Study on the applicability of vulnerability analysis based on long short-term memory neural network
The damage or failure of bridges can lead to severe casualties and significant economic losses.Therefore,accurate quantitative evaluation of the damage and seismic performance of bridges is of paramount importance.To achieve this goal,the commonly adopted approach is to construct vulnerability curves.Vulnerability curves represent the conditional probability that bridge components or structures reach or exceed a certain level of damage under given seismic ground motion intensities.The displacement ductility ratio of bridge piers was used as a damage indicator,and a long short-term memory(LSTM)neural network was successfully employed to establish seismic vulnerability curves for bridges.The research findings demonstrate that the model exhibits high computational efficiency and precise accuracy,enabling fast and accurate prediction of the damage indicators of bridge components under seismic actions.
bridge anti-seismicseismic vulnerabilitylong short-term memory neural networkfinite element analysis
NETL
NSTL
万方数据
维普
