Waves can cause significant accumulation of pore water pressure and liquefaction in seabed soils, leading to instability of foundations of marine hydrokinetic devices (MHKs). Geostatic shear stresses (existing around foundations, within slopes, etc.) can substantially alter the rate of pore pressure buildup, further complicating the liquefaction susceptibility assessments. In this study, the development of wave-induced residual pore water pressure and liquefaction within sandy seabed slopes supporting MHK structures is evaluated. Unlike most earlier studies that excluded the impact of shear stress ratios (SSR) on the residual pore pressure response of sloping seabeds, asymmetrical cyclic loadings are considered herein for a range of SSRs. To obtain wave-induced loading in the seabed (and cyclic shear stress ratios, CSRs), the poroelasticity equations governing the seabed response, coupled with those for fluid and structure domains, are solved simultaneously. Utilizing an experimental model based on anisotropic cyclic triaxial test data that includes CSR and SSR impacts, an equation for the rate of pore pressure buildup is developed and added as a source term to the 2D consolidation equation. Numerical investigations were performed by developing finite element models in time domain. The models were calibrated using particle swarm optimization method and validated against wave flume experimental data. The results indicate that the consideration of static shear stresses has led to sudden rise in residual pore pressures followed by fast dissipations at early and late time steps, respectively, beneath the structure. The exclusion of SSR is shown to cause significant overestimation of pore pressure accumulations at late cycles, potentially causing significant overdesign of MHK foundations. The impact of proximity to the free drainage boundary, CSR amplitude, and loading frequency on the accumulation of residual pore pressure is illustrated. The residual liquefaction susceptibility of the seabed is shown to decline by increase of the seabed slope angle.

The present work introduces an analytical framework based on the limit-equilibrium method for the determination of the local factor of safety (FS) and global factor of safety (FSG), and local displacements along the critical slip surface using the Morgenstern-Price (MP) method of slices. This proposed work computes displacements along the critical slip surface in addition to a single FSG. The unsaturated shear strength models, in conjunction with the soil-water characteristic curve (SWCC), are considered. The MP-based equilibrium equations to determine FSG are utilized as an objective function in the metaheuristic search algorithm of particle swarm optimization to determine the critical center, critical radius, and minimum FSG for unsaturated finite slopes. It is recommended to use a particle size of 75 and conduct 50 iterations for optimal results. The effects of SWCC fitting parameters on the critical slip surface, FSG, point FS, and point displacements are also investigated. Two distinct benchmark slope scenarios with and without negative pore water considerations are utilized in the current study. This approach enables a detailed investigation into the influence of various unsaturated soil parameters, such as af (related to the air-entry value), nf (related to the slope of the SWCC), and mf (related to the residual water content), as well as constitutive model parameters including the linear shear modulus (G) and the fitting parameter (rho). The maximum displacement occurs at the slope's top crest. Under benchmark conditions, the first scenario shows a reduction in point displacement by 3.30%, 1.98%, and 10.23% for SWCC-1, SWCC-2, and SWCC-3, respectively. However, in the second scenario with SWCC-3, the critical slip surface's position changes, affecting local displacements. This results in an increase of 32.72% (i.e., from 21.45 to 28.47 mm) in point displacement at the top when comparing SWCC-3 with no SWCC consideration. The current study advocates that the effect of fitting parameters of the SWCC should be used to better understand the local FS and displacement, because the critical slip surface is contingent on the values of the SWCC. Ignoring SWCC parameters can lead to an underestimation of slope displacement, because they significantly influence the critical slip surface position and displacement magnitude. Their inclusion is essential for accurately assessing slope stability and preventing errors in displacement prediction.

To ensure the sustainable development of the surrounding environment and the sustainable operation of landfills, detecting landfill leakage is of great significance. In landfills lacking a leakage monitoring system, the inability to detect and locate damaged High-Density Polyethylene (HDPE) membranes can lead to the contamination of soil and groundwater by landfill leachate. To address this issue, this study proposes a resistivity tomography inversion model based on the external-electrode power supply mode. Utilizing the resistivity difference between the leakage zone and the surrounding soil, electrodes are arranged symmetrically for both power supply and measurement. Upon applying direct current (DC) excitation, potential data are collected, with the finite volume method employed for inversion and the Gauss-Newton method integrated with an adaptive particle swarm optimization algorithm for parameter fitting. Experimental results show that the combined algorithm provides better clarity in edge recognition of low-resistance models compared with single algorithms. The maximum deviation between inferred leakage coordinates and the actual location is 10.1 cm, while the minimum deviation is 6.4 cm, satisfying engineering requirements. This method can effectively locate point sources and line sources, providing an accurate solution for subsequent leakage point filling and improving repair efficiency.

