Using data to explore trends in bridge performance

Abstract

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.

Developing alternative business models for smart infrastructure: a UK case study

“…This paper proposes a novel methodology for identifying potential value-generating opportunities associated with an infrastructure business, and, as evidenced in this journal, the opportunities to enhance the value of infrastructure by making it 'smart' are considerablefor example, when considering bridge maintenance operations (Bennetts et al, 2018) or seeking to improve the capacity of transport infrastructure (Hillel et al, 2018). Essentially, the methodology starts with the business that is Developing alternative business models for smart infrastructure: a UK case study Bouch, Rogers, Powell and Horsfall created by an infrastructure developmentin this case TEP, which is referred to as an energy centreand then goes through a structured search of Internet-based literature to identify potential value opportunities that can be used in the development of its business model.…”

Analysis of visual inspection data for a sample of highway bridges in the UK

“…This paper focusses primarily on the qualitative findings of a series of visual inspections and investigations on a representative sample of Highways England's stock. The detailed statistical analysis of Highways England's asset information data, including the production of 'importance dendrograms', has already been reported by Bennetts et al (2018aBennetts et al ( , 2018b. The previous work has focussed on variation of the Bridge Condition Indicator scores (BCIave and BCIcrit) which aim to give a quantitative measure of bridge condition.…”

Assessing the suitability of bridge-scour-monitoring devices

“…While the presence or progression of a scour hole (i.e., near a pier) may be monitored, the concomitant effects on the bridge structure itself are not usually captured by scour SHM systems (in other words an additional SHM system would be needed to study the bridge condition) (Prendergast et al 2018). Bennetts et al (2018a) studied network level changes in bridge condition indicators (which are based on visual inspection data) showing that key visual inspection-based metrics can detect trends such as structural deterioration at stock (regional) level. However, such metrics are much less reliable for assessing performance of an individual structure (Bennetts et al 2018b).…”

“…At network level monitoring data is very useful when aggregated in openly accessible databases which can be used in both reliability-based and risk-based bridge management (cf. Bennetts et al 2018aBennetts et al , 2018bPregnolato 2019;Pregnolato et al, 2021). Breysse (2012) highlighted the importance of 'collapse databases' for use in forensic engineering and risk management efforts.…”