Rapid Assessment of Seismic Risk for Railway Bridges Based on Machine Learning

When an earthquake occurs, railway bridges will suffer from different degrees of seismic damage, and it is necessary to assess the seismic risk of bridges. Unfortunately, the majority of studies were done on highway bridges without taking into account railway bridge characteristics; hence they are not applicable to railway bridges. Furthermore, current research methods for risk assessment cannot be performed quickly, and suffer from the problems of subjective personal experience, complicated calculations, and time-consuming. This paper we use machine learning for earthquake damage prediction and empirical vulnerability curves to represent risk assessment results, creating a rapid risk assessment procedure. We gathered and tallied seismic damage data from 335 railway bridges that were damaged in the Tangshan and Menyuan earthquakes, found six variables that had a substantial impact on seismic risk outcomes, and categorized the damage levels into five categories. It is essentially a multi-classification and prediction problem. In order to solve this problem, four algorithms were tested: Random Forest (RF) Back Propagation Artiifcial Neural Network (BP-ANN), PSO-Support Vector Machine (PSO-SVM), and K Nearest Neighbor (KNN). It was found that RF is the most effective method, with an accuracy rate of up to 93.31% for the training set and 89.39% for the test set. Then this study describes the new procedure in detail for rapidly assessing seismic risk to 269 bridges chosen at random from the sample pool. Firstly, the seismic damage data of bridges are collated, then the seismic damage rating is predicted using RF, and finally the empirical vulnerability curve is drawn using a two-parameter normal distribution function for the purpose of seismic damage risk assessment. The study's findings can be used as a guide for choosing a machine learning approach and its inputs to build a rapid assessment model for railway bridges.

Automated summary

This study uses machine learning and empirical vulnerability curves to predict earthquake damage and assess risk for railway bridges, creating a rapid risk assessment procedure. By collecting and tallying seismic damage data from 335 damaged railway bridges in the Tangshan and Menyuan earthquakes, six variables were identified that had a significant impact on seismic risk outcomes, and the damage levels were categorized into five categories. Four algorithms were tested, with Random Forest (RF) being the most effective method. The study describes in detail the rapid assessment procedure for 269 randomly selected bridges, including predicting the seismic damage rating using RF and drawing the empirical vulnerability curve using a two-parameter normal distribution function. The findings of this study can guide the selection of machine learning approaches and inputs to build a rapid assessment model for railway bridges.