Risk assessment and prevention for typical railway bridge pier under rockfall impact

Abstract

Railway bridges in mountainous regions are susceptible to damage and collapse due to rockfall impacts, posing a risk to train operations. This research specifically focuses on risk assessment of a typical railway bridge pier considering correlations between the collision parameters. Firstly, a comprehensive analysis of rockfall trajectories is conducted using the tool ROCFALL. Statistical features and correlation of impact parameters at the pier location are obtained and further characterized based on functional statistical models. Additionally, a high-fidelity finite element (FE) model of a two-span simply supported box girder with three piers is developed using LS-DYNA. Multiple structural dynamic responses including impact force, pier displacement, and internal energy of the directly impacted pier, are evaluated for both general rockfalls and potential boulders. The study examines the effects of individual impact parameters on the internal energy and displacement responses at the pier top, highlighting their significance in relation to load-bearing capacity and operational safety. Machine learning techniques are utilized to perform a comprehensive evaluation of risk states, wherein an Artificial Neural Network (ANN) model is employed for accurate prediction of risk indexes, and the K-means algorithm is applied to classify pier risk levels into minor, moderate, and severe. Furthermore, Monte Carlo (MC) simulations are employed to calculate the probabilities associated with different risk levels. A composite multilayer prevention measure is suggested to enhance the performance of a rockfall-prone bridge. It is proven that this measure can rapidly reduce associated vibrations and effectively mitigate structural damage resulting from the collision.