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.

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.

The California Bearing Ratio (CBR) is a critical parameter in pavement design and building foundation assessment; however, it requires labor intensive laboratory testing, including a 96 h soaking period. This study evaluated nine machine learning algorithms for predicting CBR from soil index properties: Extra Trees, Support Vector Regression (SVR), Random Forest, Gaussian Process Regression (GPR), CatBoost, LightGBM, XGBoost, Artificial Neural Network (ANN), and ElasticNet. Using 236 soil samples characterized by eight features, we conducted repeated stratified 10-fold cross validation (100 iterations). Extra Trees achieved the highest cross validation R² of 0.789 ± 0.095 (RMSE = 2.064 ± 0.481, MAE = 1.482 ± 0.294), followed by SVR (R² = 0.783 ± 0.102, RMSE = 2.090 ± 0.511, MAE = 1.446 ± 0.300) and Random Forest (R² = 0.777 ± 0.104, RMSE = 2.117 ± 0.460, MAE = 1.518 ± 0.299). The Friedman statistical test confirmed significant performance differences (χ² = 191.97, p < 10⁻³⁷), and Nemenyi post hoc analysis identified Extra Trees, SVR, Random Forest, and GPR as statistically equivalent superior groups. SHAP analysis highlighted gravel content (29.0%), maximum dry density (23.8%), and fines content (14.8%), which is consistent with geotechnical principles. Systematic noise injection (20% perturbation) demonstrated model stability, with less than 7% performance degradation at 15% noise. On this heterogeneous compiled dataset, which extends beyond the calibration domain of the empirical equations, all six empirical methods yielded negative R² (range: −0.803 to −22.639), while all ML models achieved positive R² (≥0.655 to 0.789). Extra Trees achieved a 3.1× lower RMSE than the best empirical equation, confirming substantially better predictive performance in this out-of-calibration setting. The framework provides a practical five step implementation workflow that may reduce the need for preliminary CBR tests under project specific accuracy thresholds.

Exploiting of Adaptive Multi Agent System Theory in Modeling and Simulation: A Survey Rabia Korkmaz Tan, Şebnem Bora Department of Computer Engineering, Ege University, Izmir, Turkey Corresponding a…

Power cables are one of the important parts of electrical power systems. It is important to know the materials that make up the cable or what the life of the cable will be at a given temperature. Therefore, aging tests are performed for the materials used in the cables. One of these tests is the thermal endurance test of the cable. The curve fitting process is performed to the data obtained as a result of this test and the temperature value (the temperature index) corresponding to the 20000-hour life of the cable is found. Automating the process of curve-fitting and finding the temperature index will be useful for companies or research centers that perform this test. Instead of expensive programs such as Matlab or Mathematica, the easy-to-use and accessible Excel program is a very economical and useful solution to evaluate these test results. In this study, the thermal endurance test was briefly described and a spreadsheet was prepared in Excel to evaluate the results of this test. Using the data taken from the measurements made for the EPR material, it has been checked that the spreadsheet works correctly. Such a spreadsheet can be easily used in cable factories to evaluate thermal endurance test results.