Geotechnical Engineering

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Browsing Geotechnical Engineering by Subject "ANN"

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    Estimation of Soil Shear Strength Parameters from Index Properties Using ANN the Case of Addis Ababa
    (Addis Ababa University, 2022-05) Hanna, Yoseph; Tensay, Gebremedhin (PhD)
    Shear strength of a soil is perhaps the most important of its engineering properties, as stability analyses in the field of geotechnical engineering are dependent on it. This research work seeks to develop models for predicting the shear strength parameters (cohesion and angle of friction) of soils in Addis Ababa city using artificial neural network modeling technique; with a view to reducing time, effort and cost usually incurred in determining these shear strength parameters in the laboratory for future planning, design and construction projects in the study area. An attempt has been made to develop separate neural network models for c and ϕ from the index properties of soil consisting of Sand % (SP), Fines % (FP), Liquid limit (LL), Plasticity Index (PI), water content (ω), and Bulk density (BD) as input parameters. A multi-layer perceptron network with feed forward back propagation is used to model varying the number of hidden layers. For this purpose, 284 soil test result data was used. The geotechnical soil properties were determined in accordance with ASTM Standards. Direct shear box method was used to determine soil cohesion and soil internal friction angle. The developed models were found to be quite satisfactory in predicting shear strength parameters with correlation coefficients of about 0.98 and 0.92 for cohesion and angle of internal friction, respectively during the testing phase. The models are validated by primary soil test data and compared with some existing correlation methods. The result showed that the artificial neural network method gave better fit and accuracy than the selected empirical formulae in the prediction of shear strength parameters.
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    Use of Artificial Neural Network to Predict Compaction Characteristics of Soil from Soil Index Properties (Case of Addis Ababa)
    (Addis Ababa University, 2021-10) Hana, Adugna; Tensay, Gebremedhin (PhD)
    Soil compaction is the most commonly practiced mechanical method used to improve soil behavior and has a significant impact on earthwork structures. However, determining the compaction characteristics of soil in the laboratory requires considerable time and effort. For the purpose of attempting the problem of spending too much time and effort, ANN models that can predict the values of compaction parameters namely Maximum Dry Density (MDD) and Optimum Moisture Content (OMC) from soil index properties are developed using Artificial Neural Network (ANN). A total of 300 secondary data divided as coarse and fine grained soils were used to develop the ANN models. All data were soil laboratory test result records of different soil samples taken from Addis Ababa, Ethiopia. Percent of grain size distribution was used as input parameter for the coarse grained soils while plasticity index along with the percent fine were used as input variables for the fine grained soils. The two variables MDD and OMC were the desired outputs from the models. Supervised learning with a backpropagation algorithm was implemented to train the models using MATLAB R2020a. The models were validated using 15 primary data and the models’ performance was evaluated using statistical values. The fifteen soil samples were collected from different locations in Addis Ababa. Three soil laboratory tests namely grain size analysis, Atterberg limits, and modified compaction tests were done on each sample. The outputs of the developed models were compared with the actual experimental soil laboratory test results and showed a good accuracy with a determination coefficient value of R=0.92 and R=0.81 for both maximum dry density and optimum moisture content respectively. Equations were derived for the ANN models. The models were also compared with existing regression equations developed using Addis Ababa soil type. From the research, it was concluded that the developed ANN models can be applied to predict the value of compaction parameters from soil index properties for both coarse and fine grained soils of Addis Ababa.