Framework for risk-based management and safety of railroad bridge infrastructure using wireless smart sensors (WSS)

To increase overall profitability, add capacity to rail operations to meet projected needs, and comply with new federal regulations on bridge safety, North American railroads are exploring means and methods to improve the management of their bridge networks. Current maintenance, repair, and replacement (MRR) decisions are informed by bridge inspections and ratings. Inspection and rating practices recommend observing the response of bridges under revenue traffic. However, an objective relationship between bridge responses and the impact to railroad operations has yet to be established. Moreover, measuring responses while trains are on the bridge can be quite challenging and sometimes may not be possible. As a result, current MRR decisions are not optimal and in general conservative, prioritizing safety to overcome the uncertainty of consequences of inaction. If the consequences of MRR decisions could be better determined, then the railroads could more effectively allocate their limited resources. This dissertation addresses this issue by developing an approach for consequence-based management of bridge networks, adopted from the field of seismic risk assessment, for making MRR decisions on a network-wide basis. The proposed framework assesses bridge service state condition based on fragility relations. Fragility curves are developed relating bridge responses under revenue service traffic to service condition limit states. Additionally, this research conducted specific Structural Health Monitoring (SHM) campaigns for railroad bridges employing Wireless Smart Sensors (WSS). Wireless strain gages installed in the rail measured real-time trainloads and speeds, while wireless accelerometers and magnetic strain gages measured associated bridge responses. The sensing system was deployed and validated on multiple railroad bridges in North America under different types of traffic and capacity. The measured bridge data can be used to update periodically the fragilities to have more accurate estimates of the bridge condition. The expenses associated with these service conditions estimate the total costs of a given MRR policy. In this way, MRR decisions can be prioritized minimizing negative consequences to railroad operations. This framework provides a consistent approach for intelligent management of railroad bridges, and more specifically, for the prioritization of railroad bridge MRR decisions. Using this framework the rail owner can identify the most efficient use of a limited budget while maintaining safe railroad operations.