Vehicle-Bourne Autonomous Railroad Bridge Impairment Detection Systems
| University | Texas A&M University (TAMU) |
| Principal Investigators | Gary Fry, Ph.D., P.E., Civil Engineering (PI) |
| PI Contact Information | Center for Railway Research Texas A&M Transportation Institute 3135 TAMU College Station, TX 77843-3135 Office (979) 458-5544 garyfry@tamu.edu a-pelton@tti.tamu.edu |
| Funding Source(s) and Amounts Provided (by each agency or organization) | Federal Funds (USDOT UTC Program): $102,176 Cost Share Funds (TAMU): $33,040 |
| Total Project Cost | $135,216 |
| Agency ID or Contract Number | DTRT13-G-UTC59 |
| Start and End Dates | January 2014 — December 2016 |
| Brief Description of Research Project | Timber bridges constitute approximately 30% of current railroad bridge inventories in North America. Inspections of these structures usually comprise visual assessments of the condition of individual, observable components of the bridge. Special inspections may call for the field personnel to observe a bridge under load: that is, while a train is crossing. When creating a prioritized list of critical bridges to be replaced or repaired, there is advantage to having complementary measurements of bridge structural behavior under load. The objective of the proposed research program is to develop technology that will facilitate detecting structural impairments in timber railroad bridges using data gathered from rail vehicles that cross the bridges. Such a capability would represent a significant improvement over the current approaches used to maintain timber railroad bridges. The underlying logic behind using rail vehicle measurements to determine bridge fitness can be summarized as follows:
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| Keywords | bridge structural behavior, impairment detection, structural impairment, timber railroad bridges, rail vehicle measurements, bridge motions, railcar-bridge system, ADRIS, neural networks, dynamic response |
| Describe Implementation of Research Outcomes (or why not implemented) Place Any Photos Here | An open deck timber trestle railway bridge in service, was chosen for field testing. Two 15-foot spans were instrumented and monitored to observe the bridge’s behavior under live loading. String potentiometers were used to measure the total and net mid-span stringer deflections of the test spans. The locations of the string potentiometers on the two spans are shown in Figure 1. The devices located in the center of the stringers record the total mid-span stringer deflection. The net stringer deflection is found by subtracting the average of the two end string potentiometer values of a stringer from the total deflection. The net deflection is an additional measurement that takes the settlement of the supports into account. Figure shows two rows of string potentiometers attached to the underside of the bridge during a test. In addition to the instrumentation attached to the bridge, the ends of four axles of a train were instrumented with accelerometers to capture the vertical accelerations of the associated wheels. The locations of the wheel paths with respect to the rest of the work train are shown in Figure 3. Wheel path 1 was the first axle to enter a test span, and wheel path 4 was the last to exit. Data was collected at speeds ranging from 10 mph to 50 mph with increasing increments of 10 mph. The objective of the project was to train a neural network-based data processing system to estimate the mid-span deflection of bridge spans given only the axle-end accelerations from a train crossing the bridge. Figure 4 shows a comparison of the experimentally measured span deflections to those predicted blindly from the trained neural network processing system. All figures, pictures and tables can be accessed on Full Report link. |
| Impacts/Benefits of Implementation (actual, not anticipated) | Using as input data, the vertical accelerations of the axle ends of a moving railcar, the proposed system for timber railroad bridge deflection detection is confirmed experimentally to predict the maximum mid-span deflection to within a 10% error. So far, this project has resulted in two journal manuscripts under revision for submission. In addition, a Ph.D. dissertation is under final revision and will be defended in May 2017.
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| Report | http://utrgv.edu/railwaysafety/_files/documents/research/infrastructure/railroad-bridge-impairment-detection-systems.pdf |
| Project Website | http://utrgv.edu/railwaysafety/research/infrastructure/railroad-bridge-impairment-detection-systems/index.htm |