Understanding slope stability is crucial for effective risk management and prevention of slides. Some deterministic approaches based on limit-equilibrium and numerical methods have been proposed for the assessment of the safety factor (SF) for a given soil slope. However, for risk analyses of slides of earth dams, a range of SFs is required due to uncertainties associated with soil strength properties as well as slope geometry. Recently, several studies have demonstrated the efficiency of artificial neural network (ANN) models in predicting the SF of natural and artificial slopes. Nevertheless, such techniques operate as black-box models, prioritizing predictive accuracy without suitable interpretability. Alternatively, multivariate polynomial regression (MVR) models offer a pragmatic interpretability strategy by combining the analysis of variance with a response surface methodology. This approach overcomes the difficulties associated with the interpretability of the black-box models, but results in limited accuracy when the relationship between independent and dependent variables is highly nonlinear. In this study, two models for a quick assessment of slope SF in earth dams are proposed considering the MVR and the ANN models. Initially, a synthetic dataset was generated considering different soil properties and slope geometries. Then, both models were evaluated and compared using unseen data. The results are also discussed from a geotechnical point of view, showing the impact of each input parameter on the assessment of the SF. Finally, the accuracy of both models was measured and compared using a real-case database. The obtained accuracy was 78% for the ANN model and 72% for the MVR one, demonstrating a great performance for both proposed models. The efficacy of the ANN model was also observed through its capacity to reduce false negatives (a stable prediction when it is not), resulting in a model more favorable to safety assessment.

This study utilizes a combined approach of Finite Element Method (FEM) simulation and Artificial Neural Network (ANN) modeling to analyze and predict the load-displacement relationship of bored piles in clayey sand. FEM is applied to simulate the nonlinear relationship between load and vertical displacement, with input parameters including load and the mechanical properties of the soil. The results obtained from FEM are used as input data for the ANN model, enabling accurate predictions of vertical displacement based on these parameters. The findings of this study show that the predicted ultimate bearing capacity of the bored piles is highly accurate, with negligible error when compared to field experiments. The ANN model achieved a high level of accuracy, as reflected by an R2 value of 0.9992, demonstrating the feasibility of applying machine learning in pile load analysis. This research provides a novel, efficient, and feasible approach for analyzing and predicting the bearing capacity of bored piles, while also paving the way for the application of machine learning in geotechnical engineering and foundation design. The integration of FEM and ANN not only minimizes errors compared to traditional methods but also significantly reduces time and costs when compared to field experiments.

The number of studies concerning the shear strength of resedimented alluvial soils is extremely limited compared to the studies conducted on fine-grained marine sediments, since alluvial soils are generally tested in remolded or reconstituted state especially in the studies investigating their liquefaction potential. In this study, estimation models were developed to predict cohesion (c) and internal friction angle (phi) parameters of a fine-grained alluvial soil using resedimented samples. A total of 60 undisturbed soil samples were obtained from Bafra district of Samsun province (Turkiye) by core drilling. A cone penetration test with pore water pressure measurement (CPTu) was also carried out alongside each borehole to determine the over-consolidation ratios of the samples. Physical-index property determinations and triaxial tests were conducted on the undisturbed samples. 20 sample sets were created with known physical, index, and strength characteristics. The samples are classified as CH, CL, MH, and ML according to the Unified Soil Classification System, with liquid and plastic limits ranging from 31.6-75% and 19.3 to 33.6% respectively. The c and phi values of the samples varied from 4.1 to 46.1 kPa and 26 to 35 degrees respectively. The samples were then resedimented in the laboratory under conditions reflecting their original in-situ properties, and triaxial tests were repeated. The c and phi values of the resedimented samples ranged from 5.3 to 24.5 kPa and 28 to 32 degrees respectively. The results indicate that the c values of the resedimented samples are generally lower than those of the undisturbed samples, whereas upper and lower bounds for phi values are similar. Multivariate regression analyses (MVR) were utilized to develop estimation models for predicting c and phi using strength and physical properties of 20 soil samples as independent variables. Three estimation models with R-2 values varying between 0.723 and 0.797 were proposed for c and phi which are statistically significant for p <= 0.05. Using artificial neural networks (ANN), the estimation models developed by MVR were replicated to validate the models. ANN yielded very similar results to the MVR, where the R-2 values for the correlations between c and phi values predicted by both methods varied from 0.852 to 0.955. The results indicate that c and phi values of undisturbed samples can be estimated with acceptable accuracy by determining basic physical and index properties of the disturbed samples and shear strength parameters of the resedimented samples. This approach, which enables the reuse of disturbed soil samples, can be used when undisturbed soil samples cannot be obtained from the field due to economic, logistical, or other reasons. Further research on the shear strength parameters of resedimented alluvial soils is needed to validate the estimation models developed in this study and enhance their applicability to a wider range of alluvial soils.

