• Geotechnical Engineering
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• v.13 no.5
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• pp.89-100
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• 1997

It is very improtant to evaluate the reliable nonlinear deformational characteristics of soils not only in the analysis of geotechnical structures under working stress conditions but also for the soil dynamic problems. Field testings such as crosshole and pressuremeter tests can be used to determine the modulus of soils under in-situ conditions, but it is not possible to determine the modulus over the entire strain amplitude range. Laboratory methods such as resonant column 1 torsional shear test can be used to determine the modulus over the whole strain amplitude range, but it is very difficult to obtain the representative undisturbed samples on the sixte. For the reliable evaluation of nonlinear deformation characteristics of soils on a typical site, small strain modulus obtained from field testy and nomalized modulus reduction curve determined by laboratory bests need to be combined. In this paper, laboratory and Held testy were performed at a sixte which consisted of granite wearthered residual boils to evaluate the nonlinear deformational characteristics of coils such as the effects of strain amplitude, loading frequency, confining pressure and sample disturbance. It has been shorn that when the effects of these factors are properly taken into account, the stiffness values evaluated by various field and labrotary tests are comparable to each other fairly well. Finally, the procedure to evaluate the nonlinear deformstional characteristics of the sixte was proposed.

• Korean Journal of Agricultural Science
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• v.12 no.1
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• pp.86-100
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• 1985

In order to investigate the strength characteristics of weathered granite soils in unsaturated state, the five physically different weathered granite soils and the common soil (sandy loam) were examined. The disturbed and the undisturbed material were prepared for triaxial compression test. The following conclusions were drawn from the study; 1. Dry density of the undisturbed soil samples was lower than maximum dry density determined from the compaction test and it showed the higher value at the well graded soil. 2. The failure strength of the samples decreased with the increase of moisture content of the soil and these results were highly pronounced at the common soil sample having a good cohesive property. 3. On weathered granite soils, the cohesion was lower measured and the internal friction angle highly, the decrease rate at internal friction angle with increase of moisture content of the soil was more significant than that of cohesion 4. The modulus of deformation of the samples decreased with increase of moisture content of the soil and these phenomena were highly pronounced at the weathered granite soils than common soil. 5. The failure strength of the samples increased with in crease of confining pressure and effect of confining pressure on failure strength was highly significant at the lower moisture content of the soil.

• The Journal of Engineering Geology
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• v.31 no.4
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• pp.701-718
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• 2021

Geological field surveys and a series of laboratory tests were conducted to obtain data related to landslides in Sancheok-myeon, Chungju-si, Chungcheongbuk-do, South Korea where many landslides occurred in the summer of 2020. The magnitudes of various factors' influence on landslide occurrence were evaluated using logistic regression analysis and an artificial neural network. Undisturbed specimens were sampled according to landslide occurrence, and dynamic cone penetration testing measured the depth of the soil layer during geological field surveys. Laboratory tests were performed following the standards of ASTM International. To solve the problem of multicollinearity, the variation inflation factor was calculated for all factors related to landslides, and then nine factors (shear strength, lithology, saturated water content, specific gravity, hydraulic conductivity, USCS, slope angle, and elevation) were determined as influential factors for consideration by machine learning techniques. Minimum-maximum normalization compared factors directly with each other. Logistic regression analysis identified soil depth, slope angle, saturated water content, and shear strength as having the greatest influence (in that order) on the occurrence of landslides. Artificial neural network analysis ranked factors by greatest influence in the order of slope angle, soil depth, saturated water content, and shear strength. Arithmetically averaging the effectiveness of both analyses found slope angle, soil depth, saturated water content, and shear strength as the top four factors. The sum of their effectiveness was ~70%.