Search

The viscoelastic dampers (VEDs), which can provide both stiffness and damping, have been recently introduced into the field of structural vibration control for seismic enhancement of civil engineering structures. In this study, a kind of high damping acrylic polymer matrix VEDs (HDPVED) is developed independently, and this innovative HDPVED can solve the significant problem of service performance under medium‐high temperature environments, as well as low‐frequency vibration control under earthquake actions for civil engineering structures. To systematically investigate the influencing rules of frequency, temperature, and displacement amplitude on the mechanical properties and damping dissipation performance of HDPVEDs, a series of dynamic mechanical performance tests of the developed HDPVEDs are carried out at different frequencies, temperatures, and displacement amplitudes. The research results show that HDPVED exhibits excellent damping dissipation capability and adaptability under medium‐high temperature environments and low‐frequency excitations. The mechanical properties and energy dissipation performance present a strong correlation with frequency, temperature and displacement amplitude, and there is an obvious coupling effect between the three influencing factors. Based on the macroscopic mechanical property research of HDPVED, the microscopic damping mechanism and microscopic mechanical properties of HDPVED are then investigated. High‐order fractional derivative fraction Voigt and Maxwell model in parallel (FVMP) models are preferred to characterize the combined hyper‐elasticity and viscoelasticity owned by networked molecular chains and free molecular chains. The breaking and reconstruction theory of microphysical bonds is used to assess the effect of packing particles, and the time‐temperature equivalence principle is introduced to assess the effect of temperature. The multi‐scale refinement model is proposed, and the validity and accuracy of this model are verified by testing data of HDPVED. The study results show that the proposed model can accurately describe the effects of frequency, temperature, displacement amplitude, and microstructure on the multi‐scale mechanical properties of HDPVED. It provides a theoretical basis for the multi‐scale design and development of high damping VEDs. [ABSTRACT FROM AUTHOR]

A novel composite column was introduced by integrating a concrete-filled steel tube column encasing a Fiber Reinforced Polymer (FRP) confined ultra high performance concrete (UHPC) core. In this paper, the seismic performance of four SCF-UHPC composite column-to-steel beam connections using external diaphragm joints was experimentally investigated under the quasi-static test. All specimens failed due to the formation of plastic hinges at the beams and the tearing failure on the beam flanges at the plastic hinge zone. The local buckling at the column end and the panel zone was observed for the specimens receiving the higher axial compression ratio. The increase of FRP tube thickness is more effective in improving the ductility and energy dissipation capacity of the joints than increasing the UHPC core diameter. On the contrary, increasing the UHPC core diameter led to better resistance to the shear deformation of the panel zone. The proportion of shear deformation to the total deformation of the panel zone and the strain distributions were investigated to determine the effect of each component. The composite column in the panel zone mainly carried the compression-bending effort, and the external diaphragm mainly took the shear effort. All joints were eventually classified as semi-rigid type joints, according to the initial rotational stiffness. [ABSTRACT FROM AUTHOR]

We propose a Context-aware Feature Transformer Network (CaFTNet), a novel network for human pose estimation. To address the issue of limited modeling of global dependencies in convolutional neural networks, we design the Transformerneck to strengthen the expressive power of features. Transformerneck directly substitutes 3 × 3 convolution in the bottleneck of HRNet with a Contextual Transformer (CoT) block while reducing the complexity of the network. Specifically, the CoT first produces keys with static contextual information through 3 × 3 convolution. Then, relying on query and contextualization keys, dynamic contexts are generated through two concatenated 1 × 1 convolutions. Static and dynamic contexts are eventually fused as an output. Additionally, for multi-scale networks, in order to further refine the features of the fusion output, we propose an Attention Feature Aggregation Module (AFAM). Technically, given an intermediate input, the AFAM successively deduces attention maps along the channel and spatial dimensions. Then, an adaptive refinement module (ARM) is exploited to activate the obtained attention maps. Finally, the input undergoes adaptive feature refinement through multiplication with the activated attention maps. Through the above procedures, our lightweight network provides powerful clues for the detection of keypoints. Experiments are performed on the COCO and MPII datasets. The model achieves a 76.2 AP on the COCO val2017 dataset. Compared to other methods with a CNN as the backbone, CaFTNet has a 72.9% reduced number of parameters. On the MPII dataset, our method uses only 60.7% of the number of parameters, acquiring similar results to other methods with a CNN as the backbone. [ABSTRACT FROM AUTHOR]

