Characterization Of Soil Shear Strength Model From Electrical Resistivity And Seismic Refraction Methods
For a robust and detailed subsurface characterization, the present study characterizes soil cohesion and friction angle models from post inversions of electrical resistivity and seismic refraction tomographic datasets, and geotechnical data using shear strength test and multiple linear regression (M...
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | English |
| Published: |
2022
|
| Subjects: | |
| Online Access: | http://eprints.usm.my/59564/1/BALARABE%20BALA%20-%20TESIS%20cut.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | For a robust and detailed subsurface characterization, the present study characterizes soil cohesion and friction angle models from post inversions of electrical resistivity and seismic refraction tomographic datasets, and geotechnical data using shear strength test and multiple linear regression (MLR) methods. It further correlates the subsurface at various sites from geotechnical and geophysical perspectives using the developed models and validates the reliability and efficacy of the models at different locations. Three models were therefore built; firstly, simple linear models were achieved between shear strength and moisture parameters with only resistivity parameter. Secondly, resistivity and seismic refraction velocity parameters with the shear strength and moisture parameters were determined as the MLR models. Two of the MLR models, soil cohesion and friction angle, were accepted based on the strong relationships among the parameters, such as coefficient of determination (R2), 0.777 and p-values, <0.050, while the other rejected. The obtained coefficients of the accepted models were transferred and applied for the estimations of 2D soil cohesion and internal angle of friction models for validation at Minden_USM, Batu Uban, Cahaya Gemilang and Bukit Gambir areas. The developed models demonstrated good performance, based on the accuracy assessments; < 5%, and < 10% for the root mean square error (RMSE) and mean absolute percentage error (MAPE) respectively. The approach generated, new geotechnical models, rebuilding of subsurface geometries in two-space. |
|---|