Physics-Informed Neural Network based Damage Identification for Truss Railroad Bridges

• URL: http://arxiv.org/abs/2502.00194v1
• Date: Fri, 31 Jan 2025 22:22:35 GMT
• Title: Physics-Informed Neural Network based Damage Identification for Truss Railroad Bridges
• Authors: Althaf Shajihan, Kirill Mechitov, Girish Chowdhary, Billie F. Spencer Jr,
• Abstract summary: Railroad bridges are a crucial component of the U.S. freight rail system, which moves over 40 percent of the nation's freight. This study proposes a physics-informed neural network (PINN) based approach for damage identification in steel truss railroad bridges.
• Score: 4.923031976899536
• License:
• Abstract: Railroad bridges are a crucial component of the U.S. freight rail system, which moves over 40 percent of the nation's freight and plays a critical role in the economy. However, aging bridge infrastructure and increasing train traffic pose significant safety hazards and risk service disruptions. The U.S. rail network includes over 100,000 railroad bridges, averaging one every 1.4 miles of track, with steel bridges comprising over 50% of the network's total bridge length. Early identification and assessment of damage in these bridges remain challenging tasks. This study proposes a physics-informed neural network (PINN) based approach for damage identification in steel truss railroad bridges. The proposed approach employs an unsupervised learning approach, eliminating the need for large datasets typically required by supervised methods. The approach utilizes train wheel load data and bridge response during train crossing events as inputs for damage identification. The PINN model explicitly incorporates the governing differential equations of the linear time-varying (LTV) bridge-train system. Herein, this model employs a recurrent neural network (RNN) based architecture incorporating a custom Runge-Kutta (RK) integrator cell, designed for gradient-based learning. The proposed approach updates the bridge finite element model while also quantifying damage severity and localizing the affected structural members. A case study on the Calumet Bridge in Chicago, Illinois, with simulated damage scenarios, is used to demonstrate the model's effectiveness in identifying damage while maintaining low false-positive rates. Furthermore, the damage identification pipeline is designed to seamlessly integrate prior knowledge from inspections and drone surveys, also enabling context-aware updating and assessment of bridge's condition.

Related papers

• Intelligent Railroad Grade Crossing: Leveraging Semantic Segmentation and Object Detection for Enhanced Safety [0.11999555634662631]
  Crashes and delays at Railroad Highway Grade Crossings (RHGC) pose significant safety concerns for the U.S. Federal Railroad Administration (FRA) This study introduces an intelligent system that leverages machine learning and computer vision techniques to enhance safety at RHGC.
  arXiv  Detail & Related papers  (2024-03-17T02:15:15Z)
• Deep Learning Overloaded Vehicle Identification for Long Span Bridges Based on Structural Health Monitoring Data [3.331125445667599]
  BWIM (bridge weigh-in-motion) method for overloaded vehicle identification is getting more popular. Deep learning based overloaded vehicle identification approach (DOVI) is proposed. Model evaluations are conducted on a simply supported beam and a long-span cable-stayed bridge under random traffic flow.
  arXiv  Detail & Related papers  (2023-09-04T13:24:54Z)
• Object-Size-Driven Design of Convolutional Neural Networks: Virtual Axle Detection based on Raw Data [0.0]
  This study presents a novel approach for real-time detection of train axles using sensors arbitrarily placed on bridges. The developed Virtual Axle Detector with Enhanced Receptive Field (VADER) has been validated on a single-track railway bridge. Using raw data as input outperformed the state-of-the-art spectrogram-based method in both speed and memory usage by 99%.
  arXiv  Detail & Related papers  (2023-09-04T12:53:54Z)
• Wooden Sleeper Deterioration Detection for Rural Railway Prognostics Using Unsupervised Deeper FCDDs [0.0]
  In this study, we devised a prognostic discriminator pipeline to automate one-class damage classification using the deeper FCDDs for defective railway components. We also performed ablation studies of the deeper backbone based on convolutional neural networks (CNNs) We demonstrated our application to railway inspection using a video acquisition dataset of railway track from backward view at a cloudy and sunny scene.
  arXiv  Detail & Related papers  (2023-05-09T00:16:49Z)
• Detecting train driveshaft damages using accelerometer signals and Differential Convolutional Neural Networks [67.60224656603823]
  This paper proposes the development of a railway axle condition monitoring system based on advanced 2D-Convolutional Neural Network (CNN) architectures. The resultant system converts the railway axle vibration signals into time-frequency domain representations, i.e., spectrograms, and, thus, trains a two-dimensional CNN to classify them depending on their cracks.
  arXiv  Detail & Related papers  (2022-11-15T15:04:06Z)
• Reinforcement Learning based Cyberattack Model for Adaptive Traffic Signal Controller in Connected Transportation Systems [61.39400591328625]
  In a connected transportation system, adaptive traffic signal controllers (ATSC) utilize real-time vehicle trajectory data received from vehicles to regulate green time. This wirelessly connected ATSC increases cyber-attack surfaces and increases their vulnerability to various cyber-attack modes. One such mode is a'sybil' attack in which an attacker creates fake vehicles in the network. An RL agent is trained to learn an optimal rate of sybil vehicle injection to create congestion for an approach(s)
  arXiv  Detail & Related papers  (2022-10-31T20:12:17Z)
• Federated Deep Learning Meets Autonomous Vehicle Perception: Design and Verification [168.67190934250868]
  Federated learning empowered connected autonomous vehicle (FLCAV) has been proposed. FLCAV preserves privacy while reducing communication and annotation costs. It is challenging to determine the network resources and road sensor poses for multi-stage training.
  arXiv  Detail & Related papers  (2022-06-03T23:55:45Z)
• Fault-Aware Design and Training to Enhance DNNs Reliability with Zero-Overhead [67.87678914831477]
  Deep Neural Networks (DNNs) enable a wide series of technological advancements. Recent findings indicate that transient hardware faults may corrupt the models prediction dramatically. In this work, we propose to tackle the reliability issue both at training and model design time.
  arXiv  Detail & Related papers  (2022-05-28T13:09:30Z)
• Do Neural Optimal Transport Solvers Work? A Continuous Wasserstein-2 Benchmark [133.46066694893318]
  We evaluate the performance of neural network-based solvers for optimal transport. We find that existing solvers do not recover optimal transport maps even though they perform well in downstream tasks.
  arXiv  Detail & Related papers  (2021-06-03T15:59:28Z)
• Knowledge transfer between bridges for drive-by monitoring using adversarial and multi-task learning [6.462702225377603]
  Monitoring bridge health using vibrations of drive-by vehicles has various benefits, such as low cost and no need for direct installation or on-site maintenance of equipment on the bridge. Many such approaches require labeled data from every bridge, which is expensive and time-consuming, if not impossible, to obtain. We introduce a transfer learning framework using domain-adversarial training and multi-task learning to detect, localize and quantify damage.
  arXiv  Detail & Related papers  (2020-06-05T19:18:45Z)
• RescueNet: Joint Building Segmentation and Damage Assessment from Satellite Imagery [83.49145695899388]
  RescueNet is a unified model that can simultaneously segment buildings and assess the damage levels to individual buildings and can be trained end-to-end. RescueNet is tested on the large scale and diverse xBD dataset and achieves significantly better building segmentation and damage classification performance than previous methods.
  arXiv  Detail & Related papers  (2020-04-15T19:52:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.