Probabilistic Prediction on Seismic Responses of a Long-Span High-Speed Railway Bridge Using BPINN

Abstract

Efficient probabilistic prediction of seismic responses is crucial for assessing the seismic-resistant capability of long-span continuous girder high-speed railway bridges. Thus, a Bayesian physics-informed neural network (BPINN) is adopted to rapidly and effectively predict the probabilistic seismic responses of such bridges. The BPINN model combines deep learning and physics to improve the accuracy and consistency of predictions, while also quantifying the uncertainties using Bayesian inference methods. Various seismic excitations, including pulse, far-field and near-field types, are employed to probabilistically predict the seismic responses of the top of bridge pier. Evaluation metrics, including mean squared error and prediction interval coverage probability, are used to assess the deterministic and probabilistic estimates of BPINN. Results demonstrate that BPINN performs better in deterministic results for far-/near-field ground motions compared to pulse-like earthquakes, with most cases exhibiting a close approximation to the 95% confidence interval. The flexibility and adaptability of BPINN in handling different types of ground motions can provide valuable insights for assessing the seismic performance of such structures.