The behaviour of the dowel action as a shear transfer mechanism across interfaces and joints has been recognised as an important component of structural analysis. In this paper, a three-dimensional non-linear bond model considering the coupled damage effect of concrete was developed to simulate the mechanism of interaction between reinforced bars and subgrade concrete. The local axis strain of the rebar and the local compressive strain of concrete were chosen as quantitative indicators of the damage effect. In total, 15 specimens were tested to investigate the effect of the bar diameters, concrete strength, pre-tension and concrete cover on the dowel action. A parameter study based on the experimental data was performed to calibrate the damage index of the model. Verification of the model was conducted by comparing the analysis results with available experimental results. The results show that the proposed model gives good predictions for both failure mechanisms of dowel action.

Aims Recent studies have shown that artificial addition of biochar is an effective way to mitigate atmospheric carbon dioxide concentrations. However, it is still unclear how biochar addition influences soil respiration in Phyllostachys edulis forests of subtropical China. Our objectives were to examine the effects of biochar addition on the dynamics of soil respiration, soil temperature, soil moisture, and the cumulative soil carbon emission, and to determine the relationships of soil respiration with soil temperature and moisture. Methods We conducted a two-year biochar addition experiment in a subtropical P. edulis forest from 2014.05 to 2016.04. The study site is located in the Miaoshanwu Nature Reserve in Fuyang district of Hangzhou, Zhejiang Province, in southern China. The biochar addition treatments included: control (CK, no biochar addition), low rate of biochar addition (LB, 5 t•hm-2), medium rate of biochar addition (MB, 10 t•hm-2), and high rate of biochar addition (HB, 20 t•hm-2). Soil respiration was measured by using a LI-8100 soil CO 2 efflux system. Important findings Soil respiration was significantly reduced by biochar addition, and exhibited an apparent seasonal pattern, with the maximum occurring in June or July (except LB in one of the replicated stand) and the minimum in January or February. There were significant differences in soil respiration between the CK and the

The CNN-based crowd counting method uses image pyramid and dense connection to fuse features to solve the problems of multiscale and information loss. However, these operations lead to information redundancy and confusion between crowd and background information. In this paper, we propose a multi-scale guided attention network (MGANet) to solve the above problems. Specifically, the multilayer features of the network are fused by a top-down approach to obtain multiscale information and context information. The attention mechanism is used to guide the acquired features of each layer in space and channel so that the network pays more attention to the crowd in the image, ignores irrelevant information, and further integrates to obtain the final high-quality density map. Besides, we propose a counting loss function combining SSIM Loss, MAE Loss, and MSE Loss to achieve effective network convergence. We experiment on four major datasets and obtain good results. The effectiveness of the network modules is proved by the corresponding ablation experiments. The source code is available at https://github.com/lpfworld/MGANet.

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The most advanced method for crowd counting uses a fully convolutional network that extracts image features and then generates a crowd density map. However, this process often encounters multiscale and contextual loss problems. To address these problems, we propose a multiscale aggregation network (MANet) that includes a feature extraction encoder (FEE) and a density map decoder (DMD). The FEE uses a cascaded scale pyramid network to extract multiscale features and obtains contextual features through dense connections. The DMD uses deconvolution and fusion operations to generate features containing detailed information. These features can be further converted into high-quality density maps to accurately calculate the number of people in a crowd. An empirical comparison using four mainstream datasets (ShanghaiTech, WorldExpo’10, UCF_CC_50, and SmartCity) shows that the proposed method is more effective in terms of the mean absolute error and mean squared error. The source code is available at https://github.com/lpfworld/MANet.