Automated Knowledge Graphs for Life-cycle Management of Coastal Bridge Networks [supporting dataset]

Details

• Creators:
  Cheng, Minghui ; Nanni, Anthony
• Corporate Creators:
  University of Miami ; CREATE - Coastal REsearch And Transportation Education
• 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:
  Bayesian Network Bridge Management Systems Coastal Zone Management Data Data Analysis Infrastructure Systems Knowledge Graph Life Cycle Analysis Networks Digital Twins Software Software Packages Structured Data System Digital Twin

  More +
• Publication/ Report Number:
  2023-1
• DOI:
  https://doi.org/10.5281/zenodo.15522594
• Resource Type:
  Dataset
• Right Statement:
  Open Access; https://creativecommons.org/licenses/by/4.0/
• Geographical Coverage:
  United States
• Corporate Publisher:
  CREATE - Coastal REsearch And Transportation Education
• Abstract:
  With the ability to harness the power of big data, the digital twin (DT) technology has been increasingly applied to the modeling and management of structures and infrastructure systems, such as buildings, bridges, and power distribution systems. Supporting these applications, an important family of methods are based on graphs. For DT applications in modeling and managing smart cities, a large-scale knowledge graph (KG) is needed to represent the complex relationships and model the urban infrastructure as a system of systems. To this end, this paper develops a conceptual framework Automated knowledge Graphs for Complex Systems (AutoGraCS). In contrast to existing KGs developed for DTs, AutoGraCS can support KGs accounting for statistical correlations and interdependencies within the complex systems. The established KGs from AutoGraCS can then be easily turned into Bayesian networks for probabilistic modeling and Bayesian analysis. Besides, AutoGraCS provides flexibility in support of users’ need to implement the ontology and rules when constructing the KG. With the user-defined ontology and rules, AutoGraCS can automatically generate a KG to represent a complex system consisting of multiple systems. The bridge network in Miami-Dade County is used as an illustrative example to generate a KG that integrates multiple layers of data from the bridge network, traffic monitoring facilities, and flood water watch stations.
  
  The total size of the zip file is 879.9 KB. The .csv, Comma Separated Value, file is a simple format that is designed for a database table and supported by many applications. The .csv file is often used for moving tabular data between two different computer programs, due to its open format. The most common software used to open .csv files are Microsoft Excel and RecordEditor, (for more information on .csv files and software, please visit https://www.file-extensions.org/csv-file-extension). The .py file extension is commonly used for files containing source code written in Python programming language. Python is a dynamic object-oriented programming language that can be used for many kinds of software development (for more information on .py files and software, please visit https://www.file-extensions.org/py-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). 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).
  
  
• Content Notes:
  GitHub Repository Note: This dataset also resulted in python code that has been made available in GitHub. Access the GitHub repository here: https://github.com/ZihengGENG/AutoGraCS
  
  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 2025-08-18. 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:
  
  Cheng, M., & Geng, Z. (2025). Computational results for: Automated knowledge graphs for complex systems (AutoGraCS): Applications to management of bridge networks. In Resilient Cities and Structures (Vol. 3, Number 4, pp. 95–106). Zenodo. https://doi.org/10.5281/zenodo.15522594
  
  
• Format:
  ZIP
• Funding:
  69A3552348330
• Collection(s):
  University Transportation Centers
• Main Document Checksum:
  urn:sha-512:63a3d3af2f46172bc85de87c60ff5ea1878167f006c58d66cb9b3169bb0eb5ce589c28ba3a808d1c2de6d496a579412bef7ea1d7204b1b6e3f3fa60643604c1f
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/86034/dot_86034_DS1.zip
• File Type:
  [ZIP - 860.93 KB ]