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Thin soil layer detection by VisCPT and FEM simulations
Hryciw R.D.a, Jung Y.a, Susila E.b, Ibrahim A.c
a University of Michigan, United States
b Bandung Institute of Technology, Indonesia
c Mansoura University, Egypt
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]A comprehensive approach for locating, characterizing the dominant grain size and estimating the strength of thin anomalous layers in a cohesionless soil stratigraphy is presented. The approach utilizes the results of finite element simulations of cone penetration across thin layers of anomalous strength and identification of their location and thickness using the Vision Cone Penetrometer (VisCPT). Through developed image processing algorithms based on wavelet decomposition, the dominant grain size in soil images is obtained. FEM simulations utilizing adaptive remeshing reveal the changes in CPT tip resistance across thin layers. Field data confirms the VisCPT’s ability to detect thin layers that are often missed by the conventional CPT. © 2009 IOS Press.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Adaptive remeshing,Cohesionless soil,Cone penetration,Cone penetration tests,FEM simulations,Field data,Finite element simulations,Grain size,Image processing algorithm,In-situ testing,Site characterization,Soil images,Soil layer,Thin layers,Tip resistance,Vision cone penetrometers[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Cone penetration test,Finite element method,Grain size,Image processing,In-situ testing,Site characterization,Vision[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.3233/978-1-60750-031-5-1052[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]