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Title: Multilevel Genetic Algorithm–Based Acoustic–Elastodynamic Imaging of Coupled Fluid–Solid Media to Detect an Underground Cavity
This work studies the feasibility of imaging a coupled fluid-solid system by using the elastodynamic and acoustic waves initiated from the top surface of a computational domain. A one-dimensional system, where a fluid layer is surrounded by two solid layers, is considered. The bottom solid layer is truncated by using a wave-absorbing boundary condition (WABC). The wave responses are measured on a sensor located on the top surface, and the measured signal contains information about the underlying physical system. By using the measured wave responses, the elastic moduli of the solid layers and the depths of the interfaces between the solid and fluid layers are identified. To this end, a multi-level Genetic Algorithm (GA) combined with a frequency- continuation scheme to invert for the values of sought-for parameters is employed. The numerical results show the following findings. First, the depths of solid-fluid interfaces and elastic moduli can be reconstructed by the presented method. Second, the frequency-continuation scheme improves the convergence of the estimated values of parameters toward their targeted values. Lastly, a preliminary inversion, using an all- solid model, can be employed to identify if a fluid layer is presented in the model by showing one layer with a very large value of Young's modulus (with a similar value to that of the bulk modulus of water) and the value of mass density being similar to that of water. Then, the primary GA inversion method, based on a fluid-solid model, can be utilized to adjust the soil characteristics and fine-tune the locations of the fluid layer. If this work is extended to a 3D setting, it can be instrumental to finding unknown locations of fluid-filled voids in geological formations that can lead to ground instability and/or collapse (e.g., natural/anthropogenic sinkhole, urban cave-in subsidence, etc.).  more » « less
Award ID(s):
2053694 2044887
NSF-PAR ID:
10470903
Author(s) / Creator(s):
; ;
Publisher / Repository:
ASCE
Date Published:
Journal Name:
Journal of Computing in Civil Engineering
Volume:
37
Issue:
1
ISSN:
0887-3801
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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