Machine Learning Enabled Information Fusion of Heterogeneous Sensing for Infrastructure

Tang, Jiong; Zhang, Yang; Wang, Ting; Zhou, Qianyu

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Machine Learning Enabled Information Fusion of Heterogeneous Sensing for Infrastructure

2025-07-01
By Tang, Jiong ; Zhang, Yang ; Wang, Ting ; ...

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English

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Creators:

Tang, Jiong ; Zhang, Yang ; Wang, Ting ; Zhou, Qianyu
Corporate Creators:

University of Maine. Transportation Infrastructure Durability Center (TIDC) ; University of Connecticut
Corporate Contributors:

United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
Subject/TRT Terms:

Artificial Intelligence Detection And Identification Technologies Full-field Measurement Infrastructure Inverse Analysis Machine Learning Optimization Sensor Network Optimization Sensors Structural Damage Identification Structural Strengthening
Publication/ Report Number:

69A3551847101
Resource Type:

Tech Report
Geographical Coverage:

United States
Edition:

Final Report
Corporate Publisher:

University of Maine. Transportation Infrastructure Durability Center (TIDC)
Abstract:

In this research, we develop a framework for machine learning enabled information fusion for infrastructure health monitoring. Traditionally, off-the-shelf sensors such as accelerometers and strain gages have been used to collect real-time measurements of structural responses to facilitate health monitoring. While certain levels of successes have been achieved, they also exhibit limitations such as relatively low detection sensitivity to incipient damage and especially limited detection range and coverage. In this project, we 1) establish a benchmark testbed that incorporates various sensors to assess different sensing mechanisms; 2) develop a machine learning based approach that can leverage sensing information and extrapolate to full-field measurement; and 3) investigate scalability strategies that can result in actual implementation of the new framework. Potential applications are large-scale infrastructure such as bridges.
Format:

PDF
Collection(s):

University Transportation Centers
Main Document Checksum:

urn:sha-512:9ebce12756bd1170dd2956282038955da722a4a6aa981daf7a97561cf5532d562610cde9dd7238bc5cba020d72b51e50710543db19a7d975a9de5b65225963e7
Download URL:

https://rosap.ntl.bts.gov/view/dot/88739/dot_88739_DS1.pdf
File Type:

[PDF - 27.04 MB ]

File Language:

English

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