Fast evaluation method of post-impact performance of bridges based on dynamic load test data using Gaussian process regression

Abstract

Bridges occasionally suffer from the vehicle or ship collision accidents, leading to structural damage and bridge collapse, resulting in severe consequences such as casualties, ship sinking, and vehicle damage. After such accidents, the performance evaluation of bridge structures is significant for bridge maintenance. The bridge's structural performance should be assessed after a collision with a vehicle or ship before regular traffic is resumed. A gray correlation analysis technique was introduced for the swift and efficient assessment of bridge structural performance following impacts. This method aimed to identify the influential parameters associated with bridge structural performance. Utilizing outcomes from dynamic load tests along with the Gaussian process regression model, adjustments were made to the original finite element analysis model. This refinement facilitated precise scrutiny of structural damage and expedited accurate performance evaluations of the bridge. Subsequently, a practical examination was carried out following a ship collision with the Wanjiang Bridge to validate the viability and precision of the proposed approach. A comparison between performance evaluation outcomes derived from the bridge's structural response to ship collision and actual field test results demonstrated the substantial accuracy and computational efficacy of the suggested technique. The proposed method uses a dynamic load test combined with an intelligent algorithm to replace the static load test, effectively solving the expensive, time-consuming, traffic-impeding static load test problem.