Machine Learning-Based Probabilistic Seismic Demand Model of Continuous Girder Bridges

Wenshan Li,Zikai Xie,Yong Huang,Mosbeh Kaloop

doi:10.1155/2022/3867782

Abstract

Probabilistic seismic demand model (PSDM) is one of the critical components of performance-based earthquake engineering frameworks. The aim of this study is to propose a procedure to generate PSDMs for a typical regular continuous-girder bridge subjected to far and near-fault ground motions (GMs) utilizing machine-learning methods. A series of nonlinear time history analyses (NTHAs) is carried out to calculate the damage caused by the far and near-fault GMs for four different site conditions, and 21 seismic intensity measures (IMs) are considered. Subsequently, PSDMs are established for the IMs and engineering demand parameters based on the existing NTHA data using machine-learning methods, which include linear regression, Bayesian regression (BR), and a tree-based model. The results indicated that random forest (RF) is the most suitable model to predict the longitudinal and transverse curvature at the bottom of the four piers from the coefficients of determination. More specifically, the relative importance of each parameter in the model is evaluated, and peak ground velocity (PGV), peak spectral velocity (PSV), Arias intensity (AI), and Fajfar intensity (FI) are found to be the critical factors for the RF-based PSDM. Finally, all of these parameters, except AI, are correlated with velocity. The research results explore a new method for establishing the seismic demand model of continuous-girder bridges, which can provide suggestions for seismic damage prediction and seismic insurance risk evaluation.

Highlights

Recent earthquakes have highlighted that continuous girder bridges, as key components of transportation networks, are one of the most vulnerable infrastructure components [1, 2]
Pan et al [10] focused on multispan supported steel highway bridges in New York, USA, and established Probabilistic seismic demand model (PSDM) for columns and bearings based on nonlinear time history analysis
Ma et al [14] compared the near-fault damage mechanism of continuous bridges with far-fault earthquakes and established a PSDM using an intensity parameter. e results showed that Housner intensity had the best correlation with the bridge pier drift ratio. e strategy currently employed to establish PSDMs for continuous girder bridges is based on linear regression utilizing only a single intensity measures (IMs) and engineering demand parameters

Summary

Introduction

Recent earthquakes have highlighted that continuous girder bridges, as key components of transportation networks, are one of the most vulnerable infrastructure components [1, 2]. E strategy currently employed to establish PSDMs for continuous girder bridges is based on linear regression utilizing only a single IM and engineering demand parameters. Jiang et al [15] investigated the Optimal IMs of PSDMs for isolated bridges subjected to pulse-like GMs. Wang et al [16] proposed a multidimensional fragility evaluation methodology considering multiple performance limit states and seismic demand parameters, indicating that the uncertainty and dependence between seismic demand parameters are dispensable in the fragility analysis process. Taking the dependence of the seismic demands on ground motion characteristics and the prevailing uncertainties into consideration, Huang et al [17] constructed the probabilistic demand models for reinforced concrete highway bridges with one single-column bent. Random forest (RF) was found to be the most suitable model, and the relative importance of each input IM was elucidated

Overview of ML Regression Methods

Example Bridge and Its Modeling

Probabilistic Seismic Demand Models

Methods