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Research Summary
Enhanced life-cycle modelling of bridges in the UK rail network with a focus on intervention and maintenance effectiveness
Supervisors: Dr Luis Neves, Prof Richard Wilkinson, Prof John Andrews
The condition of bridges and their components is the result of two key processes: deterioration and maintenance. To effectively maintain any network, asset managers typically use life-cycle models to forecast the condition of bridges and bridge components, enabling effective allocation of resources to maximise condition and safety and minimise risk. Accurate modelling requires detailed understanding of both processes. However, data on interventions and their impact is often sparse and incomplete, leading to inaccurate estimations of the impact of maintenance work. This makes it difficult to separate the contribution of the two processes to the condition record, leaving artefacts of undetected interventions and causing slower predictions for deterioration rates.
In this research, a novel approach is being undertaken to model the behaviour of defects on bridge components through progression of deterioration; inspection; decisions on the application of maintenance work; and repair and replacement activities. Using Coloured Petri Nets to model defects on a portfolio of bridges, the model simulates real-world behaviour and practices taken by Network Rail, who own and manage over 26 000 railway bridges in the UK.
This work uses extensive real condition data and limited intervention records to calibrate deterioration rates, and draws upon detailed industry expert knowledge to replicate the processes and logic taken by Network Rail in applying maintenance work, to create a comprehensive forecasting model. The project aims to output deterioration rates with improved accuracy than the current approach, and a detailed understanding of intervention processes and effectiveness.
Past Research
Enhancing the life cycle modelling capabilities of railway bridges by separating the effect of deterioration and maintenance activity. Using Bayesian Networks and Petri Net models to simulate the intervention and deterioration processes, aiming to provide improved accuracy and detect instances of historic interventions. Sponsored by Network Rail.