Machine learning-based prediction of the axial load capacity of UHPC strengthened reinforced concrete columns: A comparative analysis
Viet Hai Hoang, Minh Quang Tran, Van Thuc Ngo, Parthiban Kathirvel, Parthiban Kathirvel, Parthiban Kathirvel
TL;DR
This paper uses machine learning to accurately predict the strength of concrete columns reinforced with ultra-high-performance concrete, outperforming traditional methods.
Contribution
A novel machine learning framework is proposed for predicting axial load capacity of UHPC-strengthened columns with high accuracy.
Findings
The CatBoost model achieved an R² of 0.983 in predicting axial load capacity.
ML models outperformed traditional design codes like ACI 318 and EC2 in accuracy.
SHAP analysis revealed key parameters influencing the mechanical behavior of UHPC-jacketed columns.
Abstract
This study develops and evaluates machine learning (ML) models to predict the axial load capacity (Pu) of reinforced concrete (RC) columns strengthened with ultra-high-performance concrete (UHPC) jackets. A comprehensive experimental database containing 105 test samples with 17 key input parameters was compiled from the literature, representing the most extensive dataset of UHPC-jacketed RC columns to date. Using this database, a machine learning (ML) framework was established to predict the ultimate axial load capacity, employing six models: Extremely Randomized Trees (ER) model, K-Nearest Neighbors (KNN), Light Gradient Boosting Machine (LightGBM), Xgboost, CatBoost, and Cascade Forward Neural Networks (CFNNs). The CatBoost model achieved the best performance with R² = 0.983, MAE = 177 kN, and RMSE = 211 kN, significantly outperforming traditional design codes such as ACI 318 and EC2.…
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Taxonomy
TopicsStructural Behavior of Reinforced Concrete · Innovative concrete reinforcement materials · Structural Load-Bearing Analysis