Center for Transformative Infrastructure Preservation and Sustainability

Project Details

Title:
Subsurface Seismic Imaging Using Full-Waveform Inversion and Physics-Informed Neural Networks
Principal Investigators:
Kami Mohammadi
University:
University of Utah
Status:
Active
Year:
2024
Grant #:
69A3552348308 (IIJA / BIL)
Project #:
CTIPS-009
RiP #:
Keywords:
elastic waves, geotechnical engineering, highway maintenance, image processing, neural networks, subsidence (geology)

Abstract

Roadway subsidence presents a significant challenge in the maintenance and safety of transportation infrastructure. This localized downward movement of the ground surface is largely due to buried low-velocity anomalies, such as highly compressible soft clay or loose sand zones, voids, and abandoned mine workings. Subsidence not only compromises the integrity of the road surface but also poses a considerable risk to the safety of the traveling. The ability to effectively assess and address this geohazard is, therefore, a crucial aspect of transportation system management. The early identification of subsurface anomalies is key to mitigating risks associated with roadway subsidence. By detecting potential hazards before they manifest as surface deformations, remedial actions can be undertaken to prevent extensive damage or catastrophic collapse of the roadway. This proactive approach to roadway maintenance ensures the continuous safety and efficiency of transportation routes, thereby minimizing disruptions and potential hazards to the public. The overall objective of this research is to integrate Physics-Informed Neural Networks with full-waveform inversion to solve the elastic wave equation in heterogeneous geomaterials and invert subsurface low-velocity anomalies.

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