical waste ash concrete for sustainable construction.PloS one · 2026Article
- Machine learning framework for predicting the shear capacity of demountable bolted connectors in composite beams.Scientific reports · 2025Article
- Comparative Study of Machine Learning for Predicting Compressive Strength in Oyster Shell Cementitious Composites.Materials (Basel, Switzerland) · 2025Article
- Machine learning-driven optimization of compressive and tensile strength in concrete with GGBS, eggshell powder, and waste glass powder.Scientific reports · 2025Article
- Machine Learning-Driven Strength Prediction and Sustainability Analysis of Ultra-High-Performance Concrete.Materials (Basel, Switzerland) · 2025Article
- Intelligent low carbon reinforced concrete beam design optimization via deep reinforcement learning.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identifying the mechanical properties of High Strength Concrete (HSC), particularly compressive strength, is critical for safety purposes. Concrete compressive strength is determined by using laboratory experiments, which are costly and time-consuming. Artificial intelligence (AI) methods reduce time and money. This research proposes a machine learning (ML) model using the Python programming language to predict the compressive strength of HSC. The dataset used for the models was obtained from original experimental tests. Important parameters, namely cement content, silica fume, water, superplasticizer, sand, gravel, and curing age, were taken as input to predict the output, which was the compressive strength. Various regression models were investigated for the prediction of outcome compressive strength. To optimize the models, hyperparameters were tuned, and measures such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared were used for evaluation. XGBoost (R
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