Applications of data science and big data analytics in underground transportation infrastructure (UTI-UTC 02)

This project focuses on harnessing the power of data science, machine learning (ML), and big data analytics to enhance the construction, operation, and maintenance of underground transportation infrastructure (UTI). By collecting and processing large-scale datasets from tunneling projects—such as TBM performance data, geotechnical records, and operational logs—the research develops predictive models to assess ground conditions, detect anomalies, and forecast potential structural failures. Key objectives include refining data-driven methods for real-time TBM state prediction, designing algorithms to detect defects like cracks or rock incursions, and creating interactive visualization tools to support decision-making. The project emphasizes scalable ML architectures (e.g., deep learning, recurrent neural networks) to improve the resilience, safety, and cost-efficiency of UTI systems. Its outcome serves as a foundation for intelligent tunneling and infrastructure health monitoring frameworks in modern urban environments.

Language

• English

Project

• Status: Completed
• Funding: $417,208.00
• Contract Numbers:

  69A3551747118

• Sponsor Organizations:

  University Transportation Center for Underground Transportation Infrastructure

  Colorado School of Mines
  Golden, CO  United States  80401

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  Colorado School of Mines

  1500 Illinois St
  Golden, CO  United States  80401

  California State University, Los Angeles

  5151 State University Drive
  Los Angeles, California  United States  90032
• Performing Organizations:

  California State University, Los Angeles

  5151 State University Drive
  Los Angeles, California  United States  90032
• Principal Investigators:

  Pourhomayoun, Mohammad

• Start Date: 20170601
• Expected Completion Date: 20200501
• Actual Completion Date: 20200501
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Data analysis; Datasets; Excavation and tunneling; Machine learning; Predictive models; Structural health monitoring; Tunnel borers; Visualization
• Subject Areas: Bridges and other structures; Data and Information Technology; Geotechnology; Highways;

Filing Info

• Accession Number: 01954497
• Record Type: Research project
• Source Agency: University Transportation Center for Underground Transportation Infrastructure
• Contract Numbers: 69A3551747118
• Files: UTC, RIP
• Created Date: May 7 2025 7:00PM