2011Structure and Infrastructure EngineeringRequires access

A risk and criticality-based approach to bridge performance data collection and monitoring

Simon Bush, Piotr Omenzetter, Theunis F. P. Henning, P McCarten

Open publisher page 12 citations

Abstract

Bridges constitute a substantial and vital part of any transport infrastructure system. The functionality of the system is therefore reliant on the asset management strategies adopted for its bridges. To understand and manage bridge performance, data of appropriate quality has to be collected in a cost-efficient way and closely linked to the decisions made with it. To ensure a coherent approach, a data collection strategy for asset management of road bridges has been developed in this study. In the strategy, a combined risk and criticality-based approach is used to prioritise each bridge and to assign it to one of three data collection levels comprising core, intermediate and advanced. For each collection level, the strategy provides guidance on the type of data to collect, the accuracy and precision required in the data collection process, the frequency of inspections, and the recommended collection techniques to be used, including visual inspection, non-destructive evaluation and structural health monitoring. The strategy will help asset managers to develop a deeper level of knowledge on the bridges that have the greatest impact on the system functionality and to collect data of the required quality needed for the decisions they make.

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What this paper is about

Bridges constitute a substantial and vital part of any transport infrastructure system. The functionality of the system is therefore reliant on the asset management strategies adopted for its bridges. To understand and manage bridge performance, data of appropriate quality has to be collected in a cost-efficient way and closely linked to the decisions made with it. To ensure a coherent approach, a data collection strategy for asset management of road bridges has been developed in this study. In the strategy, a combined risk and criticality-based approach is used to prioritise each bridge and to assign it to one of three data collection levels comprising core, intermediate and advanced. For each collection level, the strategy provides guidance on the type of data to collect, the accuracy and precision required in the data collection process, the frequency of inspections, and the recommended collection techniques to be used, including visual inspection, non-destructive evaluation and structural health monitoring. The strategy will help asset managers to develop a deeper level of knowledge on the bridges that have the greatest impact on the system functionality and to collect data of the required quality needed for the decisions they make.

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Available abstract

Bridges constitute a substantial and vital part of any transport infrastructure system. The functionality of the system is therefore reliant on the asset management strategies adopted for its bridges. To understand and manage bridge performance, data of appropriate quality has to be collected in a cost-efficient way and closely linked to the decisions made with it. To ensure a coherent approach, a data collection strategy for asset management of road bridges has been developed in this study. In the strategy, a combined risk and criticality-based approach is used to prioritise each bridge and to assign it to one of three data collection levels comprising core, intermediate and advanced. For each collection level, the strategy provides guidance on the type of data to collect, the accuracy and precision required in the data collection process, the frequency of inspections, and the recommended collection techniques to be used, including visual inspection, non-destructive evaluation and structural health monitoring. The strategy will help asset managers to develop a deeper level of knowledge on the bridges that have the greatest impact on the system functionality and to collect data of the required quality needed for the decisions they make.

Key concepts: Data collection, Bridge (graph theory), Process (computing), Asset management, Asset (computer security), Risk analysis (engineering), Computer science, Data quality

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