Automated Knowledge Graphs for Life-cycle Management of Coastal Bridge Networks

Cheng, Minghui; Nanni, Anthony; Zulesza, Stacey; Coastal Research and Education Actions for Transportation Equity (CREATE) Tier-1 University Transportation Center (UTC); University of Miami

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Automated Knowledge Graphs for Life-cycle Management of Coastal Bridge Networks

2024-07-01
By Cheng, Minghui ; Nanni, Anthony ; Zulesza, Stacey

[PDF-1020.32 KB]

English

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Creators:

Cheng, Minghui ; Nanni, Anthony ; Zulesza, Stacey

Cheng, Minghui ; Nanni, Anthony ; Zulesza, Stacey Less -
Corporate Creators:

Coastal Research and Education Actions for Transportation Equity (CREATE) Tier-1 University Transportation Center (UTC) ; University of Miami

Coastal Research and Education Actions for Transportation Equity (CREATE) Tier-1 University Transportation Center (UTC) ; University of Miami Less -
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

United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology Less -
Subject/TRT Terms:

[+]

Bridge Management Systems Coastal Zone Management Data Analysis Life Cycle Analysis
Publication/ Report Number:

2023-1
Resource Type:

Tech Report
Geographical Coverage:

United States
Edition:

Final Project Report 10/01/2023-7/15/2024
Corporate Publisher:

Coastal Research and Education Actions for Transportation Equity (CREATE) Tier-1 University Transportation Center (UTC)
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.
Format:

PDF
Funding:

69A3552348330
Collection(s):

University Transportation Centers
Main Document Checksum:

[+]

urn:sha-512:f7a23ffef9f6710b7497868ff7b4f2d38a9c4dd2a6d130b5801e025eca23296c9b770115fb413ec67f29a24aec8584afb3dc7721a1a285b9480ecbbe6a4f653b
Download URL:

https://staging-dot.cdc.gov/view/dot/82617/dot_82617_DS1.pdf
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Automated Knowledge Graphs for Life-cycle Management of Coastal Bridge Networks

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