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Title: Risk-based inspection planning for bridge networks
Author: Saenthan, Sathananthan
ISNI:       0000 0004 2697 0582
Awarding Body: University of Surrey
Current Institution: University of Surrey
Date of Award: 2010
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Many bridge authorities around the world carry out bridge inspections at regular fixed intervals, without explicitly considering issues associated with risk. In large bridge networks, this policy could result in unnecessary commitment of resources on more reliable bridges, while some vulnerable bridges may reach risk levels that are substantially higher than what is normally acceptable. Risk-based inspection (RBI) planning has the potential to provide consistent risk levels in a cost effective manner and has been deployed successfully in other industry sectors. The aim of this project is to develop, implement and validate an RBI planning methodology for a large network of bridges. In a large network, it is neither feasible nor necessary to analyse each bridge individually. Hence, a risk ranking strategy, which starts by subdividing the network into a number of groups and subgroups, is developed. A qualitative risk scoring system that serves as an initial screening tool for inspection prioritisation is then proposed. The inspection intervals for these bridge subgroups can be specified using condition index as the relevant performance indicator. A novel deterioration model using Dynamic Bayesian Network (DBN), which links individual bridge element conditions to bridge group condition and then establishes the change in bridge group condition with time, is developed to obtain the required condition index deterioration profiles. The implementation and applicability of the above models and methods have been demonstrated using sample data from the UK bridge stock. The proposed risk ranking strategy helps to classify bridges according to the risk levels in a systematic and practical manner. The group risk scores are used to define appropriate inspection intervals, and a case study shows this method to be both accurate and realistic. In addition, the proposed risk ranking strategy is generic and can be adapted for other structure networks. Furthermore, it is shown how, in the absence of actual data, expert knowledge can be introduced in the DBN model and utilised to achieve a consistent evaluation of a bridge group condition. The DBN deterioration model is shown to be an efficient tool for ‘what-if analysis. The case studies revealed that the inspection intervals, which are currently fixed at 6 years, may be varied between 2 to 18 years, without compromising the average risk in the network.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available