Future Load-Carrying Capacity of Ageing Railway Bridges under Heavy Axle Trains 

Research aims and scopes:

This project aims to develop an integrated framework for predicting the future load-carrying capacity of ageing railway bridges in the United Kingdom, with specific consideration of heavy axle weight (HAW) freight train as predominant operations. The research scope so far covers structural health monitoring, deterioration characterisation, material testing, numerical modelling, and probabilistic assessment. A key objective is to formulate and implement a Bridge Deterioration Equation (BDE) within a geospatial database application as a minimum viable product (MVP) to support infrastructure managers in assessing future bridge accessibility across the railway network.

Main body of work:

The work was undertaken in four stages. The first stage involved structural health monitoring of representative ageing railway bridges, recording dynamic properties, verify deterioration conditions, and conducting material testing. At this stage, Ziliang contributed to numerical modelling of metallic bridges to establish baseline structural behaviour and the preparation and testing of metallic coupon specimens. In the second stage, he was responsible for the development of the numerical simulations and probabilistic analyses, introducing a novel framework for estimating the current and future performance of ageing metallic bridges that adapts fragility-based methods from earthquake engineering. This work led to the formulation of BDE, which relates bridge accessibility (characterised in UK railways as the bridge Route Availability number) to bridge age and permissible train speed.

In the third stage, the BDE formulation was incorporated into a geospatial database web application as part of the MVP, to which Ziliang contributed to data preparation and validation. In the ongoing fourth stage, he participated in proposing an extension of the probabilistic framework to a route/network level using advanced digital twining or surrogate modelling approaches, combining physics-based and data-driven inputs to enable scalable assessment. Across the project, he contributed to conceptual development, prepared key numerical analyses, data curation, and produced original manuscripts and visualisations, including journal and conference submissions and refereed project reports.