Civil and Environmental Engineering
Date of this Version
Railway bridges are an essential component of any modern society and play a significant transportation role. During the 19th and early 20th centuries steel truss and plate girder bridges were commonly used railway designs, with most of the trusses constructed using pin and eyebar systems and most other load carrying members being riveted, built-up sections.
Many of these bridges are still in use and are subjected to increased railway traffic intensity, loads and speeds. The current practice for evaluating the integrity of bridges in the United States, irrespective of use, is primarily via visual inspection, with those inspections occurring at a maximum prescribed frequency of one calendar year for railway bridges (Agdas et al., 2015; AREMA, 2015; Hearn, 2007; ODOT, 2017; Roach et al., 2012).
Based on observed condition and structure importance, each railway bridge could be inspected one, two or four times annually. While this method has reliably maintained railway bridge condition, it is intermittent, costly, and subject to human interpretation. To improve how condition is assessed, some railway bridges have been outfitted with traditional, voltage based, sensors, such as strain gages and accelerometers that quantify their response. These projects have largely focused on isolated, large bridges, not on a group of bridges, and have involved an extensive array of these sensors, an approach that is also costly labor intensive.
The work discussed herein summarizes initial steps of a large-scale effort focused on developing a cost-effective, optimized, robust health monitoring system that takes advantage of repeatable patterns found on most, if not all, riveted steel railway bridges and, as a result, can be broadly utilized on a railway system. Initial work completed for this project encompassed analytical model validation, sensitivity analyses, field testing and model calibration for one truss and one plate girder segment of a large railway bridge over a river crossing. This research developed and assessed a computationally-based methodology to optimize structural health monitoring (SHM) plans for this and other, similar bridges.
Other secondary findings of this work that address riveted steel bridges key behaviors observed from field testing and/or computational data are: (i) truss flooring system members may experience a higher variation in axial forces which was not addressed during the construction phase; (ii) exterior stringers, adjacent to the truss bottom chords, experience high axial tension while interior stringers experience almost negligible axial compression forces; (iii) floor beams experience high lateral bending stresses, varying between 45 and 60% of their vertical bending stress, at exterior stringer intersections; (vi) loose truss bottom chords eyebars and bottom laterals were subjected to high frequency, low amplitude stress cycles; and (v) lateral bracing connection failures could be captured from the significant reduction in the recorded strains. These insights into behavior, together with the sensitivity of damage detection to sensor placement, led to the proposed SHM plans.
As a result of the research that was completed, three structural health monitoring plans with varying number of sensors were proposed to detect deficiencies reported by the owner of the bridge. One of those proposed plans is currently deployed on a riveted steel railway bridge for continuous monitoring and evaluation of its efficiency. The deployed plan was selected initially over the other two plans because sensitivity analyses showed significant change in the monitored responses at the instrumented locations of this plan due to damage. Evaluating the other plans which contained lower number of sensors is planned via monitoring the bridge continuously in the future.
Advisor: Daniel Linzell
Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Master of Science, Major: Civil Engineering, Under the Supervision of Professor Daniel Linzell Lincoln, Nebraska May, 2018.
Copyright (c) 2018 Ahmed Rageh