Framework for dynamic risk assessment in subsurface excavation projects (UTI-UTC 20)

This project aims to develop a comprehensive framework for dynamic risk assessment tailored to subsurface excavation projects, particularly in urban environments where uncertainty in ground conditions, equipment behavior, and construction activities poses significant challenges. The framework integrates real-time monitoring data, expert judgment, and probabilistic modeling techniques to continuously assess and update the risk profile as excavation progresses. By leveraging machine learning and Bayesian updating principles, the system captures evolving project conditions and provides predictive insights into potential hazards, such as face instability, excessive ground settlement, or equipment malfunction. The objective is to improve decision-making processes in tunneling operations, enabling proactive responses to emerging risks and enhancing the safety, efficiency, and resilience of underground construction. The framework supports adaptive planning and aligns with best practices in risk-informed project management.

Language

• English

Project

• Status: Completed
• Funding: $45,104.00
• 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
• Performing Organizations:

  Colorado School of Mines

  1500 Illinois St
  Golden, CO  United States  80401
• Principal Investigators:

  Zhou, Wendy

  Gutierrez , Marte

• Start Date: 20170501
• Expected Completion Date: 20190430
• Actual Completion Date: 20190501
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Decision support systems; Excavation and tunneling; Machine learning; Risk assessment; Urban areas
• Subject Areas: Bridges and other structures; Data and Information Technology; Geotechnology; Highways;

Filing Info

• Accession Number: 01954488
• Record Type: Research project
• Source Agency: University Transportation Center for Underground Transportation Infrastructure
• Contract Numbers: 69A3551747118
• Files: UTC, RIP
• Created Date: May 7 2025 6:08PM