Multi-person pose tracking is a challenging task. It requires identifying the human poses in each frame and matching them across time. This task still faces two main challenges. Firstly, sudden camera zooming and drastic pose changes between adjacent frames may result in mismatched poses between them. Secondly, the time relationships modeled by most existing methods provide insufficient information in scenarios with long-term occlusion. In this paper, to address the first challenge, we propagate the bounding boxes of the current frame to the previous frame for pose estimation, and match the estimated results with the previous ones, which we call the Backward Temporal Pose-Matching (BTPM) module. To solve the second challenge, we design an Association Across Multiple Frames (AAMF) module that utilizes long-term temporal relationships to supplement tracking information lost in the previous frames as a Re-identification (Re-id) technique. Specifically, we select keyframes with a fixed step size in the videos and label other frames as general frames. In the keyframes, we use the BTPM module and the AAMF module to perform tracking. In the general frames, we propagate poses in the previous frame to the current frame for pose estimation and association, which we call the Forward Temporal Pose-Matching (FTPM) module. If the pose association fails, the current frame will be set as a keyframe, and tracking will be re-performed. In the PoseTrack 2018 benchmark tests, our method shows significant improvements over the baseline methods, with improvements of 2.1 and 1.1 in mean Average Precision (mAP) and Multi-Object Tracking Accuracy (MOTA), respectively. [ABSTRACT FROM AUTHOR]

To investigate the effects of parameters on the seismic performance of slender T-shaped RC walls subjected to a biaxial seismic action, a numerical model was established using a fiber-based cross-section and displacement-based beam–column element. The axial load ratio, shear span ratio, flange width to web height ratio, concrete strength grade, stirrup ratio, and longitudinal reinforcement ratio were selected for the parametric study, and the effects of these parameters on the performance degradation under biaxial loading were investigated. Furthermore, a sensitivity analysis of various parameters for the decrease was conducted. The results showed that the bearing and deformation capacities under biaxial loading were both decreased, and the total energy consumption was greater than that under uniaxial loading. The impacts of different parameters and loading paths on the decrease extent were significantly different, and the overall reduction was greater in the flange direction than in the web direction. Under the square loading path, the T-shaped wall had the greatest reduction in its seismic performance, followed by the eight-shaped and cruciform loading paths. The changes in the axial load ratio, shear span ratio, and concrete strength significantly affected the performance degradation under biaxial loading. Accordingly, it is recommended to reasonably consider the values of these three parameters in a multidimensional seismic design to maintain safety redundancy. [ABSTRACT FROM AUTHOR]

For improving the seismic isolation effect, traditional rubber isolation bearing provides a smaller horizontal stiffness. However, it is unfavorable for the displacement control of the seismic isolation layer under rare earthquakes. In this paper, an improved lead-core rubber isolation bearing is proposed. The improved isolation bearing can provide a small horizontal stiffness to enhance the seismic isolation effect under small earthquakes. Under large earthquakes, it can provide a large horizontal stiffness to prevent over-limit failure due to excessive displacement. The mechanical properties of the improved isolation bearing were investigated using the finite element method (FEM), and the restoring force model of the improved isolation bearing was established. Based on the FEMA P-58 theory, the earthquake loss assessment in terms of repair cost and casualty indexes was carried out for normal frame structures, normal isolation structures, and improved isolation structures. The results show that the improved isolation bearing maintains a smaller horizontal stiffness before the displacement is limited, giving full play to the isolation performance. After that, the horizontal stiffness of the bearing is enhanced, which can effectively control the displacement of the seismic isolation layer. The lead-core can give full play to the energy dissipation characteristics. Under the four performance levels, the improved isolation structure has the highest safety reserve and the best collapse resistance. The use of improved isolation bearings can reduce the repair cost of the structure and casualties. [ABSTRACT FROM AUTHOR]

Three-dimensional human pose estimation is a hot research topic in the field of computer vision. In recent years, significant progress has been made in estimating 3D human pose from monocular video, but there is still much room for improvement in this task owing to the issues of self-occlusion and depth ambiguity. Some previous work has addressed the above problems by investigating spatio-temporal relationships and has made great progress. Based on this, we further explored the spatio-temporal relationship and propose a new method, called STFormer. Our whole framework consists of two main stages: (1) extract features independently from the temporal and spatial domains; (2) modeling the communication of information across domains. The temporal dependencies were injected into the spatial domain to dynamically modify the spatial structure relationships between joints. Then, the results were used to refine the temporal features. After the preceding steps, both spatial and temporal features were strengthened, and the estimated final pose will be more precise. We conducted substantial experiments on a well-known dataset (Human3.6), and the results indicated that STFormer outperformed recent methods with an input of nine frames. Compared to PoseFormer, the performance of our method reduced the MPJPE by 2.1%. Furthermore, we performed numerous ablation studies to analyze and prove the validity of the various constituent modules of STFormer. [ABSTRACT FROM AUTHOR]