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.

3D printing technology is gradually considered to be a rapid development of a green revolution in the field of architecture. Recently, utilizing solid mine waste to replace natural sand not only greatly reduces the 3D printing costs, but also contributes to an environmental sustainability development. However, most solid waste inevitably has an impact on the inherent mechanical strength and printability of concrete materials. It is an urgent requirement to expand the alternative materials and improve the overall property of 3D concrete materials. This paper reported an innovative concrete material that replaced natural sand with fine limestone powders for 3D concrete printing applications. The experimental measurements were performed including microstructures characteristics, flowability, buildability, shrinkability, layer-interface properties, mechanical properties and interlayer bonding strength. Besides, an effective method was proposed to characterize the printable properties of concrete materials and then the reasonable limestone powder replacement ratio was determined. Based on the investigation results, appropriate substituting limestone powder (40%) can effectively improve the grading of the concrete, thus promoting its printability and buildability. Moreover, the microstructures of the 3D printing concrete materials after curing were denser and their mechanical property improved by approximately 45%. With the further increase of replacement ratio, the reduction in the flowability led to a decrease of the printability. A large number of fine particles increased the shrinkage of the curing process and some bubbles were stranded inside the materials due to its increase in the viscosity, thereby reducing the mechanical properties of the hardened material. The produced concrete for 3D printing can be treated as an eco-friendly building material that contributes to the rational development and resource utilization of solid water, thus promoting the sustainable development of construction field.

This 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 (σ_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 (R²), 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., σ_v’, N_SPT, LL, PL, PI), had a higher correlation coefficient (R² = 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.

The conformal fractional derivative (CFD) has become a hot research topic since it has a physical interpretation and is easier to grasp and apply to problems compared with other fractional derivatives. Its application to heat transfer, diffusion, diffusion-advection, and wave propagation problems can be found in the literature. Fractional diffusion equations have received great attention recently due to their applicability in physical, chemical, and biological processes and engineering. The diffusion of the pollutants within the ground, which is an important environmental problem, can be modeled with a diffusion equation. Diffusion in some porous materials or soil can be modeled more accurately with fractional derivatives or the conformal fractional derivative. In this study, the diffusion problem of a spilled pollutant leaking into the ground modeled with the conformal fractional time derivative in spherical coordinates has been solved analytically using the Fourier series for a constant mass flow rate and complete symmetry under the assumptions of homogeneous and isotropic soil, constant soil temperature, and constant permeability. The solutions have been simulated spatially and in time. A parametric analysis of the problem has been performed for several values of the CFD order. The simulation results are interpreted. It has also been suggested how to find the parameters of the soil to see whether the CFD can be used to model the soil or not. The approach described here can also be used for modeling pollution problems involving different boundary conditions.

The construction of buildings and infrastructure on marine deposits is challenging. The impact of the horizontal behavior of structures on reclaimed areas is critical. This study investigated the behavior of laterally loaded drilled shafts in marine deposits through a comprehensive analysis and full-scale lateral load test conducted in Songdo, South Korea. It identified various critical pile characteristics for designing and constructing architectural and civil structures in marine environments, focusing on a 2.5 m diameter, 40 m long drilled shaft. At a 900 kN design load, the test pile experienced a maximum moment of 3520.2 kN·m and a lateral deflection of 2.32 mm, with anticipated failure at a load of 1710 kN and 11.30 mm displacement. Fiber Bragg Grating (FBG) sensors enabled precise displacement and strain measurements, essential for constructing accurate load–displacement curves and understanding lateral load responses. Inverse analysis with validated data from a commercial software (LPILE) showed good alignment of maximum moment and displacement but highlighted challenges at failure loads. The study developed depth-dependent p-y curves for marine deposits, crucial for predicting soil–pile interaction and optimizing shaft design. Practical implications include using derived p-y curves and an empirical equation using Standard Penetration Test (SPT) results to predict the coefficient of horizontal subgrade reaction (k_h) with high accuracy. Overall, this research emphasizes the importance of advanced instrumentation and analytical techniques for optimizing drilled shaft design and ensuring structural stability in challenging marine geological conditions.