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.