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.

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A fibre-reinforced polymer (FRP) cycle footbridge has been proposed for construction in Bristol, United Kingdom for South Gloucestershire Council. The superstructure will span 54m, comprising a bowstring carbon fibre-reinforced polymer (CFRP) arch with a 5m wide glass fibre-reinforced polymer (GFRP) deck supported by stainless steel hangers. Recently, a methodology has been proposed that provides a structured process to assess the value of a structural health monitoring (SHM) system for a bridge prior to deployment. This methodology outputs a simple metric that quantifies the likeliness of an SHM system to yield value to an asset owner. This FRP bridge is used as a case-study to 'road test' this process. Two possible systems were considered: a system of accelerometers and a system of strain gauges. From the resulting discussions, a deployment of accelerometers received a value-rating (VR) of 4.2. A strain gauge deployment received 3.7. The scores will contribute to a monitoring specification for the FRP bridge which is currently in the design phase. Expansions to the methodology have also been proposed to better capture the potential value of an SHM system which would be of interest to structural engineers and researchers, in particular to inform model validation and research activities.

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.