Sensitivity analysis in seismic reliability of an urban self-anchored suspension bridge

Seismic resistance and disaster prevention for self-anchored suspension bridge (SASB) are important, but the seismic reliability of this complex structure is time-consuming or low-accuracy based on existing methods, let alone the relative sensitivity analysis. A sampling-based hybrid methodology of sensitivity analysis in seismic reliability, combining subset simulation (SuS), explicit time domain method (ETDM), BP neural network (BPNN) and Pearson's Linear Correlation Coefficient (PLCC), is proposed herein. The separated treatment of low and high variabilities of structural and earthquake parameters based on ETDM with BPNN accelerates the computing efficiency. With the application to the SASB, sensitivities of structural parameters to seismic reliability are obtained, where three largest ones are ambient temperature, Young's modulus and density of girder. The failure probability of this bridge is mostly smaller than 1.0 x 10(-3), but it takes only about 10 similar to 15 min to obtain single failure probability with a deterministic parameter vector based on the proposed method, which is in lieu of hundreds of days based on Monte Carlo simulation. Hence, it is verified a sound approach with great accuracy and efficiency used in engineering structures.

Automated summary

This study introduces a hybrid sensitivity analysis method based on sampling, which shows high accuracy and efficiency in seismic reliability of self-anchored suspension bridges. The sensitivity analysis of structural parameters to seismic reliability indicates that environmental temperature, Young's modulus, and girder density are important factors affecting the bridge.