Multi-defect modelling of bridge deterioration using truncated inspection records

Calvert, Gareth; Neves, Luis; Andrews, John; Hamer, Matthew

Abstract

Bridge Management Systems (BMS) are decision support tools that have gained widespread use across the transportation infrastructure management industry. The Whole Life Cycle Cost (WLCC) modelling in a BMS is typically composed of two main components: a deterioration model and a decision model. An accurate deterioration model is fundamental to any quality decision output.

There are examples of deterministic and stochastic models for predictive deterioration modelling in the literature, however the condition of a bridge in these models is considered as an ‘overall’ condition which is either the worst condition or some aggregation of all the defects present. This research proposes a predictive bridge deterioration model which computes deterioration profiles for several distinct deterioration mechanisms on a bridge.

The predictive deterioration model is composed of multiple Markov Chains, estimated using a method of maximum likelihood applied to panel data. The data available for all the defects types at each inspection is incomplete. As such, the proposed method considers that only the most significant defects are recorded, and inference is required regarding the less severe defects. A portfolio of 9,726 masonry railway bridges, with an average of 2.47 inspections per bridge, in the United Kingdom is the case study considered.

Citation

Calvert, G., Neves, L., Andrews, J., & Hamer, M. (2020). Multi-defect modelling of bridge deterioration using truncated inspection records. Reliability Engineering and System Safety, 200, Article 106962. https://doi.org/10.1016/j.ress.2020.106962

• Journal Article Type: Article
• Acceptance Date: Mar 23, 2020
• Online Publication Date: Mar 26, 2020
• Publication Date: 2020-08
• Deposit Date: Apr 1, 2020
• Publicly Available Date: Apr 30, 2020
• Journal: Reliability Engineering & System Safety
• Print ISSN: 0951-8320
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 200
• Article Number: 106962
• DOI: https://doi.org/10.1016/j.ress.2020.106962
• Keywords: Industrial and Manufacturing Engineering; Safety, Risk, Reliability and Quality
• Public URL: https://nottingham-repository.worktribe.com/output/4237110
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0951832019306015