Prediction of Neutralization Depth of RC Bridges Using Machine Learning Methods

Abstract

Machine learning techniques have become a popular solution to prediction problems. These approaches show excellent performance without being explicitly programmed. In this paper, 448 sets of data were collected to predict the neutralization depth of concrete bridges in China. Random forest was used for parameter selection. Besides this, four machine learning methods, such as support vector machine (SVM), k-nearest neighbor (KNN) and XGBoost, were adopted to develop models. The results show that machine learning models obtain a high accuracy (>80%) and an acceptable macro recall rate (>80%) even with only four parameters. For SVM models, the radial basis function has a better performance than other kernel functions. The radial basis kernel SVM method has the highest verification accuracy (91%) and the highest macro recall rate (86%). Besides this, the preference of different methods is revealed in this study.

Duan, Kangkang

PhD Student of Civil Engineering

Kangkang is a PhD student at the University of British Columbia. Previously, he received his BSc degree in Highway and Bridge Engineering from Southeast University (Mao Yisheng Class) in 2019. Then, he received his MASc degree in Civil Engineering from Southeast University in 2022. His research interests include robotics, artificial intelligence, AR/VR, and physical machine learning.