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.
Speckle noise in synthetic-aperture radar (SAR) images severely hinders remote sensing applications; therefore, the appropriate removal of speckle noise is crucial. This paper elaborates on the multilayer perceptron (MLP) neural-network model for SAR image despeckling by using a time series of SAR images. Unlike other filtering methods that use only a single radar intensity image to derive their parameters and filter that single image, this method can be trained using archived images over an area of interest to self-learn the intensity characteristics of image patches and then adaptively determine the weights and thresholds by using a neural network for image despeckling. Several hidden layers are designed for feedforward network training, and back-propagation stochastic gradient descent is adopted to reduce the error between the target output and neural-network output. The parameters in the network are automatically updated in the training process. The greatest advantage of MLP is that once the despeckling parameters are determined, they can be used to process not only new images in the same area but also images in completely different locations. Tests with images from TerraSAR-X in selected areas indicated that MLP shows satisfactory performance with respect to noise reduction and edge preservation. The overall image quality obtained using MLP was markedly higher than that obtained using numerous other filters. In comparison with other recently developed filters, this method yields a slightly higher image quality, and it demonstrates the powerful capabilities of computer learning using SAR images, which indicate the promising prospect of applying MLP to SAR image despeckling. 相似文献
Geotechnical and Geological Engineering - In order to solve the difficulties of creep deformation of surrounding rock in super large section tunnel, taking Letuan tunnel of Binlai expressway, a... 相似文献
