Structural Health Monitoring-Based Bridge Lifecycle Extension: Survival Analysis and Monte Carlo-Based Quantification of Value of Information

Abstract

A key goal of structural health monitoring (SHM) systems applied to infrastructure is to improve asset management. SHM systems yield benefits by providing information that allows improved asset management decisions. Often, improvement is measured in monetary terms, whereby lower expenses are sought. The value of information (VoI) is often evaluated through the quantification of the incremental benefit, resulting from the information provided by the SHM system. The VoI can be considered as having two components: value derived from the improved operation of the infrastructure and value derived from increased useful life. This work focuses on the latter source of value in the context of concrete decks in US highway bridges. To estimate the lifecycle extension potential and the connected VoI, we need to simulate bridge deck condition degradation over time to support a discounted cash flow analysis of bridge replacement cost. We accomplish this by utilizing a neural network-based survival analysis combined with Monte Carlo simulation. We present a case study using the developed methods. We have chosen to study the southbound portion of the bridge on the US Highway 202, located in Wayne, NJ. The selected bridge is a representative concrete highway overpass, the type of which there are large numbers in the US. The case study demonstrates the applicability of the methods developed for the general evaluation of the VoI obtained via SHM. The results are encouraging for the widespread use of SHM for lifecycle extension purposes; the potential value in such applications is large.