Enhancing Transportation Safety with InSAR Land Subsidence Monitoring
Land subsidence is a gradual downward movement and deformation of the Earth's surface. It is driven by geophysical processes such as sediment compaction, tectonic activity, erosion, and human factors like excessive groundwater extraction, mining, and urban development. This study addresses the urgent need to quantify and mitigate the impacts of land subsidence on transportation infrastructure through an integrated approach utilizing Geographic Information Systems (GIS), Interferometric Synthetic Aperture Radar (InSAR), and the Analytic Hierarchy Process (AHP). Focusing on East Baton Rouge Parish, Louisiana, the research examines areas prone to subsidence from 2017 to 2020, specifically targeting critical infrastructure such as Interstate 10, Interstate 12, and major bridges over the Mississippi River. Using a multi-criteria decision analysis framework through AHP, the study systematically prioritizes factors contributing to subsidence, including soil composition, land use/land cover, groundwater extraction rates, and slope stability, leading to the development of detailed susceptibility maps. Integrating machine learning algorithms further enhances the predictive accuracy of risk assessments and infrastructure planning. The following tasks will be performed to achieve the objectives of this study: Task 1: preprocess high-resolution Sentinel-1 SAR datasets for InSAR analysis, which generates detailed deformation fields through interferometric processing and time-series analysis. Task 2: apply AHP to assign weights to various subsidence drivers. Task 3: integrate spatial datasets within GIS to create risk maps. Task 4: validate the risk maps using ground truth data from global navigation satellite system observations and historical subsidence records. Task 5: perform temporal analysis of subsidence trends to forecast future deformation patterns, enabling the development of proactive intervention strategies. Task 6: report and share the results. The outcomes of this study include practical susceptibility maps and predictive models, offering valuable insights for transportation and urban planning stakeholders. These tools enhance infrastructure resilience by aiding in maintenance prioritization, optimizing land use, and informing policy decisions, ultimately supporting sustainable development by addressing subsidence risks, ensuring the long-term safety and efficiency of transportation networks, and advancing geospatial and remote sensing methodologies for land deformation studies.
- Record URL:
Language
- English
Project
- Status: Active
- Funding: $73,501.00
-
Contract Numbers:
69A3552348306
CY2-LSU-05
-
Sponsor Organizations:
Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC United States 20590 -
Managing Organizations:
Southern Plains Transportation Center
University of Oklahoma
202 W Boyd St, Room 213A
Norman, OK United States 73019 -
Project Managers:
Dunn, Denise
-
Performing Organizations:
Louisiana State University, Baton Rouge
P.O. Box 94245, Capitol Station
Baton Rouge, LA United States 70803 -
Principal Investigators:
Abdalla, Ahmed
Voyiadjis, George
- Start Date: 20241001
- Expected Completion Date: 20250930
- Actual Completion Date: 0
- USDOT Program: University Transportation Centers Program
Subject/Index Terms
- TRT Terms: Analytic hierarchy process; Bridges; Geographic information systems; Highways; Interferometry; Machine learning; Mapping; Predictive models; Risk assessment; Subsidence (Geology)
- Geographic Terms: Louisiana
- Subject Areas: Bridges and other structures; Geotechnology; Highways; Maintenance and Preservation; Planning and Forecasting;
Filing Info
- Accession Number: 01940485
- Record Type: Research project
- Source Agency: Southern Plains Transportation Center
- Contract Numbers: 69A3552348306, CY2-LSU-05
- Files: UTC, RIP
- Created Date: Dec 20 2024 7:40PM