ArticleMaterials (Basel, Switzerland)2024
Performance Comparison of Machine Learning Models for Concrete Compressive Strength Prediction.
Article in Materials (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Predicting the strength of waste aggregate concrete blocks using novel hybrid machine learning models and graphical user interface deployment.Scientific reports · 2026Article
- Predictive modeling of strength characteristics of sustainable concrete using artificial neural networks with luffa fiber and marble dust.Scientific reports · 2026Article
- Predicting concrete compressive strength using optimized deep learning and large language models.Scientific reports · 2026Article
- Compressive strength prediction of carbonated recycled aggregate concrete using regression based machine learning models.Scientific reports · 2026Article
- Modeling and optimization of sustainable ternary concrete incorporating rice husk ash and extracted micro silica.Scientific reports · 2026Article
- Comparative Study of Machine Learning for Predicting Compressive Strength in Oyster Shell Cementitious Composites.Materials (Basel, Switzerland) · 2025Article
- A Comprehensive Review of Numerical and Machine Learning Approaches for Predicting Concrete Properties: From Fresh to Long-Term.Materials (Basel, Switzerland) · 2025Review
- Buckling Performance of Prefabricated Light-Gauge Steel Frame Materials Under Combined Random Defects During Construction: A CRITIC-Based Analysis.Materials (Basel, Switzerland) · 2025Article
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Authors and funding
2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
This study explores the prediction of concrete compressive strength using machine learning models, aiming to overcome the time-consuming and complex nature of conventional methods. Four models-an artificial neural network (ANN), a multiple linear regression, a support vector machine, and a regression tree-are employed and compared for performance, using evaluation metrics such as mean absolute deviation, root mean square error, coefficient of correlation, and mean absolute percentage error. After preprocessing 1030 samples, the dataset is split into two subsets: 70% for training and 30% for testing. The ANN model, further divided into training, validation (15%), and testing (15%), outperforms others in accuracy and efficiency. This outcome streamlines compressive strength determination in the construction industry, saving time and simplifying the process.
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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.