Rapid seismic damage assessment of railway piers based on machine learning
In the aftermath of an earthquake,prompt damage assessment of bridge structures is a crucial step toward restoring traffic flow.This study focuses on representative railway rectangular bridge piers,validating the reliability of the finite element modeling method through four sets of quasistatic tests.We conducted endurance time analyses on 1 000 sets of data derived from the finite element model of bridge piers in both longitudinal and transverse directions.To fit the seismic dy-namic response requirements,we constructed a BP neural network and established a rapid evaluation model for assessing seismic damage to railway rectangular bridge piers.The efficacy of this model was then confirmed through its application to a three-span concrete beam bridge.Our findings suggest that the reinforcement ratio,stirrup ratio,shear span ratio,and axial compression ratio are the four main fac-tors affecting the seismic damage of piers.Meanwhile,the aspect ratio and the strength of both concrete and steel bars emerge as secondary factors.Under a design earthquake with a PGA of 0.32g,the prob-abilities of minor damage to the bridge,as calculated by numerical analysis and rapid evaluation of the neural network model,are 96.7％,44.6％,49.1％,and 96.7％,and 95.6％,40.4％,60.9％,and 95.8％,respectively.The probabilities of moderate damage are 40.1％,1.2％,1.6％,and 40.1％,and 37.4％,2.3％,6.0％,and 37.7％,respectively.The BP neural network algorithm can effectively establish the relationship between structural parameters and seismic responses,producing output errors within an acceptable range and exhibiting a high degree of re-gression.The BP neural network-based bridge seismic damage assessment model demonstrates excellent universality and can effectively replace some numerical simulation calculations.
seismic resistance of bridgeneural networkendurance time analysisrequirement analysisfragility
国家科技期刊平台
NSTL
万方数据
维普
