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The orientations of particles, pores, and other constituents of an artificially made cohesive sandy silt–clay soil were studied to investigate how they change during drained and undrained shear. The results show that the orientation pattern before shearing is nearly random, although there may be some degree of preferred orientation caused by the overburden pressure. The degree of preferred orientation increases as the shearing increases until failure in both the drained and undrained tests and increases towards the failure plane. After failure, the degree of preferred orientation does not change considerably near the failure plane but does continue to increase away from it. The number of oriented particles, pores, and other constituents increases, but their averages stay about the same as the shearing continues after failure in the drained tests. The differences between the degrees of preferred orientations 5 and 10 mm away from the failure planes at different shear (horizontal) displacements are much less in the drained tests than in the undrained tests, indicating formation of a wider deformation zone in the drained tests. This is probably because particles in the drained tests have enough time to respond to the applied shear stresses and change their orientation. This may explain why deformations occur in wide zones along tectonically active creeping (aseismic) faults, whose mechanisms are analogous to those of drained shear tests, and in narrow zones along seismic faults, whose mechanisms are analogous to those of undrained shear tests.Key words: shear test, soil structure, soil fabric, preferred orientation, seismic, aseismic.

Shortening the periods of compaction tests can be possible by analyzing the data obtained from previous laboratory tests with regression methods. The regression analysis applied to current data reduces the cost of experiments, saves time, and gives estimated outputs. In this study, the MLS-SVR, KB-SVR, and DTR algorithms were employed for the first time for the estimation of soil compaction parameters. The performances of these regression algorithms in estimating maximum dry unit weight (MDD) and optimum water content (OMC) were compared. Furthermore, the soil properties (fine-grained soil, sand, gravel, specific gravity, liquid limit, and plastic limit) were employed as inputs in the study. The data used for the study were supplied from the experimental soil tests from small dams in Niğde, a province in the southern part of Central Anatolia, Turkey. Polynomial-based KB-SVR yielded the best R-values with 0.93 in the prediction of both OMC and MDD. Moreover, in the multioutput estimation model, polynomial and RBFbased KB-SVR methods were successful with 0.98 and 0.99, respectively. Additionally, while the MSE value was 1.33 in the estimation of OMC, this value was 0.04 in the estimation of MDD. Accordingly, MDD was the most successfully estimated parameter in all processes. It was concluded that through the algorithms used in this study, the prediction of soil compaction parameters could be possible without the need for further laboratory tests.

Some challenging studies are experimentally applied for characterizing parameters in Proctor compaction tests. Compression of a fill is mechanically done in Compaction process. Compaction is a physical process which gets the soil into a dense state. Improving the shear strength and decreasing the compressibility and permeability of the soil can be done with this physical process. Support Vector Machine (SVM) is a popular method due to its performance today. This method is commonly employed in the regression analysis as well as being used in the classification process. In this study, SVM was employed to predict of compaction parameters (maximum dry unit weight and optimum moisture content) without making any experiments in a soil laboratory. In the study, more than a hundred compaction data collected from the small dams in central Anatolia region was employed. In the study, R errors are satisfied (0.92 and 0.89) for SVM models. Consequently, the proposed regression analysis with SVM is useful for model design of the projects in where there are limitations as financial and temporal.

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