Acta Geotechnica - Many civil engineering projects are related to hydromechanical behavior of unsaturated soils over a wide suction range, which was investigated by imposing suctions on clayey silt... 相似文献
Acta Geotechnica - This article presents a new test prototype that leverages the 3D printing technique to create artificial particle assembles to provide auxiliary evidences that supports the... 相似文献
A new low-dimensional parameterization based on principal component analysis (PCA) and convolutional neural networks (CNN) is developed to represent complex geological models. The CNN–PCA method is inspired by recent developments in computer vision using deep learning. CNN–PCA can be viewed as a generalization of an existing optimization-based PCA (O-PCA) method. Both CNN–PCA and O-PCA entail post-processing a PCA model to better honor complex geological features. In CNN–PCA, rather than use a histogram-based regularization as in O-PCA, a new regularization involving a set of metrics for multipoint statistics is introduced. The metrics are based on summary statistics of the nonlinear filter responses of geological models to a pre-trained deep CNN. In addition, in the CNN–PCA formulation presented here, a convolutional neural network is trained as an explicit transform function that can post-process PCA models quickly. CNN–PCA is shown to provide both unconditional and conditional realizations that honor the geological features present in reference SGeMS geostatistical realizations for a binary channelized system. Flow statistics obtained through simulation of random CNN–PCA models closely match results for random SGeMS models for a demanding case in which O-PCA models lead to significant discrepancies. Results for history matching are also presented. In this assessment CNN–PCA is applied with derivative-free optimization, and a subspace randomized maximum likelihood method is used to provide multiple posterior models. Data assimilation and significant uncertainty reduction are achieved for existing wells, and physically reasonable predictions are also obtained for new wells. Finally, the CNN–PCA method is extended to a more complex nonstationary bimodal deltaic fan system, and is shown to provide high-quality realizations for this challenging example.
Based on the global method, an approach is proposed to consider the effect of anchor reinforcement on slope stability, where equilibrium conditions are formulated in terms of the whole slip body. Anchor pre-tension is assumed to be undertaken by the whole slip body instead of individual slices, causing internal force within slope more realistic. Meanwhile, the optimization model for locating the critical slip surface is of weak nonlinearity and easy to solve using the conventional optimization procedures. The effects of anchoring orientation and position are thoroughly investigated, and interesting results are obtained. 相似文献
Hydrological regimes in the Yellow River have changed significantly because of climate change and intensive human interventions. These changes present severe challenges to water resource utilization and ecological development. Variation of run‐off, suspended sediment load (SSL), and eight precipitation indices (P1: 0–12 mm·day?1, P12: 12–25 mm·day?1, P25: 25–50 mm·day?1, P50: P ≥ 50 mm·day?1 and corresponding rainfall day: Pd1, Pd12, Pd25, Pd50 day year?1) in three critical parts of the Yellow River basin (source region: SRYRB, upper reaches: URYRB, middle reaches: MRYRB) were investigated for the period from 1960 to 2015. The results show that run‐off and SSL significantly decreased (P < 0.01) in the URYRB and the MRYRB, whereas their decline in the SRYRB was insignificant (P > 0.05). Moreover, run‐off in the URYRB had one change point in 1987, and SSL in the URYRB as well as run‐off and SSL in the MRYRB had two change points (in the 1970s and the 1990s). Over the same period, only Pd1 and Pd12 in the SRYRB showed significant increasing trends, and an abrupt change appeared in 1981. The optimal precipitation indices for assessing the effects of precipitation on run‐off and SSL in the URYRB and MRYRB were Pd50 and P12, respectively. A double‐mass curve analysis showed that precipitation and human activities contributed to approximately 20% and 80% of the reduction in run‐off, respectively, for both the SRYRB and the MRYRB. However, the contribution rate of precipitation and human activities on SSL reduction was approximately 40% and 60% in the URYRB and 5% and 95% in the MRYRB, respectively. Human activities, primarily soil and water conservation measures and water extraction (diversion), were the main factors (>50%) that reduced the run‐off. However, the dominant driving factors for SSL reduction were soil and water conservation measures and reservoir interception, for which the contribution rate was higher than 70% in the MRYRB. This work strengthens the understanding of hydrological responses to precipitation change and provides a useful reference for regional water resource utilization. 相似文献
The paper relates to a motion planning algorithm for the feed support system of the Five-hundred-meter Aperture Spherical radio Telescope(FAST).To enhance the stability of the feed support system,the start/termination planning segments are adopted with an acceleration and deceleration section.The source switching planning adopts a combination of a line segment and focal segment to realize stable control of the feed support system.Besides,during the observation trajectory,a transition segment which is not used for observation data is planned with a required time.Through an example simulation,a smooth change is realized via the motion planning algorithm and presented in this paper. 相似文献
