Bridge Impact Detection and Classification using Artificial Neural Networks

Abstract

Low-clearance railroad bridges are susceptible to impacts from trucks that exceed the required clearance. These impacts can be detected by mounting instrumentation on the bridge, such as accelerometers and inclinometers, and analyzing data from these sensors. A train running over a bridge also generates a significant response, and the bridge responses produced by trains are not always dissimilar to those produced by vehicle impacts. Understanding whether an event is produced by a vehicle impact or a train is critical; a vehicle impact should be investigated, while a train crossing the bridge is part of a bridge’s regular operation. This paper presents a system of artificial neural networks capable of examining response time histories and determining if the response is a train or an impact. Event data obtained from sensor modules mounted on several diverse railroad bridges has been analyzed to identify key signal characteristics, train neural algorithms, and classify events. Signal characteristics of importance include time and frequency content and a signal center of mass metric. Combinations of these characteristics have been used to improve impact detection on specific bridges to 66%- 97% and reduce false positive rates to 0.09% - 1.82%.