Modelling the interactions between defect mechanisms on metal bridges

Calvert, G.; Neves, L.; Andrews, J.; Hamer, M.

Abstract

Bridge asset managers have finite resources at their disposal to minimise the risk of structural failure and ensure bridges are maintained to a suitable safety threshold. Any maintenance strategy must be efficient in its use of resources and deliver an optimal Whole Life Cycle Cost (WLCC). The calculation of an accurate WLCC is contingent on having an accurate deterioration model to predict future asset condition and sufficient performance indicators to appraise targeted maintenance strategies in a decision model. Typically predictive bridge deterioration models output a probability distribution for a single condition indicator over time. However, bridge deterioration is a diverse physical process with different degradation mechanisms. For example, metallic bridge elements may undergo corrosion and loss of coating or paintwork, as well as suffer from structural component failure modes such as buckling, permanent distortion, tearing and fracture. This paper presents a multi-defect approach to modelling bridge asset management. A multi-defect deterioration model is implemented using a Dynamic Bayesian Network (DBN). The model can predict the simultaneous progression of multiple bridge defects. The decision model can utilise the multiple condition indicators to apply the most appropriate maintenance intervention to provide an uplift in condition. The industrial data used in this research takes the format of a longitudinal study, which is common for many transportation agencies. The use of such data restricts the deterioration model to use a memoryless distribution which assumes a constant failure rate. However, bridge deterioration has been empirically shown to be a non-constant process. By modelling bridge deterioration as a combination of interacting defects, non-constant behaviour can be modelled, even when the model itself is parametrised using an exponential distribution. The paper presents a case study of the model calibrated using data from 13,569 metallic railway bridge girders in the United Kingdom.

Citation

Calvert, G., Neves, L., Andrews, J., & Hamer, M. (2021, April). Modelling the interactions between defect mechanisms on metal bridges. Presented at 10th International Conference on Bridge Maintenance, Safety and Management (IABMAS 2020), Sapporo, Japan

• Presentation Conference Type: Edited Proceedings
• Conference Name: 10th International Conference on Bridge Maintenance, Safety and Management (IABMAS 2020)
• Start Date: Apr 11, 2021
• End Date: Apr 15, 2021
• Acceptance Date: Dec 26, 2019
• Online Publication Date: Apr 20, 2021
• Publication Date: Apr 20, 2021
• Deposit Date: Mar 27, 2020
• Publicly Available Date: Apr 21, 2022
• Series Title: International Conference on Bridge Maintenance, Safety and Management
• Book Title: Bridge Maintenance, Safety, Management, Life-Cycle Sustainability and Innovations: Proceedings of the Tenth International Conference on Bridge Maintenance, Safety and Management (IABMAS 2020)
• ISBN: 9780367232788
• Public URL: https://nottingham-repository.worktribe.com/output/4209539
• Publisher URL: https://www.taylorfrancis.com/chapters/edit/10.1201/9780429279119-374/modelling-interactions-defect-mechanisms-metal-bridges-calvert-neves-andrews-hamer
• Related Public URLs: https://www.routledge.com/Bridge-Maintenance-Safety-Management-Life-Cycle-Sustainability-and-Innovations/Yokota-Frangopol/p/book/9780367232788
• Additional Information: Due to the pandemic the conference did not go ahead as planned on 28 June - 2 July 2020. It has been moved online and will now take place 11th – 15th April 2021.