Life cycle cost estimation for railway bridge maintenance

Railway bridges are long life assets that deteriorate with age, usage, environmental conditions and poor maintenance practices. A whole of life approach has demonstrated to be an effective and efficient management regime for these assets. However, failure to do so increases risk and bridges become increasingly costly to maintain at the required standards. Unfortunately, there have been limited studies into the development and application of whole of life bridge maintenance cost modelling to assist railway companies. Additionally, recent trends in railway deregulation and budget constraints within the operating companies have created further challenges to this problem. As a result, many railway companies have been reducing maintenance budgets over recent decades and focusing on short term economic rationalisation. However, despite these challenges, there remains an expectation by infrastructure owners and the wider community that bridges will operate above acceptable safety levels. Considering the above challenges and requirements, this study predominantly aims to reduce uncertainty and increase the accuracy of maintenance cost estimation for effective life cycle management. Analysis of maintenance intervention costs is included by employing subsystem and element information from the bridge asset register, condition reports, and an application of a maintenance intervention decision model using data from the respective databases. Consequently, a whole of life cost model is developed in this study. The model is built with the capability of generating multiple maintenance strategies for providing cost effective options and enhances the quality of whole of life bridge management decision process. Additionally, the model reduces the subjective inputs from the railway operator, bridge user and bridge maintainers. The maintenance intervention database stores maintenance actions applicable to relevant element condition states. The derived information is employed to estimate maintenance costs that are based on desired condition state, location, influencing factors, defect size, degradation rate and residual risk.