In this study, two series of physical modeling experiments, with and without a grouting process, were conducted under different grouting pressures to study the effect of compaction grouting on the performance of compaction-grouted soil nails. In addition, a hyperbola-based model was proposed to describe the variation of the pullout forces with and without grouting. Some of the main conclusions drawn are as follows. First, the compaction effect initially influences the mobilized pullout force, but not the final stage of pullout; the large difference between the two series of tests in regard to the pullout force at the initial stage led to the first part of this conclusion. However, the final pullout force results of the tests, both those with and those without grouting, were similar. Second, once the soil condition changes, the compaction effect on the performance of a soil nail depends on the grouting pressure rather than the diameter of the grout bulb. Third, the difference in the soil response (i.e., vertical dilatancy and the vertical and horizontal squeezing effects) derived from the compaction grouting effect will result in the initial difference in the increased rate of the pullout force between the tests with a grouting process and those without. Finally, a hyperbola-based model was proposed to describe the variation of the pullout force of the model tests with and without grouting, through which the pullout force is available of prediction for the given diameter of grout bulb and pullout displacement.
Availability of reliable delineation of urban lands is fundamental to applications such as infrastructure management and urban planning. An accurate semantic segmentation approach can assign each pixel of remotely sensed imagery a reliable ground object class. In this paper, we propose an end-to-end deep learning architecture to perform the pixel-level understanding of high spatial resolution remote sensing images. Both local and global contextual information are considered. The local contexts are learned by the deep residual net, and the multi-scale global contexts are extracted by a pyramid pooling module. These contextual features are concatenated to predict labels for each pixel. In addition, multiple additional losses are proposed to enhance our deep learning network to optimize multi-level features from different resolution images simultaneously. Two public datasets, including Vaihingen and Potsdam datasets, are used to assess the performance of the proposed deep neural network. Comparison with the results from the published state-of-the-art algorithms demonstrates the effectiveness of our approach. 相似文献
Regression-based methods are commonly used for riverine constituent concentration/flux estimation, which is essential for guiding water quality protection practices and environmental decision making. This paper developed a multivariate adaptive regression splines model for estimating riverine constituent concentrations (MARS-EC). The process, interpretability and flexibility of the MARS-EC modelling approach, was demonstrated for total nitrogen in the Patuxent River, a major river input to Chesapeake Bay. Model accuracy and uncertainty of the MARS-EC approach was further analysed using nitrate plus nitrite datasets from eight tributary rivers to Chesapeake Bay. Results showed that the MARS-EC approach integrated the advantages of both parametric and nonparametric regression methods, and model accuracy was demonstrated to be superior to the traditionally used ESTIMATOR model. MARS-EC is flexible and allows consideration of auxiliary variables; the variables and interactions can be selected automatically. MARS-EC does not constrain concentration-predictor curves to be constant but rather is able to identify shifts in these curves from mathematical expressions and visual graphics. The MARS-EC approach provides an effective and complementary tool along with existing approaches for estimating riverine constituent concentrations. 相似文献
Sky surveys represent one of the most important efforts to improve developments in astrophysics,especially when using new photometric bands. We are performing the Stellar Abundance and Galactic Evolution(SAGE) survey with a self-designed SAGE photometric system, which is composed of eight photometric bands. The project mainly aims to study the stellar atmospheric parameters of ～0.5 billion stars in ～12 000 deg2 of the northern sky, which mainly focuses on Galactic astronomy, as well as some aspects of extragalactic astronomy. This work introduces the detailed data reduction process of the test field NGC 6791, including the data reduction of single-exposure images and stacked multi-exposure images, and properties of the final catalog. 相似文献
