Development of a seismic vulnerability and risk model for typical bridges considering innovative intensity measures

Seismic intensity measures are one of the core parameters for estimating regional bridges’ seismic risk and vulnerability. Using macroscopic seismic intensity indicators to predict and assess the seismic vulnerability of bridges can contribute positively to establishing urban and rural earthquake risk models. However, the developed vulnerability model rarely considers the contribution of instrument intensity to its quantification results and ignores the influence of nondamage bridge samples on the development of bridge risk models and the membership relationship between seismic vulnerability levels, which makes the developed model somewhat localized and uncertain. This study proposes a quantitative method and innovative model considering composite seismic intensity (instrumental and macroseismic intensity) indicators. The model was updated and validated using 300,000 acceleration records monitored by ten seismic stations during the Luding earthquake on September 5, 2022, in China, and the features of seismic motion parameters were analysed. The established empirical vulnerability dataset of bridges during the 2008 Wenchuan earthquake in China was updated and optimized using the developed models and methods. The traditional vulnerability level of bridges has been expanded by adding and refining actual samples (2317 bridges) from different seismic intensity zones. A vulnerability prediction model based on an updated extended bridge database was developed using nonlinear regression methods. A fragility membership index calculation model for estimating the correlation features of bridge vulnerability levels is proposed by combining the probability risk and fuzzy membership correlation algorithm. The traditional method for calculating the seismic vulnerability index has been improved, and a multiparameter bridge vulnerability model validation and rationality analysis have been conducted using the newly developed bridge sample dataset.