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Öğe Deep Learning Approach on Prediction of Soil Consolidation Characteristics(Mdpi, 2024) Kim, Mintae; Senturk, Muharrem A.; Tan, Rabia K.; Ordu, Ertugrul; Ko, JunyoungArtificial neural network models, crucial for accurate predictions, should be meticulously designed for specific problems using deep learning-based algorithms. In this study, we compare four distinct deep learning-based artificial neural network architectures to evaluate their performance in predicting soil consolidation characteristics. The consolidation features of fine-grained soil have a significant impact on the stability of structures, particularly in terms of long-term stability. Precise prediction of soil consolidation under planned structures is vital for effective foundation design. The compression index (Cc) is an important parameter used in predicting consolidation settlement in soils. Therefore, this study examines the use of deep learning techniques, which are types of artificial neural network algorithms with deep layers, in predicting compression index (Cc) in geotechnical engineering. Four neural network models with different architectures and hyperparameters were modeled and evaluated using performance metrics such as mean absolute percentage error (MAPE), mean squared error (MSE), root mean squared error (RMSE), and coefficient of determination (R2). The dataset contains 916 samples with variables such as natural water content (w), liquid limit (LL), plasticity index (PI), and compression index (Cc). This approach allows the results of soil consolidation tests to be seen more quickly at less cost, although predictively. The findings demonstrate that deep learning models are an effective tool in predicting consolidation of fine-grained soil and offering significant opportunities for applications in geotechnical engineering. This study contributes to a more accurate prediction of soil consolidation, which is critical for the long-term stability of structural designs.Öğe Geotechnical assessment of seismicity and liquefaction potential at the Banarli landfill in Tekirdag, Turkey(Taylor & Francis Ltd, 2024) Kim, Mintae; Ordu, Ertugrul; Lee, Woojin; Ko, JunyoungThis study, conducted in the Banarli district of Tekirdag Province, Turkey, focuses on soil reaction analysis and liquefaction potential in an area where a landfill and an associated power plant are planned. The research was examined with a one-dimensional ground response analysis to understand seismic behaviour, revealing the varied responses of soil categories to seismic waves. Soft soil layers were found to lengthen the dominant period of oscillation rather than significantly enhancing peak acceleration during seismic events. Earthquake records specific to the region were crucial for accurate assessments. The study comprehensively assessed liquefaction potential using various estimation methods, including the probability of liquefaction (PL), liquefaction potential index (LPI), and liquefaction risk index (IR). Moreover, this study examined liquefaction potential based on Standard Penetration Test (SPT) results, identifying susceptibility in the clayey sand layer at a depth of 3 to 12 meters. Recommendations for soil improvement measures are made to mitigate the risk. This research highlights the need for customized seismic protocols and emphasizes the importance of site-specific assessments in construction projects. By addressing susceptibility to liquefaction and proposing targeted ground improvement strategies, we contribute to safer and more resilient infrastructure development.Öğe Prediction of California bearing ratio (CBR) for coarse- and fine-grained soils using the GMDH-model(Techno-Press, 2023) Kim, Mintae; Ordu, Seyma; Arslan, Ozkan; Ko, JunyoungThis study presents the prediction of the California bearing ratio (CBR) of coarse-and fine-grained soils using artificial intelligence technology. The group method of data handling (GMDH) algorithm, an artificial neural network-based model, was used in the prediction of the CBR values. In the design of the prediction models, various combinations of independent input variables for both coarse-and fine-grained soils have been used. The results obtained from the designed GMDH-type neural networks (GMDH-type NN) were compared with other regression models, such as linear, support vector, and multilayer perception regression methods. The performance of models was evaluated with a regression coefficient (R2), root-mean-square error (RMSE), and mean absolute error (MAE). The results showed that GMDH-type NN algorithm had higher performance than other regression methods in the prediction of CBR value for coarse-and fine-grained soils. The GMDH model had an R2 of 0.938, RMSE of 1.87, and MAE of 1.48 for the input variables {G, S, and MDD} in coarse-grained soils. For fine-grained soils, it had an R2 of 0.829, RMSE of 3.02, and MAE of 2.40, when using the input variables {LL, PI, MDD, and OMC}. The performance evaluations revealed that the GMDH-type NN models were effective in predicting CBR values of both coarse-and fine-grained soils.Öğe Prediction of Undrained Shear Strength by the GMDH-Type Neural Network Using SPT-Value and Soil Physical Properties(MDPI, 2022) Kim, Mintae; Okuyucu, Osman; Ordu, Ertuğrul; Ordu, Şeyma; Arslan, Özkan; Ko, JunyoungThis study presents a novel method for predicting the undrained shear strength (c(u)) using artificial intelligence technology. The c(u) value is critical in geotechnical applications and difficult to directly determine without laboratory tests. The group method of data handling (GMDH)-type neural network (NN) was utilized for the prediction of c(u). The GMDH-type NN models were designed with various combinations of input parameters. In the prediction, the effective stress (sigma(v)'), standard penetration test result (N-SPT), liquid limit (LL), plastic limit (PL), and plasticity index (PI) were used as input parameters in the design of the prediction models. In addition, the GMDH-type NN models were compared with the most commonly used method (i.e., linear regression) and other regression models such as random forest (RF) and support vector regression (SVR) models as comparative methods. In order to evaluate each model, the correlation coefficient (R-2), mean absolute error (MAE), and root mean square error (RMSE) were calculated for different input parameter combinations. The most effective model, the GMDH-type NN with input parameters (e.g., sigma(v)', N-SPT, LL, PL, PI), had a higher correlation coefficient (R-2 = 0.83) and lower error rates (MAE = 14.64 and RMSE = 22.74) than other methods used in the prediction of c(u) value. Furthermore, the impact of input variables on the model output was investigated using the SHAP (SHApley Additive ExPlanations) technique based on the extreme gradient boosting (XGBoost) ensemble learning algorithm. The results demonstrated that using the GMDH-type NN is an efficient method in obtaining a new empirical mathematical model to provide a reliable prediction of the undrained shear strength of soils.
