Study of Tunnel-Induced Ground Settlement Using Integrated Machine Learning and Remote Sensing Techniques (UTI-UTC 35)

This research aims to develop an integrated framework combining machine learning (ML) and remote sensing techniques to study and predict ground settlement resulting from tunnel excavation activities. By leveraging Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) data and time-series analysis, the project monitors surface deformations with high spatial and temporal resolution in urban environments. ML models—including deep learning and physics-informed networks—are trained using geotechnical parameters and tunneling records to estimate settlement behavior both during and after tunnel construction. The framework enhances understanding of settlement mechanisms, enables early warning of potential hazards, and supports safer design and construction of underground infrastructure. The project also contributes to the development of robust predictive tools for urban planners and engineers, ultimately improving the resilience and sustainability of tunnel systems.

Language

• English

Project

• Status: Completed
• Funding: $281,897.00
• 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: 20190901
• Expected Completion Date: 20220430
• Actual Completion Date: 20220501
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Ground settlement; Machine learning; Predictive models; Remote sensing; Time series analysis; Tunneling; Urban areas
• Subject Areas: Bridges and other structures; Data and Information Technology; Geotechnology; Highways;

Filing Info

• Accession Number: 01954475
• Record Type: Research project
• Source Agency: University Transportation Center for Underground Transportation Infrastructure
• Contract Numbers: 69A3551747118
• Files: UTC, RIP
• Created Date: May 7 2025 5:01PM