• International Journal of Highway Engineering
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• v.11 no.1
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• pp.165-175
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• 2009

With the current move towards adopting mechanistic-empirical concepts in the design of pavement structures, state-of-the-art mechanistic analysis methodologies are needed to determine accurate pavement responses, such as stress, strain, and deformation. Previous laboratory studies of pavement foundation geomaterials, i.e., unbound granular materials used in base/subbase layers and fine-grained soils of a prepared subgrade, have shown that the resilient responses followed by nonlinear, stress-dependent behavior under repeated wheel loading. This nonlinear behavior is commonly characterized by stress-dependent resilient modulus material models that need to be incorporated into finite element (FE) based mechanistic pavement analysis methods to predict more realistically predict pavement responses for a mechanistic pavement analysis. Developed user material subroutine using aforementioned resilient model with nonlinear solution technique and convergence scheme with proven performance were successfully employed in general-purpose FE program, ABAQUS. This numerical analysis was investigated in predicted critical responses and domain selection with specific mesh generation was implemented to evaluate better prediction of pavement responses. Results obtained from both axisymmetric and three-dimensional (3D) nonlinear FE analyses were compared and remarkable findings were described for nonlinear FE analysis. The UMAT subroutine performance was also validated with the instrumented full scale pavement test section study results from the Federal Aviation Administration's National Airport Pavement Test Facility (FAA's NAPTF).

• Journal of IKEEE
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• v.18 no.2
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• pp.272-281
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• 2014

In electrical impedance tomography (EIT), modified Newton Raphson (mNR) method is widely used inverse algorithm for static image reconstruction due to its convergence speed and estimation accuracy. The unknown conductivity distribution is estimated iteratively by minimizing a cost functional such that the residual error namely the difference in measured and calculated voltages is reduced. Although, mNR method has good estimation performance, EIT inverse problem still suffers from ill-conditioned and ill-posedness nature. To mitigate the ill-posedness, generally, regularization methods are adopted. The inverse solution is highly dependent on the choice of regularization parameter. In most cases, the regularization parameter has a constant value and is chosen based on experience or trail and error approach. In situations, when the internal distribution changes or with high measurement noise, the solution does not get converged with the use of constant regularization parameter. Therefore, in this paper, in order to improve the image reconstruction performance, we propose a new scheme to determine the regularization parameter. The regularization parameter is computed based on residual error and updated every iteration. The proposed scheme is tested with numerical simulations and laboratory phantom experiments. The results show an improved reconstruction performance when using the proposed regularization scheme as compared to constant regularization scheme.

• Geophysics and Geophysical Exploration
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• v.25 no.4
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• pp.214-216
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• 2022

Distributed acoustic sensing (DAS), an increasingly growing acquisition technique in the oil and gas exploration and seismology fields, has been used to record seismic signals using optical cables as receivers. With the development of imaging methods for DAS data, full waveform inversion (FWI) is been applied to DAS data to obtain high-resolution property models such as P- and S-velocity. However, because the DAS systems measure strain from the phase distortion between two points along optical cables, DAS data must be transformed from strain to particle velocity for FWI algorithms. In this study, a plane-wave FWI algorithm based on the relationship between strain and horizontal particle velocity in the plane-wave assumption is proposed to apply FWI to DAS data. Under the plane-wave assumption, strain equals the horizontal particle velocity, which is scaled by the velocity at the receiver position. This relationship was confirmed using a numerical experiment. Furthermore, 4-layer and modified Marmousi-2 velocity models were used to verify the applicability of the proposed FWI algorithm in various survey environments. The proposed FWI was implemented in land and marine survey environments and provided high-resolution P- and S-velocity models.