DSpace-CRIS

Ensuring safety in geotechnical engineering has consistently posed challenges due to the inherent variability of soil. In the case of slope stability problems, performing on-site tests is both costly and time-intensive due to the need for sophisticated equipment (to acquire and move) and logistics. Consequently, the analysis of simulation models based on soft computing proves to be a practical and invaluable alternative. In this research work, learning abilities of the Class Noise Two (CN2), Stochastic Gradient Descent (SGD), Group Method of Data Handling (GMDH) and artificial neural network (ANN) have been investigated in the prediction of the factor of safety (FOS) of slopes. This has been successfully done through literature search, data curation and data sorting. A total of three hundred and forty-nine (349) data entries on the FOS of slopes were collected from literature and sorted to remove odd values and unlogic results, which had been used together in a previous research work. After the sorting process, the remainder of the realistic data entries was 296. The previous work which had included unrealistic data entries had unit weight, γ (kN/m<sup>3</sup>), cohesion, C (kPa),angle of internal friction (Φ°), slope angle (°), slope height H (m), and pore water pressure ratio, r<inf>u</inf> as the studied parameters, which formed the independent variables. After careful checks, the initial results showed poor correlation with the individual factors and the factors were collected into three non-dimensional parameters based on the understanding of the physics of flows, which are: C/γ.h-Cohesion/unit weight x slope height, tan(ϕ)/tan(β)-the tangent of internal friction angle/Tangent of slope angle, and ρ/γ.h-Water pressure/unit weight x slope height, which are deployed as inputs and FOS-the safety factor of the slope as the output. At the end of the exercise, the ANN outclassed the other techniques with SSE of 62%, MAE of 0.27, MSE of 0.21, RMSE of 0.46, average total error of 24%, and R<sup>2</sup> of 0.946 thereby becoming the decisive intelligent model in this exercise. However, there is an advantage the deployment of GMDH, which comes second in order of superiority, has over the ANN. This is the development of a closed-form equation that allows its model to be applied manually in the design of slope stability problems. Overall, the present research models outperformed the eleven (11) models of the previous work due to sorting and elimination of unrealistic data entries deposited in the literature, the application of dimensionless combination of the studied slope stability parameters and the superiority of the selected machine learning techniques.

Different soil improvement techniques have been used to intensify the engineering properties of soil. Three different lignocellulose fiber-reinforced (jute, coir and water hyacinth (WH)) have been explored on the desiccation potential of compacted clayey silt coil. The experimental methodology involved the mixing of fibers with soil at requisite amount and subjecting them to natural environment with controlled irrigating. The controlled irrigation comprised of 15 wetting/drying cycles for 105 days. Parameters like matric suction and water content were focused upon and recorded along with the surface crack formation. The data obtained from the field experiments were analyzed using the Artificial Neural Network (ANN) approach, which is developed in house using C++ language. From the analysis, it can be comprehended that coir is more effective as a reinforcement due to its multifilament nature and higher lignin content which is suitable in resisting crack formation. Further, optimization analysis and sensitivity analysis suggested mechanism of cracking for each fiber.

KMITL

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