Abstract:The static drill rooted nodular pile is a new type of pile foundation consisting of precast nodular pile and the surrounding cemented soil. This composite pile has a relatively high bearing capacity and the mud pollution will be largely reduced during the construction process by using this type of pile. In order to investigate the bearing capacity and load transfer mechanism of this pile, a group of experiments were conducted to provide a comparison between this new pile and the bored pile. The axial force of a precast nodular pile was also measured by the strain gauges installed on the pile to analyze the distribution of the axial force of the nodular pile and the skin friction supported by the surrounding soil, then 3D models were built by using the ABAQUS finite element program to investigate the load transfer mechanism of this composite pile in detail. By combining the results of field tests and the finite element method, the outcome showed that the bearing capacity of a static drill rooted nodular pile is higher than the bored pile, and that this composite pile will form a double stress dispersion system which will not only confirm the strength of the pile, but also make the skin friction to be fully mobilized. The settlement of this composite pile is mainly controlled by the precast nodular pile; meanwhile, the nodular pile and the surrounding cemented soil can be considered as deformation compatibility during the loading process. The nodes on the nodular pile play an important role during the load transfer process, the shear strength of the interface between the cemented soil and the soil of the static drill rooted pile is larger than that of the bored pile.
In an orchard environment with a complex background and changing light conditions, the banana stalk, fruit, branches, and leaves are very similar in color. The fast and accurate detection and segmentation of a banana stalk are crucial to realize the automatic picking using a banana picking robot. In this paper, a banana stalk segmentation method based on a lightweight multi-feature fusion deep neural network (MFN) is proposed. The proposed network is mainly composed of encoding and decoding networks, in which the sandglass bottleneck design is adopted to alleviate the information a loss in high dimension. In the decoding network, a different sized dilated convolution kernel is used for convolution operation to make the extracted banana stalk features denser. The proposed network is verified by experiments. In the experiments, the detection precision, segmentation accuracy, number of parameters, operation efficiency, and average execution time are used as evaluation metrics, and the proposed network is compared with Resnet_Segnet, Mobilenet_Segnet, and a few other networks. The experimental results show that compared to other networks, the number of network parameters of the proposed network is significantly reduced, the running frame rate is improved, and the average execution time is shortened.
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