Visual inspection is the most common form of condition monitoring used by bridge owners. Information derived from visual inspection data is commonly used to indicate the performance of bridge stocks and inform bridge management decisions. However, several studies have highlighted that the inherently subjective nature of the methods used to record this data can result in uncertainty, due to differences between different inspectors' perceptions of the severity and extent of defects. It is important for asset managers to understand the nature of this uncertainty and the implications for decision making. This paper reports the results of a study which compared scoring of bridge defects by pairs of independent inspectors across 200 bridge structures on England's strategic road network. A sample of 200 structures was selected to be representative of Highways England's stock with regard to, inter alia, age, condition and structural form. Routine Principal Inspections for these sample structures, undertaken every six years by the relevant maintaining agents, were also attended by inspectors from WSP Ltd, with defects scored independently by each inspector. The results of these comparisons were used to derive an empirical profile of the uncertainty in different individual defect severity and extent scores. Statistical methods were then used to derive empirical probability density functions for the values of bridge and stock level condition metrics according to the widely adopted Bridge Condition Indicator system. The reported results highlight trends in the reliability of individual defect scores and the impact of uncertainty on commonly used performance metrics.
Asset management organisations collect large quantities of data on the inventory, condition and maintenance of their bridge structures. A key objective in the collection of these asset data is that these can be processed into useful information that can inform best practice for the design of new structures and the management of existing stocks. As a leading bridge asset owner, Highways England, UK, is applying insights from mining of its asset data to contribute to continual improvement in the management of structures and its understanding of their performance. This paper presents the application of modern data science tools and optimal decision tree learning to Highways England’s asset information database comprising bridge inventory, inspection records and historic and current defects for its stock of thousands of bridges. Trends are observed in the factors affecting the current condition of bridges and their rate of deterioration. Optimal decision trees are used to identify the most influential factors in the performance of bridge structures and present complex multifactor trends in a format readily digested by managers and decision makers, to inform standards and policy.
The Strategic Road Network (SRN) in England carries 33% of traffic in England and Highways England's bridge management systems play a crucial role in the maintenance of infrastructure assets along the SRN. Reinforced concrete half-joint structures are susceptible to deterioration and hard to inspect, hence they require special attention within management programmes.Inspection data relating to half-joint structures on the SRN was gathered. Within this portfolio, 252 structures with half-joint related defects were interrogated to classify the most common defects and identify any potential shortcomings in current inspection practice. Common half-joint defects were related to corrosion and cracking. Clear correlations were shown to exist between defect classes, emphasising the need for quality control and proper workmanship during construction.A revised inspection methodology for half-joint structures that provides more comprehensive information about crack details, and a greater alignment between defect information and indicators of structural measures, is proposed. A decision-tree approach is used to overcome some of the shortcomings of subjective classifications of the defect cause. Concurrently, knowledge of the zonal crack location, crack orientation and crack severity helps inform the decision-making about the structural condition. There is scope for use of the methodology in conjunction with automated processing procedures to identify half-joint structures with particular defect characteristics and profiles. In this way, asset managers will be better able to allocate resources to structures with an increased risk of failure.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.