Railway bridge – Virtual City Model

The individual assignments undertaken by the author focused on the life-cycle assessment and maintenance optimisation of a railway bridge system, which served as a representative infrastructure component characterised by long service life, high structural criticality, and maintenance-induced service interruptions.

In the first individual assignment, a life-cycle assessment framework was developed for the railway bridge, including the definition of material inventories, emission factors, and environmental indicators such as energy consumption and greenhouse gas emissions. This work established the methodological basis for quantifying environmental impacts associated with infrastructure components over their service life.

The second individual assignment extended the analysis to failure behaviour and maintenance planning of the railway bridge. Bridge deterioration and failure behaviour were represented using established infrastructure performance modelling approaches, including Markov-based transition models and condition-dependent deterioration concepts [1,3,4]. Deterministic maintenance timelines were developed to represent inspection, minor maintenance, and major intervention events, consistent with common bridge performance prediction and maintenance modelling practices [1,5]. Performance indicators such as total interruption duration and distance between major interventions were defined. A Pareto-based evaluation approach was applied to identify trade-offs between maintenance availability and intervention regularity.

In the group project, the methodologies developed for the railway bridge were directly transferred and extended to a multi-component station system. The railway bridge, previously analysed as a standalone system, was incorporated as a critical subsystem within the integrated station model. The failure timeline and maintenance logic developed in the individual assignments informed the bridge component modelling in Chapter 3, while system interaction and dependency effects were represented using fault-tree-based deterioration modelling concepts [6,7]. The life-cycle assessment framework was further expanded to account for interactions with additional subsystems and coordinated maintenance strategies.

This progression from a single railway bridge system to an integrated station-level system demonstrates methodological continuity between the individual and group assignments. The individual work provided validated modelling approaches for failure representation, maintenance optimisation, and life-cycle impact assessment, which were subsequently scaled and integrated within the group project to address system-level decision-making under multiple competing objectives [2,5].

References
[1] Madanat, S., Mishalani, R., & Wan Ibrahim, W. H. (1995). Estimation of infrastructure transition probabilities from condition rating data. Journal of Infrastructure Systems, ASCE, 1(2), 120–125.
[2] Huang, Y. (2010). Artificial neural network model of bridge deterioration. Journal of Performance of Constructed Facilities, 24(6).
[3] Jiang, Y., Saito, M., & Sinha, K. C. (1988). Bridge performance prediction model using the Markov chain. Transportation Research Record 1180, Transportation Research Board, Washington, D.C.
[4] Morcous, G. (2006). Performance prediction of bridge deck systems using Markov chains. Journal of Performance of Constructed Facilities, 20.
[5] Wellalage, N. K. W., Zhang, T., Dwight, R., & El-Akruti, K. (2015). Bridge deterioration modeling by Markov Chain Monte Carlo (MCMC) simulation method.
[6] Sianipar, P. R. M., & Adams, T. M. (1997). Fault-tree model of bridge element deterioration due to interaction. Journal of Infrastructure Systems.
[7] LeBeau, K. H., & Wadia-Fascetti, S. J. Fault Tree Analysis of Schoharie Creek Bridge.