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In order to investigate the construction of column-to-beam joints and to understand the effect of recycled aggregate concrete (RAC) and cross-section of beams and columns on the seismic performance of recycled aggregate concrete-filled light steel tubular frame structure, four new types of assembly joints were proposed in this paper. A low cyclic loading test of six frame specimens was carried out. The failure characteristic, load bearing capacity, hysteresis property, ductility, strength and stiffness degradation, and energy dissipation were analysed. The damage process of the specimen was simulated using the ABAQUS software, and the results agreed well with those obtained from the experiments. The results showed that the construction pathway of the joints exhibited significant influence on the seismic performance of the frame. The proposed reinforced joint (using angle steel and stiffeners) significantly improved the bearing capacity, stiffness and energy dissipation capacity of the recycled aggregate concrete-filled steel tube frame. The seismic performance of the steel frame was improved, while the energy dissipation capacity increased by 635.7% using RAC filled in the steel tubes. Finally, by appropriately increasing the size of cross-section on the beams and columns can improve the bearing capacity, stiffness and ductility of the structure.

Land use change is a considerable representation of mutual effect between human and natural activities. Northern Shanxi is located in the northern agro pastoral ecotone, which the land use pattern changes violently. This paper focuses on the dynamic change characteristics of cultivated land and grassland in the agro pastoral ecotone of northern Shanxi from 2000 to 2015, and explores the main driving factors of their change. It can provide support for land planning and management and relevant policy-making in northern Shanxi and promote regional sustainable development. The results showed that: ➀ There was a close interrelation between the change of cultivated land and grassland I n northern Shanxi, and the whole showed the opposite dynamic change trend. ➁The main land transfer type of cultivated land and grassland is construction land. And grassland is also the main transfer type of cultivated land. And cultivated land contributes greatly to the growth of grassland area. ➂Economic factors are the main driving force which affecting the area change of cultivated land and grassland, followed by the benefit factors of agricultural development, and finally the labor factors. ➃Economic factors can explain the shift of cultivated land and grassland in different districts and counties in northern Shanxi. The benefit factor of cultivated land development plays a negative driving role on cultivated land, the benefit of grassland development plays a promoting role on the growth of grassland area, and the driving force of labor factor is relatively small.

Ground penetrating radar (GPR) is widely used in nondestructive tests of asphalt pavements, but the characteristics of its detection data need to be extracted manually, and it is very difficult to determine the hidden distress efficiently and accurately inside the asphalt pavement. Therefore, to recognize hidden distress inside asphalt pavement intelligently and improve the efficiency and accuracy of GPR for asphalt pavement detection, this research used the convolutional neural network (CNN) algorithm combined with GPR detection technology. Significantly, the image of the dielectric properties distribution of the road was obtained by processing the GPR detection data, and the GPR detection image data set was established. Moreover, this research dealt with the imbalance of the data set by weighting the loss value. Next, this research designed a CNN model with a simple structure, called visual GPR (VGPR), and used the GPR image data set to train, validate, and test the VGPR. Finally, the model was fine-tuned according to the results, and the optimal hyperparameters were selected. The results show that VGPR converges fast in the training process, and no obvious overfitting phenomenon exists. During the test, the weighted average F1 score in the comprehensive metrics reached 99.626%, indicating that VGPR has excellent generalization performance. When VGPR uses a graphics processing unit for computing, the image recognition efficiency can meet the engineering requirements. A comparison of VGPR with other CNN models reveals that when using the same GPR detection image data set, VGPR has the best recognition effect. In summary, the method detailed herein can quickly and accurately identify the hidden distress of the asphalt pavement and provide a guarantee for the maintenance and operation of the expressway. [ABSTRACT FROM AUTHOR]

Vehicle re-identification research under surveillance cameras has yielded impressive results. However, the challenge of unmanned aerial vehicle (UAV)-based vehicle re-identification (ReID) presents a high degree of flexibility, mainly due to complicated shooting angles, occlusions, low discrimination of top–down features, and significant changes in vehicle scales. To address this, we propose a novel dual mixing attention network (DMANet) to extract discriminative features robust to variations in viewpoint. Specifically, we first present a plug-and-play dual mixing attention module (DMAM) to capture pixel-level pairwise relationships and channel dependencies, where DMAM is composed of spatial mixing attention (SMA) and channel mixing attention (CMA). First, the original feature is divided according to the spatial and channel dimensions to obtain multiple subspaces. Then, a learnable weight is applied to capture the dependencies between local features in the mixture space. Finally, the features extracted from all subspaces are aggregated to promote their comprehensive feature interaction. In addition, DMAM can be easily plugged into any depth of the backbone network to improve vehicle recognition. The experimental results show that the proposed structure performs better than the representative method in the UAV-based vehicle ReID. Our code and models will be published publicly. [ABSTRACT FROM AUTHOR]