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Driven by an increase in the scale of laboratory testing programs, the industry is looking for new methods of soil characterisation and ways to extract more benefit from existing data. In this paper, applications of Multi-Sensor Core Logging (MSCL) to site characterisation projects are discussed, highlighting the potential for providing a fast, quantitative assessment of recovered samples. Sensors of the MSCL include bulk density, P-wave velocity, natural gamma, electrical resistivity and magnetic susceptibility. Natural gamma is shown to be related to higher silt and clay content, acting as a useful parameter for soil classification and quantitative assessment of fines content. A comparison between MSCL and in-situ CPTu data reveals correlations between natural gamma and friction ratio, normalised soil behaviour index, Ic, and net pore pressure response. By collecting MSCL data on all samples at the beginning of a lab program, one can obtain a full overview of the material available for testing, leading to more strategic sample selection and a reduced overall volume of testing. Correlations with MSCL parameters and geotechnical properties can be used to predict continuous profiles, and be extended to in-situ measurements from borehole logging and CPT
 
Driven by an increase in the scale of laboratory testing programs, the industry is looking for new methods of soil characterisation and ways to extract more benefit from existing data. In this paper, applications of Multi-Sensor Core Logging (MSCL) to site characterisation projects are discussed, highlighting the potential for providing a fast, quantitative assessment of recovered samples. Sensors of the MSCL include bulk density, P-wave velocity, natural gamma, electrical resistivity and magnetic susceptibility. Natural gamma is shown to be related to higher silt and clay content, acting as a useful parameter for soil classification and quantitative assessment of fines content. A comparison between MSCL and in-situ CPTu data reveals correlations between natural gamma and friction ratio, normalised soil behaviour index, Ic, and net pore pressure response. By collecting MSCL data on all samples at the beginning of a lab program, one can obtain a full overview of the material available for testing, leading to more strategic sample selection and a reduced overall volume of testing. Correlations with MSCL parameters and geotechnical properties can be used to predict continuous profiles, and be extended to in-situ measurements from borehole logging and CPT
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== Full Paper ==
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<pdf>Media:Draft_Sanchez Pinedo_441707639213.pdf</pdf>

Revision as of 12:01, 7 June 2024

Abstract

Driven by an increase in the scale of laboratory testing programs, the industry is looking for new methods of soil characterisation and ways to extract more benefit from existing data. In this paper, applications of Multi-Sensor Core Logging (MSCL) to site characterisation projects are discussed, highlighting the potential for providing a fast, quantitative assessment of recovered samples. Sensors of the MSCL include bulk density, P-wave velocity, natural gamma, electrical resistivity and magnetic susceptibility. Natural gamma is shown to be related to higher silt and clay content, acting as a useful parameter for soil classification and quantitative assessment of fines content. A comparison between MSCL and in-situ CPTu data reveals correlations between natural gamma and friction ratio, normalised soil behaviour index, Ic, and net pore pressure response. By collecting MSCL data on all samples at the beginning of a lab program, one can obtain a full overview of the material available for testing, leading to more strategic sample selection and a reduced overall volume of testing. Correlations with MSCL parameters and geotechnical properties can be used to predict continuous profiles, and be extended to in-situ measurements from borehole logging and CPT

Full Paper

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Published on 07/06/24
Submitted on 07/06/24

Volume Emerging technologies in site characterization for Offshore Wind Towers, 2024
DOI: 10.23967/isc.2024.213
Licence: CC BY-NC-SA license

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