Accurate prediction of peak ground intensity measures is inevitably influenced by geotechnical variability. Variations in soil properties, subsurface conditions, and seismic inputs introduce complexities that challenge the reliability of predictions. This study introduces a Bayesian generalized linear model (GLM) to probabilistically predict peak ground acceleration (PGA) while accounting for uncertainties associated with geotechnical variability. Latin hypercube sampling (LHS) was employed to generate synthetic datasets of key geotechnical parameters, including plasticity index, shear wave velocity, soil thickness, input motion intensity, and unit weight of soil for hypothetical sites. Subsequently, a series of one-dimensional equivalent linear (1D-EQL) seismic site response analyses were performed, and PGA value at ground surface level were recorded for each analysis. The Bayesian GLM was then developed using these comprehensive datasets to probabilistically predict PGA. The performance and reliability of the developed model were evaluated on a separate test dataset. To benchmark its performance, a Bayesian neural network (BNN) was also developed and compared. In addition, a Shiny-based graphical user interface (GUI), named Bayes-PGA-predictor, was implemented to facilitate practical application. The findings demonstrate that the Bayesian GLM offers a robust and interpretable approach to predicting PGA while effectively quantifying uncertainty associated with geotechnical variability.

This study examines the current status and future potential of the offshore wind sector. Offshore wind is pivotal in transitioning to a low-carbon society and meeting rising energy demands, despite being capital-intensive. The industry aims to develop larger-scale wind farms in deeper ocean locations, with projections indicating significant cost reductions. To explore deeper ocean areas, specialized foundations like floating platforms moored to the seabed are required. This study proposes helical piles anchored in the seabed as a method to secure mooring lines. Using Plaxis 3D, a parametric examination was conducted on helical piles with two plates: one fixed at the pile’s toe and the other varying in position between 0.5 and 13 m from the seabed surface. Load inclination angles (0, 20, 40, and 60 degrees) were used to simulate mooring line loads. Results indicate the optimal Zh/Z ratios for maintaining load-bearing capacity and stability: 0.12 (10 mm movements), 0.22 (25 mm), and 0.26 (50 mm) for small shaft diameters; and 0.34 (10 mm), 0.38 (25 mm), and 0.46 (50 mm) for large shaft diameters. These findings highlight the importance of specific load inclination angles based on shaft diameter and allowable movement for effective performance.

Accounting for geotechnical property variability is crucial in seismic site response analysis. Traditionally, the influence of each geotechnical property on response parameters is assessed independently. However, this approach limits our understanding of the combined effects of multiple properties on ground response parameters. This study presents a novel, explainable machine learning (ML)-based approach to assess the influence of multiple geotechnical property variations on response parameters. Four ML models, namely AdaBoost, Extreme Gradient Boosting (XGBoost), Random Forest Regressor (RFR) and Gradient Boosting Machine (GBM), were developed for predictive models. The input factors were shear-wave velocity, plasticity index, soil thickness, input motion intensity and unit weight of the soils. The response parameters were peak ground acceleration (PGA) and peak ground displacement (PGD). Multiple statistical performance metrics were computed to evaluate the performance of the models. The results show the superior prediction performance of the GBM model with low error rates and high agreement index (AI), Kling–Gupta efficiency (KGE) and coefficient of determination (Formula presented.). The output of the GBM model was further analyzed using Shapley Additive exPlanation (SHAP) technique to explain and identify the most significant factors contributing to the predictions. Finally, the model was used to develop user-friendly web-based software to facilitate rapid predictions of PGA and PGD.

The optimal design of riverbank retaining walls requires a careful balance between structural safety, constructability, and economic efficiency. In this study, a constraint-aware optimization framework is developed for the design of concrete gravity retaining walls by explicitly incorporating stability, serviceability, and geometric feasibility constraints. Several metaheuristic algorithms are comparatively evaluated under identical computational conditions using 30 independent runs, a population size of 50, and 1000 iterations. The results demonstrate that enforcing geometric constraints is essential to prevent non-physical designs and to ensure engineering realism. Quantitative analysis shows that the Flower Fertilization Optimization (FFO) algorithm yields the minimum wall weight, reducing material usage by approximately 19% compared to more conservative solutions. In contrast, the adaptive exploration artificial bee colony (AEABC) algorithm exhibits the most robust and repeatable convergence behavior with low statistical dispersion across independent runs. An economic assessment based on concrete volume further confirms the direct impact of material efficiency on construction cost. The proposed framework highlights the importance of constraint-aware optimization for achieving reliable and economically efficient retaining wall designs.