Please use this identifier to cite or link to this item: http://oaps.umac.mo/handle/10692.1/338
Title: Improved Methods For Direct Estimation Of Shear-wave Velocity Profile From Surface Wave Dispersion Curve
Authors: JIANG, JIAN XIN(江健鑫)
Department: Department of Civil and Environmental Engineering
Faculty: Faculty of Science and Technology
Issue Date: 2024
Citation: JIANG, J. X. (2024). Improved Methods For Direct Estimation Of Shear-wave Velocity Profile From Surface Wave Dispersion Curve (Outstanding Academic Papers by Students (OAPS)). Retrieved from University of Macau, Outstanding Academic Papers by Students Repository.
Abstract: Shear wave velocity (Vs) profiling is essential for assessing seismic risks and soil stability in geotechnical engineering. Traditional inversion-based methods are computationally intensive and sensitive to data quality. This study refines Lin's Sequence-Deduction Method, introducing the Depth-Dependent κ Method and the Weight Method to achieve more accurate Vs profiles with reduced computational demands. The Depth-Dependent κ Method employs depth-specific κ values, significantly enhancing accuracy across different soil depths. In contrast, the Weight Method utilizes the normalized amplitude of Rayleigh wave vertical motion as a weighting factor, effectively aligning Vs profiles with both simulated and field sites, particularly advantageous for shallow to medium-depth profiles. Comparative analyses demonstrate that both methods surpass Lin's original approach in accuracy for shear wave velocity profiling with acceptable minor deviations in the dispersion curve. Further investigation into the depth influence of Rayleigh waves suggests that assumptions of one-half and 0.75 wavelengths are both effective, optimizing Vs estimation efficiency. These improvements indicate a promising direction for more reliable Vs profiling in geotechnical practice.
Instructor: Prof. Lok, Man Hoi
Programme: Bachelor of Science in Civil Engineering
URI: http://oaps.umac.mo/handle/10692.1/338
Appears in Collections:FST OAPS 2024



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