This study aims to improve the forecasting performance of slope stability for impacting environmental sustainability and infrastructure safety predictions by using the Binary Particle Swarm Optimization BPSO technique is utilized to select relevant features from the dataset, thereby improving the overall effectiveness of the predictive models. The research includes 108 slope stability examples, with the dataset split between 70% training and 30% validation. The dataset comprises seven input parameters: cohesiveness, slope angle, unit weight, angle of internal friction, slope height, pore water pressure coefficient, and factor of safety. The objective is to classify the slope status, turning the problem into a classification task. To obtain optimal hyper-parameters for the SVM model, Grid Search was exploited. The accuracy of the slope stability predictions given by several models was assessed using receiver operating characteristic (ROC) curves. The results indicate that the BPSO-SVM model outperforms the standalone SVM and BPSO models, serving as a robust computational tool capable of accurately predicting slope stability to enhance the environmental sustainability.

Accurate evaluation of cumulative strains in marine soils under long-term cyclic loading is essential for the design and safe operation of offshore wind turbines. This study proposes an enhanced machine learning model to predict the cumulative strain in marine soils subjected to cyclic loading. Cumulative strains of marine soils from five offshore wind farms under long-term cyclic loading were tested. Four prediction models for cumulative strains were developed and evaluated based on test results using the Back Propagation Neural Network (BP-NN), Random Forest (RF), Support Vector Regression (SVR), and eXtreme Gradient Boosting (XGBoost) models, each combined with the Particle Swarm Optimization (PSO) algorithm. The prediction model with the highest accuracy was further analyzed using the SHapley Additive exPlanations (SHAP) method. Results show that the RF and XGBoost algorithms have higher prediction accuracy, with R2 values above 0.99, compared to the BP-NN and SVR models. Furthermore, dynamic triaxial test parameters significantly influence the cumulative strain predictions more than the soil properties. This study provides a more efficient method for cumulative strain assessment of marine soils under long-term cyclic loading.

Purpose Rubber-based isolation systems produce enormous isolator displacement, requiring large seismic gap and causing excessive residual displacement, which can damage the isolator and it has lack of energy dissipation capability. These can be overcome by incorporating shape memory alloy (SMA) with rubber bearing (SMARB). However, studies were conducted ignoring the effect of soil structure interaction (SSI), which significantly alters seismic responses of isolated buildings due to soil flexibility effect. Methods This study aims to assess the optimal seismic performance of a multistoreyed building isolated with SMARB device subjected to recorded earthquakes using particle swarm optimization algorithm to minimize top storey acceleration of building considering the effect of different types of soil, which is modelled using direct method and the soil is considered linear, elastic, massless and homogeneous. The numerical modelling of SMA is done using Graesser-Cozzarelli model and the responses are evaluated by solving dynamic equation of motion of the combined system, which comprises the superstructure, isolator and soil. Results The effect of SSI reduces top storey acceleration and isolator displacement of the isolated building. The top storey acceleration is reduced by 3.1%, 27.8% and 35.8% and isolator displacement is reduced by 15.2%, 24.9% and 32.0% for hard, medium and soft soil, respectively. Negligible residual displacement is obtained for SMARB system considering SSI effects. Conclusion Among the various isolation devices (rubber bearing, lead rubber bearing and SMARB), SMARB performs significantly better and ignoring the effects of soil typology leads to a severe underestimation of the performance of the isolated building.

In geotechnical engineering, soil slopes are crucial in various civil engineering projects, including highways, embankments, dams, and excavations. Understanding the behavior of soil slopes is essential for designing stable and safe structures. Combining different soft computing (SC) models can provide more robust slope stability predictions. This paper employs two hybrid computational algorithms to make accurate slope stability predictions. In this research project, the adaptive neuro fuzzy inference system (ANFIS) model is optimized by two novel meta-heuristic optimization algorithms (MOAs): genetic algorithm (GA) and particle swarm optimization (PSO). To this end, slope inputs are taken from a literature survey consisting of 206 input datasets for the training and testing of models. Eleven statistical indices have been evaluated for assessing the performance of proposed hybrid models, along with evaluating rank analysis. ANFIS, ANFIS-GA, and ANFIS-PSO outcomes from the suggested models have R2 values of 0.6783, 0.7624, 0.7378 during training, 0.6684, 0.8143, and 0.7013 during testing. Also, the ANFIS-GA hybrid model yielded error matrices such as RMSE, MAE, and MSE with values of 0.1217, 0.0912, and 0.0148 in training and 0.12570, 0.0968, and 0.1391 in testing; in contrast, the ANFIS PSO model yielded values of 0.1264, 0.0902, 0.016 in training, and 0.1591, 0.1170, 0.1290 in testing; the ANFIS model yielded values of 0.1345, 0.1127, 0.0172 in training, and 0.1642, 0.1267, 0.1391 in testing. The regression plot was analyzed to compare the predicted value with the actual one. In the present paper, the Metropolis Hastings MCMC sampling method has been introduced to establish the relationship between the inputs, which is slope height (H), slope angle (alpha), cohesion (c), pore water pressure ratio (Ru), unit weight (Upsilon), angle of internal friction (phi), and output reliability of slopes. A sensitivity analysis was also performed to determine which variable affects the reliability of soil slope more. After that, comparing hybrid models with the ANFIS model notified the engineers and researchers that the model best predicts slope failure for extensive observations.

