Artificial 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 (C_c) 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 (C_c) 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 (C_c). 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.

Soil consolidation, particularly in fine-grained soils like clay, is crucial in predicting settlement and ensuring the stability of structures. Additionally, the compressibility of fine-grained soils is of critical importance not only in civil engineering but also in various other fields of study. The compression index (C_c), derived from soil properties such as the liquid limit (LL), plastic limit (PL), plasticity index (PI), water content (w), initial void ratio (e₀), and specific gravity (G_s), plays a vital role in understanding soil behavior. This study employs machine learning algorithms—the random forest regressor (RFR), gradient boosting regressor (GBR), and AdaBoost regressor (ABR)—to predict the C_c values based on a dataset comprising 915 samples. The dataset includes LL, PL, W, PI, G_s, and e₀ as the inputs, with C_c as the output parameter. The algorithms are trained and evaluated using metrics such as the coefficient of determination (R²), mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE). Hyperparameter optimization is performed to enhance the model performance. The best-performing model, the GBR model, achieves a training R² of 0.925 and a testing R² of 0.930 with the input combination [w, PL, LL, PI, e₀, G_s]. The RFR model follows closely, with a training R² of 0.970 and a testing R² of 0.926 using the same input combination. The ABR model records a training R² of 0.847 and a testing R² of 0.921 under similar conditions. These results indicate superior predictive accuracy compared to previous studies using traditional statistical and machine learning methods. Machine learning algorithms, specifically the gradient boosting regressor and random forest regressor, demonstrate substantial potential in predicting the C_c value for fine-grained soils based on multiple soil parameters. This study involves leveraging the efficiency and effectiveness of these algorithms in geotechnical engineering applications, offering a promising alternative to traditional oedometer testing methods. Accurately predicting the compression index can significantly aid in the assessment of soil settlement and the design of stable foundations, thereby reducing the time and costs associated with laboratory testing.

In this study, it is aimed to provide significant advantages in terms of time and cost by estimating critical standard compaction parameters such as maximum dry density (MDD) and optimum moisture content (OMC) with machine learning methods instead of traditional laboratory tests. A large dataset including different soil components such as gravel, sand, fine-grained, liquid limit (LL), plastic limit (PL) and plasticity index (PI) was used and algorithms such as decision tree, random forest, gradient boosting and group data processing method (GMDH) were compared. Model performances were evaluated using metrics such as R $$^{2}$$ 2 (coefficient of determination) and RMSE (root mean square error). The results show that the gradient boosting algorithm achieved high accuracy in estimating optimum moisture content (OMC) with a testing R $$^{2}$$ 2 value of 0.91, while the random forest algorithm was the most successful model in estimating maximum dry density (MDD) with a testing R $$^{2}$$ 2 value of 0.92. Machine learning models have been shown to provide faster and lower-cost predictions by reducing the dependency on laboratory tests and offer an effective alternative for soil standard compaction analyses in geotechnical engineering.

This study aims to predict the soil compaction parameters maximum dry density (MDD) and optimum moisture content (OMC) using machine learning models. As a result of this aim, fast and much less costly advanced machine learning models can be used as an alternative to traditional methods such as proctor test, which are time-consuming and costly in geotechnical engineering. The study conducts a comparative analysis of various machine learning models such as decision tree, random forest, gradient boosting and group method of data handling (GMDH) to determine the most accurate and efficient model. As a result of this comparative analysis, the random forest model was the most successful model. According to the 5-fold cross-validation results of the random forest model, the average training coefficient of determination (R²) value is 0.95 and the average testing R² value is 0.71. Surely, if we had a dataset with more data, higher performance results could be achieved with existing machine learning models. The results obtained demonstrate that machine learning models increase project efficiency and reduce costs by reducing reliance on laboratory testing, and offer an approach with the potential to improve soil compaction applications in geotechnical engineering.

Epidemics like the COVID-19 epidemic are major global problems, some of which spread rapidly, causing high mortality rates. The rapid spread of such diseases leaves behind the production and global use of medical testing tools, and during this period, the undetected disease spreads quickly and becomes hard to control. Therefore, non-medical digital technologies with faster disease prediction are needed. The studies on deep learning, a subfield of artificial intelligence, reveal that it is possible to predict disease more quickly. It will be possible to stop the pandemic progress with minimal damage if the best predictive approach is used. Lives are saved when the disease is detected early. A dataset containing low-dose CT scan images, gender, age, weight, COVID-19 PCR test result, and symptoms including cough, fever, shortness of breath, chest pain, and fatigue were used in this study. The primary goal is developing an assistive system for diagnosing COVID-19 disease based on deep learning using this dataset. The deep learning technique used in this study has a multi-branch architecture consisting of both classical artificial neural networks and convolutional neural networks. As a result of this study, it was concluded that the developed technique can be used not only for COVID-19 but also for different diseases and is open to development with data from more patients.