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[This article belongs to Volume - 57, Issue - 6]
Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Journal ID : AES-13-06-2025-843
Title :
Examining Machine Learning Methods to Determine High Performance Concrete's Strength
Varshitha D N, , Ranjan Kumar H S, , Latha D U, , Savita Choudhary, , Srushti S, , Chandana M, , Harshitha M,
Keywords : HPC, KNN, Examining Machine Learning Methods, Determine High Performance Concrete, Strength This
Varshitha D N, , Ranjan Kumar H S, , Latha D U, , Savita Choudhary, , Srushti S, , Chandana M, , Harshitha M,
Abstract : This paper explores the application of machine learning algorithms in predicting the compressive strength of high-performance concrete (HPC), a critical aspect of ensuring structural integrity in modern construction. Various machine learning models—such as XGBoost, K-nearest neighbors (KNN), Decision Tree, and Random Forest—were evaluated to predict HPC strength with high accuracy. The study compares the performance of these models using metrics like R², MAE, and RMSE to identify the most effect
Keywords : HPC, KNN, Examining Machine Learning Methods, Determine High Performance Concrete, Strength This