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Title:Autonomous wireless smart sensor for monitoring of railroad bridges
Author(s):Hoang, Tu
Director of Research:Spencer, Billie F
Doctoral Committee Chair(s):Spencer, Billie F
Doctoral Committee Member(s):Agha, Gul A; Fahnestock, Larry A; Dyke, Shirley J
Department / Program:Civil & Environmental Eng
Discipline:Civil Engineering
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):Wireless
Sensor
Railroad
Bridges
Monitoring
Autonomous
End-to-end
Abstract:One of the most critical components of the US transportation system is railroads, accommodating transportation for 48% of the nation’s total modal tonnage. Despite such vital importance, more than half of the railroad bridges, an essential component of railroad infrastructure in maintaining the flow of the network, were built before 1920; as a result, bridges comprise one of the most fragile components of the railroad system. Current structural inspection practice does not ensure sufficient information for both short- and long-term condition assessment, while keeping the operation cost sufficiently low for mandatory annual inspection. This research focuses on developing an autonomous, affordable system for monitoring railroad bridges using wireless smart sensor (WSS), so that a complete end-to-end monitoring solution can provide relevant information directly to the bridge owners. The system’s central feature is to capture the train-crossing event efficiently and eliminate the need for a human-in-the-loop for remote data retrieval and post-processing. An adaptive strategy combining an event-based scheme, scheduled rendezvous framework, and preemptive multitasking operation is implemented in the proposed system. The robust monitoring strategy is designed to detect and monitor both train-crossing and impact events. The wireless system addresses the challenges of remote data retrieval by integrating 4G-LTE functionality into the sensor network and completes the data pipeline with a cloud-based data management and visualization solution. This system is realized on the hardware, software, and framework levels. To demonstrate the efficacy of this system, data-driven anomaly detection algorithms are adopted to analyze full-scale monitoring results to reveal both short- and long-term changes. By overcoming the challenges of monitoring railroad bridges wirelessly and autonomously, this system is expected to become an essential tool for bridge engineers and decision-makers.
Issue Date:2021-07-15
Type:Thesis
URI:http://hdl.handle.net/2142/113194
Rights Information:Copyright 2021 Tu Hoang
Date Available in IDEALS:2022-01-12
Date Deposited:2021-08


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