Design and field implementation of an impact detection system using committees of neural networks.

Highlights • Features are extracted from raw bridge acceleration data from 8 bridges to provide network training data; no FE models are necessary for training. • Multiple neural network voting ensemble configurations are presented. • Impact detection ranges from 91 −100% while average false positive rates are 0.00–0.75%. Abstract Many critical societal functions depend on uninterrupted service of civil engineering infrastructure. Railroads represent important infrastructure components of the transportation sector and provide both passenger and freight services. Railroad bridges over roadways are susceptible to impacts from overheight vehicles and equipment, which may damage bridge girders or supports and must be investigated after each event. One method of monitoring for vehicle-bridge collisions utilizes accelerometers to monitor for abnormal bridge vibrations corresponding to abnormal activity. Passing trains under normal operating conditions frequently produce significant bridge responses that have similar response characteristics to bridge strikes, but do not need to be investigated. This paper presents an expert system which comprises committees of artificial neural networks trained to interrogate data collected from accelerometers mounted on the bridge, assess the nature of the acceleration signal, and classify the event as either a passing train or a potentially damaging impact. This system is trained using acceleration time histories from accelerometers installed on 8 low-clearance rail bridges; no finite element model simulations were used for network training or data stream creation. The presented system accurately detects and classifies impacts with average impact detection performance ranging from 91–100% with average false positive rates limited to 0.00–0.75%. [ABSTRACT FROM AUTHOR]