This study investigated the stabilization of fine-grained soil from the Indo-Gangetic plain using nano-silica (NS) and predicted the unconfined compressive strength (UCS) using advanced machine learning techniques. Experimental investigations were conducted on 118 UCS samples with NS contents varying from 0.5 to 4%. The results showed significant improvements in the soil plasticity, compaction characteristics, and UCS with NS incorporation. NS acted as a reinforcing agent, filling void spaces and improving interlocking between soil particles, leading to increased maximum dry density, reduced optimum moisture content, and notable improvements in the UCS. Microstructure analysis revealed the positive impact of NS on soil properties, attributed to enhanced durability, reduced swell strains, and improved strength due to the synergistic effects of NS particles. Furthermore, five innovative hybridized models based on artificial neural networks (ANN) and nature-inspired optimization algorithms were developed to predict the UCS of NS-stabilized fine-grained soils. The models demonstrated high accuracy, with R2 values exceeding 0.96 and 0.89 for the training and testing dataset. The ANN-Firefly algorithm (ANN-FF) model emerged as the most proficient predictor. This study highlights the importance of input parameters in model development and suggests that further research should focus on expanding experimental data to enhance model flexibility. The proposed approach offers significant implications for cost and time savings in experimental sample preparation and demonstrates the high capability of ANN to determine optimal values for soil stabilization techniques in the Indo-Gangetic plains.

Many experiments and computational techniques have been employed to explain the mechanical properties of frozen soils. Nevertheless, due to the substantial complexity of their responses, modeling the stress-strain characteristics of frozen soils remains challenging. In this study, artificial neural networks (ANNs) were employed for modeling the mechanical behavior of frozen soil, while different testing strategies were carried out. A database covering stress-strain data from frozen sandy soil subjected to varying temperatures and confining pressures, resulting from triaxial tests, was compiled and employed to train the model. Subsequently, different artificial neural networks were trained and developed to estimate the deviatoric stress and volumetric strain, while temperature, axial strain, and confining pressure were considered as the main input variables. Based on the findings, it can be indicated that the models effectively predict the stress-strain behavior of frozen soil with a significant level of accuracy.

This research explores the application of Artificial Intelligence (AI) techniques to assess the mechanical properties of geopolymer concrete made from a blend of Banana Peel-Ash (BPA) and Sugarcane Bagasse Ash (SCBA), using a sodium silicate (Na2SiO3) to sodium hydroxide (NaOH) ratio ranging from 1.5 to 3. Utilizing three AI methodologies-Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Gene Expression Programming (GEP)-the study aims to enhance prediction accuracy for the mechanical properties of geopolymer concrete based on 104 datasets. By optimizing mix designs through varying proportions of BPA and SCBA, alkaline activator molarity, and aggregate-to-binder ratios, the research identified combinations that significantly enhance mechanical properties, demonstrating notable international relevance as it contributes to global efforts in sustainable construction by effectively utilizing industrial by-products. The experimental results demonstrated that increasing the molarity of the alkaline activator from 4 to 10 M significantly enhanced both the compressive and flexural strengths of the geopolymer concrete. Specifically, a mixture containing 52.5% SCBA and 47.5% BPA at a 10 M molarity achieved a maximum compressive strength of 33.17 MPa after 20 h of curing. In contrast, a mixture composed of 95% SCBA and 5% BPA at a 4 M molarity exhibited a substantially lower compressive strength of only 21.27 MPa. Additionally, the highest recorded flexural strength of 9.95 MPa (77.25% SCBA and 22.5 BPA) was observed at the 10 M molarity, while the flexural strength at 4 M was lowest, at 4.12 MPa (95% SCBA and 5% BPA). Microstructural analysis through Scanning Electron Microscopy with Energy-Dispersive X-ray Spectroscopy (ED-SEM) revealed insights into the pore structure and elemental composition of the concrete, while Thermogravimetric Analysis (TGA) provided data on the material's thermal stability and decomposition characteristics. Performance analysis of the AI models showed that the ANN model had an average MSE of 1.338, RMSE of 1.157, MAE of 3.104, and R2 of 0.989, while the ANFIS model outperformed with an MSE of 0.345, RMSE of 0.587, MAE of 1.409, and R2 of 0.998. The GEP model demonstrated an MSE of 1.233, RMSE of 1.110, MAE of 1.828, and R2 of 0.992, confirming that ANFIS is the most accurate model for predicting the mechanical and rheological properties of geopolymer concrete. This study highlights the potential of integrating AI with experimental data to optimize the formulation and performance of geopolymer concrete, advancing sustainable construction practices by effectively utilizing industrial by-products.

