Quantifying uncertainty in visual inspection data

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.