An Event-Classification Neural Network Approach for Rapid Railroad Bridge Impact Detection

Omobolaji Lawal, Shaik Althaf V. Shajihan, Kirill Mechitov, Billie F. Spencer

Research output: Contribution to journalArticlepeer-review

Abstract

Railroads are a critical part of the United States’ transportation sector. Over 40 percent (by weight) of the nation’s freight is transported by rail, and according to the Bureau of Transportation statistics, railroads moved $186.5 billion of freight in 2021. A vital part of the freight network is railroad bridges, with a good number being low-clearance bridges that are prone to impacts from over-height vehicles; such impacts can cause damage to the bridge and lead to unwanted interruption in its usage. Therefore, the detection of impacts from over-height vehicles is critical for the safe operation and maintenance of railroad bridges. While some previous studies have been published regarding bridge impact detection, most approaches utilize more expensive wired sensors, as well as relying on simple threshold-based detection. The challenge is that the use of vibration thresholds may not accurately distinguish between impacts and other events, such as a common train crossing. In this paper, a machine learning approach is developed for accurate impact detection using event-triggered wireless sensors. The neural network is trained with key features which are extracted from event responses collected from two instrumented railroad bridges. The trained model classifies events as impacts, train crossings, or other events. An average classification accuracy of 98.67% is obtained from cross-validation, while the false positive rate is minimal. Finally, a framework for edge classification of events is also proposed and demonstrated using an edge device.
Original languageEnglish (US)
Article number3330
JournalSensors
Volume23
Issue number6
DOIs
StatePublished - Mar 22 2023

Keywords

  • impact detection
  • event classification
  • railroad bridge
  • wireless sensors
  • artificial neural networks

ASJC Scopus subject areas

  • Analytical Chemistry
  • Information Systems
  • Instrumentation
  • Atomic and Molecular Physics, and Optics
  • Electrical and Electronic Engineering
  • Biochemistry

Fingerprint

Dive into the research topics of 'An Event-Classification Neural Network Approach for Rapid Railroad Bridge Impact Detection'. Together they form a unique fingerprint.

Cite this