Liquefaction hazard analysis is crucial in earthquake-prone regions as it magnifies structural damage. In this study, standard penetration test (SPT) and shear wave velocity (Vs) data of Chittagong City have been used to assess the liquefaction resistance of soils using artificial neural network (ANN). For a scenario of 7.5 magnitude (Mw) earthquake in Chittagong City, estimating the liquefaction-resistance involves utilizing peak horizontal ground acceleration (PGA) values of 0.15 and 0.28 g. Then, liquefaction potential index (LPI) is determined to assess the severity of liquefaction. In most boreholes, the LPI values are generally higher, with slightly elevated values in SPT data compared to Vs data. The current study suggests that the Valley Alluvium, Beach and Dune Sand may experience extreme liquefaction with LPI values ranges from 9.55 to 55.03 and 0 to 37.17 for SPT and Vs respectively, under a PGA of 0.15 g. Furthermore, LPI values ranges from 25.55 to 71.45 and 9.55 to 54.39 for SPT and Vs correspondingly. The liquefaction hazard map can be utilized to protect public safety, infrastructure, and to create a more resilient Chittagong City.

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.

With the growing need for efficient mitigation strategies in liquefaction-prone regions, ensuring both seismic resilience and sustainability of infrastructure has become increasingly significant. This paper presents a datadriven probabilistic seismic demand model (PSDM) prediction and sustainability optimization framework to mitigate liquefaction-induced lateral deformation in regional mildly sloping ground improved with stone columns. The framework integrates finite element (FE) simulations with machine learning (ML) models, generating 1,200 ground FE models based on the key site attributes, such as ground inclination, soil properties, and stone column configurations. The performance of the selected ML models is evaluated through hyperparameter tuning by k-fold cross-validation, with the artificial neural network (ANN) outperforming other models in accurately predicting the PSDM. Subsequently, this framework is applied to a set of representative mildly sloping ground sites, enabling rapid PSDM prediction for each site with varying site attributes. Moreover, by incorporating cost and sustainability metrics, multi-objective optimization is performed using the developed ANN predictive model to maximize seismic performance while minimizing total carbon emissions and costs associated with ground improvement. Overall, the framework allows for rapid and accurate PSDM prediction and regional optimization, facilitating the identification of the optimal stone column configurations for efficient and sustainable liquefaction mitigation.

This paper focuses on enhancing the prediction of vertical soil displacement during deep excavation using Artificial Neural Networks (ANN). Precise prediction of soil movement is essential to ensure the stability of the construction site and surrounding infrastructure. Traditional methods, such as the Finite Element Method (FEM), while accurate, are time-consuming and computationally intensive. In contrast, ANN offers fast and reliable predictions, making it a valuable tool for real-time decision-making. This study integrates FEM-based data to train the ANN model, ensuring the ANN captures complex, non-linear interactions between input variables like depth, pore water pressure, and coordinates. The model is trained and evaluated using performance metrics such as MAE, MSE, RMSE, and R2. With a high correlation coefficient R2 = 0.969238, the ANN model provides predictions with minimal error, demonstrating its effectiveness in replicating real-world measurements. The combined approach of ANN and FEM leverages the strengths of both methods, with FEM offering detailed physical insights and ANN optimizing computational efficiency. The results indicate that ANN-based models can serve as an efficient predictive tool in large-scale construction projects, improving safety by anticipating potential soil displacement issues. Future research will focus on expanding the model's applicability across different soil conditions and enhancing prediction capabilities with other machine learning algorithms.

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.

Utilising recycled materials, such as construction and demolition waste (C&DW), into soil improvement projects offers a promising solution to reduce the environmental impact of the C&DW industry. This approach helps address issues related to waste generation, resource depletion, and environmental degradation, while enhancing the overall sustainability and resilience of soil stabilisation efforts. This study investigates the effectiveness of incorporating recycled C&DW into cement-treated peat and clayey soils to enhance their strength and stiffness. To achieve this goal, laboratory experiments were conducted on over 296 soil specimens to assess their Unconfined Compressive Strength (UCS), small-strain Young's modulus (E0) and shear modulus (G0). These tests included varying curing times (28, 60, 90, and 120 days), different cement and recycled material content, and water-to-cement ratios. Moreover, laboratory testing methods for determining geotechnical parameters are often time-consuming and prone to challenges. In this context, reliable predictive models, such as artificial neural networks (ANNs), offer an efficient alternative for accurately assessing these parameters. The findings of this research reveal that, along with cement content, the water-to-cement ratio (w/c) and curing time are key factors influencing the strength and stiffness of treated soft soils, underscoring their critical role in soil stabilisation. Additionally, while minimizing cement content and increasing RM yield improvements in both peat and clay, the effect is more pronounced in peat due to the time-dependent nature of pozzolanic reactions. This suggests that achieving optimal performance with increased strength and stiffness requires a carefully balanced RM content. Finally, the study demonstrates that ANN-based models can accurately predict the strength and mechanical properties of soft soils, offering a viable alternative to traditional UCS and FFR tests.

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.

The expansion of offshore wind farms, driven by better offshore wind conditions and fewer spatial limitations, has promoted the growth of this technology. This study focuses on the design of jacket support structures for Offshore Wind Turbines, which are suitable for deeper waters. However, the structural analysis required for designing these structures is computationally intensive due to multiple load cases and numerous checks. To reduce this computational cost, artificial-neural-network-based surrogate models capable of estimating the feasibility of a jacket structure acting as the support structure for any given wind turbine at a specific site are developed. A synthetic dataset generated through random sampling and evaluated by a structural model is utilized for training and testing the models. Two kind of models are compared: one is trained to estimate global feasibility, while the other estimates compliance with each of the structural partial requirements. Also, several assembly methods are proposed and compared. The best-performing model shows great classification metrics, with a Matthews Correlation Coefficient of 0.674, enabling an initial assessment of the structural feasibility. The low computational cost of artificial neural networks compared to structural models makes this surrogate model useful for accelerating otherwise prohibitive parametric studies or optimization processes.

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.