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To evaluate the mechanical property of concrete materials rapidly, a fast prediction model of the concrete equivalent modulus is proposed based on the random aggregate model and scaled boundary finite element method (SBFEM). First, a random aggregate model of meso-concrete is employed to construct the representative volume element (RVE) according to the aggregate content, gradation, shape, etc. Second, the RVE model is transformed to be a grayscale image and stored as a digital matrix. The quadtree mesh is partitioned automatically for simulation by SBFEM. There are only six types of unique subdomains, and the hanging node does not affect the simulation accuracy. The global stiffness matrix can be assembled directly according to the six subdomain stiffness matrices. Finally, the equivalent modulus is predicted by using the numerical homogenization method. Several numerical examples are employed to verify this model, and the results are compared with that of other methods. The result indicates that the proposed model can efficiently determine the equivalent modulus. Furthermore, the effect of the aggregate gradation, shape, porosity, and pore water are studied and analysed in this work. The proposed model is potential and helpful in predicting the mechanical properties of concrete or other composite materials.

The aim of this paper is to investigate the water pressure effects on hydraulic fracture behavior of concrete with wedge-splitting testing under dynamic loading. Five waterproof strain gauges are stuck along the crack path to observe the fracture process during the experiments. Four silicon water pressure sensors successfully measured the water pressure value on concrete face. The results show that the water pressure on crack faces accelerates the crack propagation of the concrete. The critical values of the splitting force decrease 26.7 and 25.6%, respectively, with the external applied water pressure of 0.2 and 0.4 MPa. Moreover, the hydraulic crack propagation speed increases at the beginning and tends to reach a peak value finally. The peak value of cracking speed is 11.08 m/s, which is high. Under dynamic loading, the water fails to fill the crack and only the trapped water interacts with the crack face. The water pressure is mainly a parabolic curve distribution along the crack path and the peak value decreases with the increase in the crack length.

The landing safety of aircraft is of great significance for the normal operation of an aircraft carrier. This paper presents an optimal method for landing routes of aircraft. Owing to the fuzziness of environmental information and human judgment, a decision-making method is introduced to reduce the workload of landing console operators (LCO). Specifically, a route option method for aircraft landing is proposed based on Fuzzy Multi-attribute Group Decision Making (FMAGDM). Firstly, the route option problem is described. Then the essential elements of route option are described. A group decision-making method is proposed, and the most reasonable landing route in current environment is obtained considering the knowledge composition and the weight of each decision maker. Finally, simulations under different environments are conducted. The results indicate that the decision-making method is able to adapt to the environment changes, and determine the most reasonable route for each validation case.

Fractured carbonate reservoirs are significantly developed in the eastern area of the Amu Darya Right Bank. However, their types, distributions, and fracture characteristics remain unclear. This uncertainty complicates reservoir prediction and hampers exploration and development processes. Given the strong correlation between fracture development and productivity, analyzing fractures is crucial. Comprehensive evaluation and prediction methods for fractured reservoirs are essential for advancing the oil and gas industry. Based on core and geological data analyses, it finds that these reservoirs exhibit low porosity and low to ultra-low permeability. By employing conventional logging alongside specialized methods, such as electrical imaging, nuclear magnetic resonance, and far detection logging, fractures and their effectiveness can be identified and evaluated, clarifying the characteristics of reservoir spaces. Constrained by the results from core and logging analyses, seismic single attribute analysis techniques is applied to predict fractures in the HX block of Amu Darya. To mitigate the limitations of single-attribute analysis, utilize a well-supervised BP neural network method for comprehensive fracture prediction. This multi-attribute approach increases the fracture prediction probability from less than 70%–72.7%. By integrating geological understanding and well logging, and considering the influence of lithology and structure on the reservoir, synthesize the fracture prediction results to optimally select favorable areas. [ABSTRACT FROM AUTHOR]