Integrating multivariate techniques in bridge management systems for life-cycle prediction

2016-01-01

Hanley, Ciarán; Matos, José C.; Kelliher, Denis; Pakrashi, Vikram

Bridge management systems; Multivariate analysis; Asset management; Condition assessment

The use of bridge management systems (BMS) by infrastructure stakeholders has led to the collection andretention of large quantities of data concerning the condition states of bridges throughout national and regional networks. Thedatabase for the BMS is often populated by the results of routine visual inspections, based on a prescribed scale for defining thecondition state of the bridge’s individual elements, and of the bridge structure as a whole. The populating of the database alsoleads to the storage of large quantities of so-called metadata; which can describe the physical parameters of the bridge. Theavailability of this data allows the assessment of the BMS using multivariate techniques to enhance the life-cycle assessment ofbridge networks, through advanced descriptive and predictive techniques applied to deteriorating network assets. Multivariatetechniques such as principal component analysis have been demonstrated by the authors to be effectively applied as a descriptivetool to an existing BMS, and the results of a case study of a large dataset of bridges indicate its viability to be integrated intodata-based approaches to infrastructural asset management.