“数字湛江”地理空间框架的理论和应用     

黄少衡 《地理空间信息》2013,(4):28-29

以湛江地理空间框架为研究重点,介绍了"数字城市"地理空间基础框架建设的目标、架构和主要内容。建设"数字城市"地理空间框架,将为实现湛江市各级各部门间的地理空间数据共享,消除信息孤岛起到重要作用。  相似文献   

The effect of periodic intermittency on the cyclic behavior of marine sedimentary clay     

Qingqing Zheng  Tangdai Xia  Shaoheng He 《Marine Georesources & Geotechnology》2013,31(8):945-959

Abstract

For subway systems built in coastal areas, the marine sediments are subjected to regular load sequences of waves and intermittencies, resulting in more complex reactions in their cyclic behaviors compared with those under the uniform cyclic loading applied in common studies. This research involved a series of experimental investigations into the undrained behavior of undisturbed saturated marine sedimentary clay subjected to cyclic loading with periodic intermittency considering the initial deviator stress and the conventional uniform cyclic triaxial tests for comparison. The results indicate that periodic intermittency significantly increases cyclic resistance, manifested by weakening of the long-term response and decreases in the number of vibration times required to achieve a steady state. The effect is greater with longer intermittency durations. Furthermore, changes in the pore water volume during cyclic loading were analyzed via nondestructive detection based on nuclear magnetic resonance. A conversion from bound water to free water was observed, referring to vibration magnitude and times. Lastly, the macroscopic results observed in triaxial tests and the microscopic results obtained in the nuclear magnetic resonance test appear to be closely related, indicating that the use of the variation in pore water is an applicable approach to delineate microchanges.  相似文献   

A predictive deep learning framework for path-dependent mechanical behavior of granular materials     

Ma  Gang  Guan  Shaoheng  Wang  Qiao  Feng  Y. T.  Zhou  Wei 《Acta Geotechnica》2022,17(8):3463-3478

As we transition into an era of data generation and collection, empirical summaries in the classical continuum modeling of granular materials cannot take full advantage of the increasingly larger data sets. This work presents a data-driven model for modeling granular materials, with the material data being extracted from discrete element method (DEM) simulations. A long short-term memory (LSTM) network is then employed to learn the mechanical behaviors of granular materials from the material dataset. Particular emphasis is placed on three elements: modification of LSTM unit cell, phase space sampling, and material history parameterization. The LSTM unit cell is modified so that the initial hidden state can be specified as the initial states of granular materials. Massive DEM simulations are performed to consider the effects of particle size distribution, initial density, confining pressure, and loading path on the mechanical behaviors of granular materials. The history-dependency of the granular materials is well represented by the architecture of the LSTM network and internal variable-based history parameterization. We compare the model predictions against DEM simulations to assess the performance of the proposed data-driven model. The results demonstrate that the model can predict the material behaviors of granular materials with different microstructures and initial states and reproduce the material responses under complex nonmonotonic loading paths. This data-driven model exhibits good generalization ability and high prediction accuracy in various situations.

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Vertical groundwater flux estimation from borehole temperature profiles by a numerical model,RFLUX     

Shaoheng Li  Linyao Dong  Jianyao Chen  Rui Li  Zhigang Yang  Zuobing Liang 《水文研究》2019,33(11):1542-1552

We developed a numerical model, RFLUX, which uses the heat tracer method for vertical groundwater flux estimation, and applied it to the Leizhou Peninsula, South China, to provide information to inform local groundwater resource utilization and management. The temperature–depth (TD) profiles of 24 boreholes, along with the observed ground surface temperature (GST) and surface air temperature (SAT) series in recent decades, were collected in this area. Underground TD data demonstrated the capacity to identify groundwater flow patterns, and local GST and SAT data demonstrated a strong correlation with each other over monthly, seasonal, and annual scales. In the RFLUX model, the average GST and SAT series were applied as an upper boundary condition, and a nonlinear initial condition was set using an analytical solution from the literature. The model results of selected TD profiles demonstrated that the annual vertical groundwater flux was about 0.15 m a^?1, which tended to be overestimated if a linear initial condition was used. This model can be easily applied with minor modifications, considering its clear purpose and simplicity.  相似文献