Abstract
Machine learning and statistical approaches have transformed the management of infrastructure systems such as water, energy and modern transport networks. Artificial Intelligence-based solutions allow asset owners to predict future performance and optimize maintenance routines through the use of historic performance and real-time sensor data. The industrial adoption of such methods has been limited in the management of bridges within aging transport networks. Predictive maintenance at bridge network level is particularly complex due to the considerable level of heterogeneity encompassed across various bridge types and functions. This paper reviews some of the main approaches in bridge predictive maintenance modeling and outlines the challenges in their adaptation to the future network-wide management of bridges. Survival analysis techniques have been successfully applied to predict outcomes from a homogenous data set, such as bridge deck condition. This paper considers the complexities of European road networks in terms of bridge type, function and age to present a novel application of survival analysis based on sparse data obtained from visual inspections. This research is focused on analyzing existing inspection information to establish data foundations, which will pave the way for big data utilization, and inform on key performance indicators for future network-wide structural health monitoring.
Highlights
The industrial application of Structural Health Monitoring (SHM) systems in the network-wide management of bridges on transport networks is broadly underutilized
A recent UK study [1] revealed that, with the exception of a few strategically important structures, SHM is currently only deployed on individual structures which have been identified as having significant defects
The steps necessary to overcome uncertainties in existing data are outlined with a view towards moving to an operational Bridge Management Systems (BMS) with Artificial Intelligence (AI)-based solutions, which can enable the integration on sensor systems for network-wide monitoring
Summary
The industrial application of Structural Health Monitoring (SHM) systems in the network-wide management of bridges on transport networks is broadly underutilized. Recent developments in Population-Based SHM (PBSHM) provide an opportunity for a shift change in the potential inclusion of sensor data for network level bridge assessment. In this case, data obtained from a population of structures can provide extra information and improve damage detection for each individual structure. A case study looking at the application of survival analysis to the Northern Ireland road network is presented This will lead into a discussion that illustrates the challenges of enabling more advanced AI-based solutions for the management of bridge assets in ageing infrastructures networks such as those in Europe and the UK. The steps necessary to overcome uncertainties in existing data are outlined with a view towards moving to an operational BMS with AI-based solutions, which can enable the integration on sensor systems for network-wide monitoring
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