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Session S08 - Inverse Problems and Applications

Thursday, July 15, 16:00 ~ 16:30 UTC-3

New Inverse Problem Approach to Thin Soil Layer Identification Applications in Earthquake Engineering

Eileen Martin

Virginia Tech, USA   -   This email address is being protected from spambots. You need JavaScript enabled to view it.

A common field-testing method to characterize soil properties in geotechnical engineering is the cone penetration test (CPT), in which a probe with a conical-shaped tip is hydraulically pushed into the ground at a constant rate, and the tip resistance is recorded at depth intervals of 1 to 2 cm, typically. This results in a tip resistance depth profile. These tip resistance profiles might initially appear to be comprised of depth-specific in-situ measurements, but the observed profiles appear blurred when compared to the true layered soil stratigraphy. This is because the measured penetration resistance reflects a stress bulb that forms around the penetrometer tip as it advances and the stress bulb may extend into neighboring layers. This can reduce the ability to detect thin layers and directly impacts the accuracy of the predictions of earthquake liquefaction potential of the soil. Previous methods (including one prior inverse problem approach) to correct these tip resistance profiles to remove this blurring have, in some scenarios, had limited efficacy in the presence of thin layers or multiple layers. We posed this deblurring as an inverse problem constrained to the space of layered soil profiles (with a thickness and corrected tip resistance at each depth). We propose a new, efficient correction algorithm that incorporates new techniques for initial guess generation, particle swarm optimization, and the addition or removal of layers throughout the optimization process. We compare the performance of two objective functions with modifications of this algorithm, one of which tends to yield more accurate results but takes longer to converge. In addition to proposing and testing this new algorithm, we have released open-source code so others may apply and improve upon our methods. This work focuses on the inversion algorithm, but our analyses indicate further improvement may be possible with more accurate forward modeling procedures.

Joint work with Jon Cooper (Virginia Tech), Kaleigh Yost (Virginia Tech), Alba Yerro (Virginia Tech) and Russell A. Green (Virginia Tech).

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