In dynamic analyses of important structures, seismic input may be defined in the form of time series. It is required that the response spectrum of this input time series be compatible with a specified target response spectrum. Time domain spectral matching, which is used to generate spectrum compatible acceleration time series, is investigated in some detail. First, a new, improved wavelet is presented, and the new adjustment wavelet can prevent drifts in the resulting velocity and displacement time series without applying a baseline correction. Next, the analytical solution of the matrix accounting for the cross correlation of each wavelet is given in order to ensure the speed of the matching procedure. Finally, some aspects, such as the reduction factors and the matching order, are discussed to ensure the stability and efficiency of the matching procedure. Accordingly, the characteristics of the matching procedure are illustrated by numerical examples.

This paper presents a variational limit equilibrium closed-form solution for the stability of slurry-supported trenches in cohesive soil, frictional cohesive soil, and cohesionless soil. Study results indicate that the critical slip-surface geometry is either planar or log spiral. The normal stress distribution, which indicates the likely depth of a tension crack at the crest over the slip surface, was also obtained as part of the complete solution and as a function of groundwater-table level. The closed-form solution was used to produce stability charts for a practical range of parameters. The charts can be used to conveniently assess the stability of a given slurry trench for preliminary design. The paper gives two different examples for trenches to illustrate the influence of the slurry level on the trench stability. To introduce the analytical solution, this study is limited to a simple slurry trench problem; however, extension of the present framework to deal with complex problems would be straightforward.

Skirted foundations are widely used in offshore and subsea engineering. Previous studies have shown that soil undrained shear strength variability has a notable impact on probabilistic analyses of skirted foundation bearing capacity. This study proposes an efficient machine-learning method to predict the uniaxial bearing capacity factors of skirted foundations under pure horizontal and moment loads, without relying on traditional time-consuming random finite element methods. A two-dimensional convolutional neural network is adopted to capture the potential correlation between soil random fields and bearing capacity factors. The proposed CNN-based model exhibits satisfactory prediction performance with regard to coefficients of variation and scale of fluctuations in two directions. Specifically, coefficient of determination (R2) values exceed 0.97, while root mean square error (RMSE) values remain below 0.13 for the surrogate model. In addition, more than 96% of the predictions are associated with a relative error of 5% or less, providing evidence of the proposed 2D-CNN model’s satisfactory prediction performance.

The vibration of offshore wind turbines caused by external loads is significant, which will cause fatigue damage to offshore wind turbines. Wind load is the main load during the operation period of the wind turbine, and available studies have shown that the external wind field often exhibits certain non‐Gaussian characteristics. This article aims to obtain the fatigue assessment of the monopile foundation of the wind turbine under the non‐Gaussian wind fields. A 5 MW wind turbine is selected in this article, and OpenFAST is applied to simulate the wind load. By comparing the Mises stress time histories of the pile foundation at a different depth, the fatigue analysis of the critical spots of the pile foundation is obtained. In the analysis of fatigue damage, the rain flow counting method is adopted, and the two‐segment S‐N curve is selected to analyze the fatigue life of the critical spots. The results show that, by taking the non‐Gaussian characteristic of the wind field into account, the fatigue life of the monopile foundation decreases. Therefore, attention should be paid to the influence of non‐Gaussian characteristics of wind fields on the fatigue life of monopile‐supported wind turbines.