New flood records are being set across the world as precipitation patterns change due to a warming climate. Despite the presence of longstanding water management infrastructure like levees and reservoirs, this rise in flooding has been met with property damage, loss of life, and hundreds of billions in economic impact, suggesting the need for new solutions. In this work, the authors suggest the active management of distributed networks of ponds, wetlands and retention basins that already exist across watersheds for the mitigation of flood damages. As an example of this approach, we investigate optimal control of the gated outlets of 130 such locations within a small watershed using linear programming, genetic algorithms, and particle swarm optimization, with the objective of reducing downstream flow and maximizing basin storage. When compared with passive operation (i.e., no gated outlets) and a uniformly applied active management scheme designed to store water during heavy rainfall, the optimal control techniques (1) reduce the magnitudes of peak flow events by up to 10%, (2) reduce the duration of flood crests for up to several days, and (3) preserve additional storage across the watershed for future rainfall events when compared with active management. Combined, these findings provide both a better understanding of dynamically controlled distributed storage as a flood fighting technique and a springboard for future work aimed at its use for reducing flood impacts.

The undrained shear strength is widely acknowledged as a fundamental mechanical property of soil and is considered a critical engineering parameter. In recent years, researchers have employed various methodologies to evaluate the shear strength of soil under undrained conditions. These methods encompass both numerical analyses and empirical techniques, such as the cone penetration test (CPT), to gain insights into the properties and behavior of soil. However, several of these methods rely on correlation assumptions, which can lead to inconsistent accuracy and precision. The study involved the development of innovative methods using extreme gradient boosting (XGB) to predict the pile set-up component A based on two distinct data sets. The first data set includes average modified cone point bearing capacity (q(t)), average wall friction (f(s)), and effective vertical stress (sigma(vo)), while the second data set comprises plasticity index (PI), soil undrained shear cohesion (S-u), and the over consolidation ratio (OCR). These data sets were utilized to develop XGBoost-based methods for predicting the pile set-up component A. To optimize the internal hyperparameters of the XGBoost model, four optimization algorithms were employed: Particle Swarm Optimization (PSO), Social Spider Optimization (SSO), Arithmetic Optimization Algorithm (AOA), and Sine Cosine Optimization Algorithm (SCOA). The results from the first data set indicate that the XGBoost model optimized using the Arithmetic Optimization Algorithm (XGB - AOA) achieved the highest accuracy, with R2 values of 0.9962 for the training part and 0.9807 for the testing part. The performance of the developed models was further evaluated using the RMSE, MAE, and VAF indices. The results revealed that the XGBoost model optimized using XGBoost - AOA outperformed other models in terms of accuracy, with RMSE, MAE, and VAF values of 0.0078, 0.0015, and 99.6189 for the training part and 0.0141, 0.0112, and 98.0394 for the testing part, respectively. These findings suggest that XGBoost - AOA is the most accurate model for predicting the pile set-up component.

This work introduces an optimal performance model for predicting the unconfined compressive strength (UCS) of lime-stabilized soil using the machine (ensemble tree (ET), Gaussian process regression (GPR), and decision tree (DT), support vector machine (SVM)), and hybrid (relevance vector machine (RVM)) learning computational techniques. The conventional (non-optimized) and hybrid (genetic (GA) and particle swarm algorithm optimized (PSO)) RVM models have been developed and compared with machine learning models. For the first time, UCS of virgin cohesive soil has been used as input variable to predict the UCS of lime-stabilized soil. A database of 371 results of lime-stabilized soil has been compiled from the literature and used to create training, testing, and validation databases. Furthermore, the multicollinearity levels for each input variable, i.e., lime content, UCS of cohesive soil, and curing period, have been determined as weak for the overall database. The performance of built-in models has been measured by three new index performance metrics: the a20-index, the index of scatter (IOS), and the index of agreement (IOA). This research concludes that the weak multicollinearity of input variables affects the performance of the non-optimized RVM models. Also, the ensemble tree has performed better than SVM, DT, and GPR because it consists of the number of trees. The overall performance comparison concludes that the PSO-optimized Laplacian kernel-based RVM model UCS16 outperformed all models with higher a20-index (testing = 67.30, validation = 55.95), IOA (testing = 0.8634, validation = 0.7795), and IOS (testing = 0.2799, validation = 0.3506) and has been recognized as the optimal performance model. ANOVA, Z, and Anderson-darling tests reject the null hypothesis for the present research. The lime content influences the prediction of UCS of lime-stabilized soil. The computational cost and external validation results show the robustness of model UCS16.