The jacket substructure is a critical component of the offshore wind turbine (OWT) that is the interface between the transition piece at the top and the grouted connection. This paper presents a comprehensive study on the optimization of a jacket substructure to achieve greater cost efficiency while maintain acceptable structural performance. A fast parametric finite element modelling (FEM) approach for jacket substructures was firstly proposed. The generated models took into account realistic loading conditions, including self-weight, wind load and sectiondependent wave load, and soil-pile interaction. Parametric studies were conducted afterwards to investigate the trends of the mass and response of the jacket substructure with respect to the variation of geometric and sectional parameters. Optimizations of the jacket substructure were carried out using parametric optimization and numerical genetic algorithm (GA) optimization under three different optimization strategies corresponding to three groups of objective and constraint functions. The trends obtained by parametric analysis were used to guide the parameter selection in parametric optimization, while a rank-based mutation GA was established with the proposed efficient FEM embedded in as the solver to the optimization objective and constraint functions. Parametric optimization gained its advantage in computational efficiency, and the mass reduction were 6.2%, 10% and 14.8% for the three strategies respectively. GA optimization was more aggressive as the mass reductions were 16.8%, 22.3% and 34.3% for the three strategies, but was relatively more computational intense. The two proposed optimization methods and the three optimization strategies are both expected to be applied in practical engineering design of OWT jacket substructure with good optimization output and high computational efficiency.

Thermal conductivity is a key soil property widely used for agricultural production, land surface processing research, and geothermal resource development, among others. Although the rapid and accurate determination of soil thermal conductivity (A) has been a hot topic in recent years, there is still no unified model for the different soil types of soil. Furthermore, the lack of data on thermal conductivity and soil properties leads to errors in parametric models of thermal conductivity. In order to overcome the data shortage, a comprehensive lambda dataset of 2972 items was established and 10 influential parameters on thermal conductivity were identified in this study. Based on this, an empirical comparison was made between four classical parametric models and nine machinelearning models with and without an intelligent optimization algorithm was carried out. Of all the methods, the ensemble machine-learning methods perform better in lambda simulations. The XGBoost model has better simulation accuracy and generalization capability. Soil moisture properties are the key parameters in performing lambda simulations, while the soil texture-related properties such as bulk density and solid thermal conductivity, along with the sand content, also play an important role. The results of this study can provide basic thermal conductivity data and a parameterization scheme for referencing in land surface processing research.