Damage Identification of Urban Overpass Based on Hybrid Neurogenetic Algorithm Using Static and Dynamic Properties

Hanbing Liu,Xianqiang Wang,Yubo Jiao,Yafeng Gong

doi:10.1155/2015/404675

Abstract

Urban overpass is an important component of transportation system. Health condition of overpass is essential to guarantee the safe operation of urban traffic. Therefore, damage identification of urban overpass possesses important practical significance. In this paper, finite element model of left auxiliary bridge of Qianjin Overpass is constructed and vulnerable sections of structure are chosen as objects for damage recognition. Considering the asymmetry of Qianjin bridge, change rate of modal frequency and strain ratio are selected as input parameters for hybrid neurogenetic algorithm, respectively. Identification effects of damage location and severity are investigated and discussed. The results reveal that the proposed method can successfully identify locations and severities with single and multiple damage locations; its interpolation ability is better than extrapolation ability. Comparative analysis with BP neural network is conducted and reveals that the damage identification accuracy of hybrid neurogenetic algorithm is superior to BP. The effectiveness between dynamic and static properties as input variable is also analyzed. It indicates that the identification effect of strain ratios is more satisfactory than frequency ratio.

Highlights

Bridge structures are exposed to various external environments including vehicle, wind, and temperature
The results reveal that damage localization of hybrid neurogenetic algorithm with multi-damage locations is favorable
A hybrid neurogenetic algorithm-based method is proposed for damage identification of urban overpass

Summary

Introduction

Bridge structures are exposed to various external environments including vehicle, wind, and temperature. Guo and Yi built the functional relationship between structural damage and change rate of frequency based on modal perturbation theory and successfully realized the damage identification of location and level [14]. Patil and Maiti put forward a multicrack damage identification method based on modal frequency for slender Bernoulli-Euler beams [15]. Artificial Neural Networks- (ANNs-) based damage identification methods have been widely utilized because of their excellent pattern recognition capacity. Kao and Hung [20] presented a novel neural network-based approach for detecting structural damage by using two-step method. Fang et al [21] proposed a backpropagation (BP) neural network-based damage identification method using frequency response functions as input data. Mehrjoo et al [22] presented a method for estimating the damage intensities of joints for truss bridge structures using a BP neural network. Comparative analysis is conducted to verify the superiority between BP and neurogenetic algorithm and between dynamic property and static property

Theoretical Background

Numerical Simulation

Y Z1X1 Figure 6

Comparative Analysis

Conclusions

Methods