UCLA

Model parameter updating can enhance the use of nonlinear structural response simulation to guide decision-making in the postearthquake environment. Since most structures in high seismic regions are not instrumented with sensors, the response history during ground shaking is usually not available after an earthquake. Nevertheless, technologies such as Light Detection and Ranging (LiDAR) and drone-mounted imaging devices have made it possible to more effectively measure residual deformations after the shaking has subsided. It is within this context that a framework for performing nonlinear structural model parameter updating based only on residual drift measurements is proposed. The considered setting is one where a structure is subjected to a sequence of ground motions (without repairs), whereby after each event, the structural model parameters are updated using a Bayesian formulation and the measured residual drift. The methodology is demonstrated by using experimental data from a reinforced concrete bridge pier subjected to six back-to-back ground motions with significant residual drifts recorded after the third, fourth, and fifth records in the sequence. The results showed that the updating procedure is able to incrementally (after each record) improve the accuracy of both the concrete and steel model parameters which also enhanced the estimates of the simulated peak and residual drifts. Finally, a sensitivity analysis is also carried out using an Extreme Gradient Boosting (XGBoost) machine learning model to assess the relative influence of the model parameters on the peak and residual drifts of the bridge pier.