Physics-informed neural network for damage identification in railroad bridges
Veluthedath Shajihan, Shaik Althaf
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https://hdl.handle.net/2142/124660
Description
Title
Physics-informed neural network for damage identification in railroad bridges
Author(s)
Veluthedath Shajihan, Shaik Althaf
Issue Date
2024-04-25
Director of Research (if dissertation) or Advisor (if thesis)
Chowdhary, Girish
Department of Study
Computer Science
Discipline
Civil Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Physics-informed Neural Network
Damage Identification
Structural Health Monitoring
Railroad Bridges
Language
eng
Abstract
Railroad bridges are a crucial component of the U.S. freight rail system which accounts for moving more than 40 percent of freight in the country, playing a critical role in the U.S. economy. The aging infrastructure coupled with the increasing train traffic poses a safety hazard and risks disruption of services. While identifying damages and performing holistic assessments of railroad bridges remain challenging tasks. This research proposes a physics-informed neural network (PINN) based approach for damage identification and model updating of truss railroad bridges. The proposed framework adopts an unsupervised learning method, leveraging train wheel loads and measured responses at bridge nodes as inputs. The PINN model explicitly incorporates the governing differential equations of system dynamics using a recurrent neural network (RNN) based architecture with a custom Runge-Kutta 4th order (RK-4) integrator cell. This approach enables the identification of damage ratios and localization of damaged members in the bridge. To validate the performance of the proposed approach, a case study is conducted on the Calumet bridge in Chicago, utilizing a simplified 2D model with simulated damage scenarios. The results demonstrate the model’s ability to accurately identify and quantify damage under various conditions while maintaining low false-positive rates. Furthermore, the proposed updating pipeline is designed to seamlessly incorporate prior knowledge gathered from site inspections and drone surveys, enabling a context-aware updating and assessment of bridge’s condition.
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