Photogrammetry and LiDAR-Based Precast Railroad Crossties Abrasion Damage Detections [supporting dataset]

Details

• Creators:
  Guan, Shanyue ; Sun,Chao
• Corporate Creators:
  Purdue University. School of Construction Management Technology
• Corporate Contributors:
  Transportation Infrastructure Precast Innovation Center (TRANS-IPIC) Tier-1 University Transportation Center (UTC) ; 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:
  Abrasion Flaw Detection Laser Radar Photogrammetry Precast Concrete Railroad Ties Railroad Tracks
• Publication/ Report Number:
  PU-23-RP-04
• DOI:
  https://doi.org/10.21949/1eqg-jh21
• Resource Type:
  Dataset
• Right Statement:
  Open Access; https://creativecommons.org/licenses/by/4.0/
• Geographical Coverage:
  Ohio;United States
• Corporate Publisher:
  Transportation Infrastructure Precast Innovation Center (TRANS-IPIC) Tier-1 University Transportation Center (UTC)
• Abstract:
  Recent derailment accident that happened in East Palestine, Ohio has drawn huge public attention to railroad system safety. While this accident is under investigation, one of the major contributions to many other derailment accidents is the precast concrete crossties abrasion damage. Concrete crossties can lose concrete sections on portions of the tie bottom and sides during service. Identifying the abrasion damage of precast concrete crossties is critical to extend the railroad service life and prevent potential derailment. This project is a collaboration among Purdue University, Louisiana State University, Rocla Concrete Tie, and CSX. The ultimate goal of this research is to develop mitigation measures to reduce concrete railroad tie section loss at the ballast interface based on expected service life. As a first step to achieve this goal, this project develops a photogrammetry and LiDAR scanning-based precast concrete crossties abrasion damage detection system.
  
  The total size of the zip file is 322 MB. The .jpg file extension is associated with JPEG (Joint Photographic Experts Group) file format. JPEG is a lossy image compression algorithm that significantly reduces the file size of the original image at the cost of quality. The higher the compression ratio the lower the quality of the .jpg file (for more information on .jpg files and software, please visit https://www.file-extensions.org/jpg-file-extension). The file extension .md is among others related to texts and source codes in Markdown markup language. Markdown is a lightweight markup language, to write using an easy-to-read, easy-to-write plain text format, then convert it to structurally valid XHTML (or HTML) (for more information on .md files and software, please visit https://www.file-extensions.org/md-file-extension). File extension .json is associated to JavaScript Object Notation file format, a lightweight, text-based, language-independent data interchange format. JSON defines a small set of formatting rules for the portable representation of structured data. It is used by various applications as alternative option to XML file format. The data in a json file are stored in simple text file format and the content is viewable in any simple text editor (for more information on .json files and software, please visit https://www.file-extensions.org/json-file-extension). The .txt file type is a common text file, which can be opened with a basic text editor. The most common software used to open .txt files are Microsoft Windows Notepad, Sublime Text, Atom, and TextEdit (for more information on .txt files and software, please visit https://www.file-extensions.org/txt-file-extension).
  
  
• Content Notes:
  National Transportation Library (NTL) Curation Note: This dataset has been curated to CoreTrustSeal's curation level "A. Active Preservation". To find out more information on CoreTrustSeal's curation levels, please consult their "Curation & Preservation Levels" CoreTrustSeal Discussion Paper" (https://doi.org/10.5281/zenodo.11476980). NTL staff last accessed this dataset at its repository URL on 2025-03-19. If, in the future, you have trouble accessing this dataset, please email NTLDataCurator@dot.gov describing your problem. NTL staff will do its best to assist you at that time.
  
  Public Access Note: This item is made available under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license https://creativecommons.org/licenses/by/4.0/. Use the following citation:
  
  Guan, Shanyue and Chao Sun (2025). Photogrammetry and LiDAR-Based Precast Railroad Crossties Abrasion Damage Detections [supporting dataset]. Transportation Infrastructure Precast Innovation Center (TRANS-IPIC) Tier-1 University Transportation Center https://doi.org/10.21949/1eqg-jh21
  
  
• Format:
  ZIP
• Funding:
  69A3552348333
• Collection(s):
  University Transportation Centers
• Main Document Checksum:
  urn:sha-512:c0fede0aca943d7332b1e4977bec8e9045f887e3fa9c40ec7dbe7f47c15a0ba16494c3e12ac7ed034d649d169cf079513a63aba554a6d6415d90efc72da8991b
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/81793/dot_81793_DS1.md
• File Type:
  [md - 7.02 KB ]