Accurate settlement forecasting is essential for preventing severe structural and infrastructure damage. This paper investigates predicting tunneling-induced ground settlements using machine learning models. Empirical methods for estimating settlements are often imprecise and site-specific. Developing novel, accurate prediction methods is critical to avoid catastrophic damage. The umbrella arch method constrains deformations for initial stability before installing primary support. This study develops machine learning models to forecast settlements solely from umbrella arch parameters, disregarding soil properties. Multilayer perceptron artificial neural networks (MLP-ANN) and support vector regression (SVR) are applied. Results demonstrate machine learning outperforms empirical methods. The MLPANN surpasses SVR, with R2 of 0.98 and 0.92, respectively. Strong correlation is observed between umbrella arch configuration and settlements. The suggested approach effectively estimates surface displacements lacking mechanical properties. Overall, this study supports machine learning, specifically MLP-ANN, as an efficient, reliable alternative to empirical methods for predicting tunneling-induced ground settlements from umbrella arch design.

Featured Application This work is intended to provide additional approaches for analyzing and interpreting dam monitoring data.Abstract Pore water pressure (PWP) response is significant for evaluating the earth dams' stability, and PWPs are, therefore, generally monitored. However, due to the soil heterogeneity and its non-linear behavior within earths, the PWP is usually difficult to estimate and predict accurately in order to detect a pathology or anomaly in the behavior of an embankment dam. This study endeavors to tackle this challenge through the application of diverse machine learning (ML) techniques in estimating the PWP within an existing earth dam. The methods employed include random forest (RF) combined with simulated annealing (SA), multilayer perceptron (MLP), standard recurrent neural networks (RNNs), and gated recurrent unit (GRU). The prediction capability of these techniques was gauged using metrics such as the coefficient of determination (R2), mean square error (MSE), and CPU training time. It was found that all the considered ML methods could give satisfactory results for the PWP estimation. Upon comparing these methods within the case study, the findings suggest that, in this study, multilayer perceptron (MLP) gives the most accurate PWP prediction, achieving the highest coefficient of determination (R2 = 0.99) and the lowest mean square error (MSE = 0.0087) metrics. A sensitivity analysis is then presented to evaluate the models' robustness and the hyperparameter's influence on the performance of the prediction model.

In underground space technology, the issue of tunnel stability is a fundamental concern that significantly causes catastrophe. Owing to sedimentation and deposition processes, the strengths of clays are anisotropic, where the magnitudes of undrained shear strengths in the vertical and horizontal directions are different. The anisotropic undrained shear (AUS) model is effective at considering the anisotropy of clayey soils when analyzing geotechnical stability issues. This study aims to assess the stability of rectangular tunnels by adjusting the dimensionless overburden factor, cover-depth ratio, and width-depth ratio in clay with various anisotropic strength ratios. The stability analysis of these tunnels involves employing finite element limit analysis and the AUS model to identify the planes of soil collapse in response to the aforementioned variations. In addition, this study presents the development of soft-computing models utilizing artificial neural networks (ANNs) to forecast the stability of rectangular tunnels across various combinations of input parameters. The findings of this study are presented in the form of design charts, tables, and soft-computing models to facilitate practical applications.