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How to describe the difference among various combat networks and measure their effectiveness is an important problem in combat analysis. In this paper three basic network models are developed based on the theory of complex networks and a new method is put forward for measuring the network effect of combat SoS. Numerical comparisons of the three combat network models indicate that though integrated joint operations network has the highest networked effects in networked effectiveness and clustering coefficient, but when considering the Average Path Length index, it has the lowest effectiveness. The results also suggest that the degree distribution of integrated joint operations network is scale-free thus it has the highest survivability.

The article proposed the reliability calculation method for tunnel lining design combining Rock-Mechanics model and Monte Carlo finite element method.The reliability calculation model of tunnel lining structure design was established considering tunnel character. The types of the statistical characteristics and distribution of axial force for the big cross section of the loess tunnel structure had been summarized. The study result stated that the method is feasible. It has important theoretical guiding significance to the practice structure reliability design of tunnel and underground engineering.

Two methods were compared to predict a ship’s fuel consumption: the simplified naval architecture method (SNAM) and the deep neural network (DNN) method. The SNAM relied on limited operational data and employed a simplified technique to estimate a ship’s required power by determining its resistance in calm water. Here, the Holtrop–Mennen technique obtained hydrostatic information for each selected voyage, the added resistance in the encountered natural seaways, and the brake power required for each scenario. Additional characteristics, such as efficiency factors, were derived from literature surveys and from assumed working hypotheses. The DNN method comprised multiple fully connected layers with the nonlinear activation function rectified linear unit (ReLU). This machine-learning-based method was trained on more than 12,000 sample voyages, and the tested data were validated against realistic operational data. Our results demonstrated that, for some ship topologies (general cargo and containerships), the physical models predicted more accurately the realistic data than the machine learning approach despite the lack of relevant operational parameters. Nevertheless, the DNN method was generally capable of yielding reasonably accurate predictions of fuel consumption for oil tankers, bulk carriers, and RoRo ships.

2D elasto-plastic finite element method was adopted to analyze the forced state of the surrounding rock and the tunnel lining in every step of the construction project for Fulongping double-decked tunnel. In the analysis,Mohr-Coulombs yield criterion was employed incoporating associated flow law to account for the elasto-plastic characteristics of materials. The tangent stiffness method was adopted to solve the equation of equilibrium, and the stresses which exceeded yielded point were adjusted.