Predicting Compressive Strength of Concrete Incorporating Fly Ash, Blast Furnace Slag, and Superplasticizer Using Machine Learning Techniques
Year: 2024
Type: article
Abstract: Concrete is the second most essential element in the construction industry, and its strength requirements vary based on the specific conditions of each project. However, determining the compressive strength of concrete involves laboratory tests, which wastes a lot of time and money. Researchers have developed machine learning models that predict the compressive strength of cement-based concrete having various mixes. In this research, the compressive strength of concrete incorporating fly ash, blast furnace slag, and superplasticizer is predicted using different machine learning models, namely, Linear Regression, Random Forest Regression, Decision Tree Regression, Extreme Gradient Boosting, Light Gradient Boosting, AdaBoost, and CatBoost Regression. Then the modelβs performance is evaluated based on prediction accuracy and prediction error rates i.e. R square, MSE, RMSE, MAE, MSLE, and RMSLE. Comparing all seven models, CatBoost and XGBoost have shown the highest prediction accuracy (π
2=0.8948,ππ΄πΈ=4.034,π
πππΈ= 5.766 and π
2=0.890,ππ΄πΈ=3.978,π
πππΈ=5.881) respectively. In conclusion, the CatBoost and XGBoost are the best machine learning models for predicting the compressive strength of concrete with the highest prediction accuracy, and lowest modeling error.
DOI: 10.25776/vf07-8125
Author: Yaqub, Muhammad Faisal
Institution: National University of Sciences and Technology
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6889701421
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