Automated Detection and Analysis of Minor Deformations in Flat Walls Due to Railway Vibrations Using LiDAR and Machine Learning

• URL: http://arxiv.org/abs/2501.06457v3
• Date: Sat, 18 Jan 2025 02:06:33 GMT
• Title: Automated Detection and Analysis of Minor Deformations in Flat Walls Due to Railway Vibrations Using LiDAR and Machine Learning
• Authors: Surjo Dey, Ankit Sharma, Hritu Raj, Susham Biswas,
• Abstract summary: This study introduces an advanced methodology for automatically identifying minor deformations in flat walls caused by vibrations from nearby railway tracks.<n>It leverages high-density Terrestrial Laser Scanner (TLS) LiDAR surveys and AI/ML techniques to collect and analyze data.<n>The analysis focuses on identifying sections along flat walls and estimating their deformations relative to the ground orientation.
• Score: 0.34779962034842477
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study introduces an advanced methodology for automatically identifying minor deformations in flat walls caused by vibrations from nearby railway tracks. It leverages high-density Terrestrial Laser Scanner (TLS) LiDAR surveys and AI/ML techniques to collect and analyze data. The scan data is processed into a detailed point cloud, which is segmented to distinguish ground points, trees, buildings, and other objects. The analysis focuses on identifying sections along flat walls and estimating their deformations relative to the ground orientation. Findings from the study, conducted at the RGIPT campus, reveal significant deformations in walls close to the railway corridor, with the highest deformations ranging from 7 to 8 cm and an average of 3 to 4 cm. In contrast, walls further from the corridor show negligible deformations. The developed automated process for feature extraction and deformation monitoring demonstrates potential for structural health monitoring. By integrating LiDAR data with machine learning, the methodology provides an efficient system for identifying and analyzing structural deformations, highlighting the importance of continuous monitoring for ensuring structural integrity and public safety in urban infrastructure. This approach represents a substantial advancement in automated feature extraction and deformation analysis, contributing to more effective management of urban infrastructure.

Related papers

• Automatic Drywall Analysis for Progress Tracking and Quality Control in Construction [3.6350564275444186]
  Digitalization in the construction industry has become essential, enabling centralized, easy access to all relevant information of a building. This paper presents a method for image-based automated drywall analysis enabling construction progress and quality assessment through on-site camera systems.
  arXiv  Detail & Related papers  (2025-03-05T11:49:32Z)
• Localized Gaussians as Self-Attention Weights for Point Clouds Correspondence [92.07601770031236]
  We investigate semantically meaningful patterns in the attention heads of an encoder-only Transformer architecture. We find that fixing the attention weights not only accelerates the training process but also enhances the stability of the optimization.
  arXiv  Detail & Related papers  (2024-09-20T07:41:47Z)
• Machine Learning for Pre/Post Flight UAV Rotor Defect Detection Using Vibration Analysis [54.550658461477106]
  Unmanned Aerial Vehicles (UAVs) will be critical infrastructural components of future smart cities. In order to operate efficiently, UAV reliability must be ensured by constant monitoring for faults and failures. This paper leverages signal processing and Machine Learning methods to analyze the data of a comprehensive vibrational analysis to determine the presence of rotor blade defects.
  arXiv  Detail & Related papers  (2024-04-24T13:50:27Z)
• Exploring Challenges in Deep Learning of Single-Station Ground Motion Records [0.0]
  Experimental results reveal a strong reliance on the highly correlated P and S phase arrival information. Our observations highlight a potential gap in the field, indicating an absence of robust methodologies for deep learning of single-station ground motion recordings.
  arXiv  Detail & Related papers  (2024-03-12T11:56:50Z)
• Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features [69.47588461101925]
  We propose a method to adapt 3D object detectors to new driving environments. Our approach enhances LiDAR-based detection models using spatial quantized historical features. Experiments on real-world datasets demonstrate significant improvements.
  arXiv  Detail & Related papers  (2023-09-21T15:00:31Z)
• 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)
• Towards Scale Consistent Monocular Visual Odometry by Learning from the Virtual World [83.36195426897768]
  We propose VRVO, a novel framework for retrieving the absolute scale from virtual data. We first train a scale-aware disparity network using both monocular real images and stereo virtual data. The resulting scale-consistent disparities are then integrated with a direct VO system.
  arXiv  Detail & Related papers  (2022-03-11T01:51:54Z)
• Robust Self-Supervised LiDAR Odometry via Representative Structure Discovery and 3D Inherent Error Modeling [67.75095378830694]
  We develop a two-stage odometry estimation network, where we obtain the ego-motion by estimating a set of sub-region transformations. In this paper, we aim to alleviate the influence of unreliable structures in training, inference and mapping phases. Our two-frame odometry outperforms the previous state of the arts by 16%/12% in terms of translational/rotational errors.
  arXiv  Detail & Related papers  (2022-02-27T12:52:27Z)
• Analysis for full face mechanical behaviors through spatial deduction model with real-time monitoring data [21.131001656350712]
  The spatial tunnel structure is divided into many parts and reconstructed in a form of matrix. The external load applied on structure in the field was considered to study the mechanical behaviors of tunnel. A double-driven model was developed to obtain the full-faced information.
  arXiv  Detail & Related papers  (2021-09-27T16:28:21Z)
• Online structural health monitoring by model order reduction and deep learning algorithms [0.17499351967216337]
  We propose a simulation-based classification strategy to move towards online damage localization. The proposed strategy has been validated by means of two case studies, concerning a 2D portal frame and a 3D portal frame railway bridge.
  arXiv  Detail & Related papers  (2021-03-26T08:40:41Z)
• RGB-D Railway Platform Monitoring and Scene Understanding for Enhanced Passenger Safety [3.4298729855744026]
  This paper proposes a flexible analysis scheme to detect and track humans on a ground plane. We consider multiple combinations within a set of RGB- and depth-based detection and tracking modalities. Results indicate that the combined use of depth-based spatial information and learned representations yields substantially enhanced detection and tracking accuracies.
  arXiv  Detail & Related papers  (2021-02-23T14:44:34Z)
• Deep Learning Framework for Detecting Ground Deformation in the Built Environment using Satellite InSAR data [7.503635457124339]
  We adapt a pre-trained convolutional neural network (CNN) to detect deformation in a national-scale velocity field. We focus on the UK where previously identified deformation is associated with coal-mining, ground water withdrawal, landslides and tunnelling. The results demonstrate the potential applicability of the proposed framework to the development of automated ground motion analysis systems.
  arXiv  Detail & Related papers  (2020-05-07T03:14:00Z)

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.