Real-Time Seismic Damage Assessment of Various Bridge Types Using a Nonlinear Three-Stage Least Squares Approach

In the event of a strong earthquake, the motorway administrator will likely have to interrupt network operations to inspect potentially damaged bridges. Although continuing operation without inspection may be dangerous for motorway users, unnecessary interruption may have adverse consequences, especially with respect to rescue operations. This calls for development and implementation of a RApid REsponse (RARE) system, which will facilitate rationalized decisions. Such system requires real-time assessment of the seismic damage of motorway infrastructure, including which bridges are arguably the most vulnerable. This study addresses the issue by combining nonlinear finite element (FE) simulations with advanced econometric modeling. Based on a published simplified modeling approach and classification schemes, simplified FE models of characteristic bridge categories are developed, accounting for key structural components (pier, deck, abutment bearings, abutment stoppers) and soil-structure interaction. Employing the three-stage least squares (3SLS) approach, the data from the FE analyses are used to develop a relationship among the seismic damage (using the maximum and residual drift ratio, and the ratio of maximum ductility demand over ductility capacity as damage indices) of the bridge and the statistically significant intensity measures. The proposed 3SLS approach accounts for (1) both simultaneous equation bias and cross-equation contemporaneous correlation of the disturbances (error terms) caused by shared unobserved effects across the damage indices; (2) endogeneity among the damage indices with the use of instrumental variables; and (3) unobserved heterogeneity and panel effects, through the use of fixed effects. The 3SLS approach is compared to a traditional ordinary least squares (OLS) regression, and the comparison depicts the superiority of 3SLS in terms of explanatory power and forecasting accuracy.