In many arid ecosystems, vegetation frequently occurs in high-cover patches interspersed in a matrix of low plant cover. However, theoretical explanations for shrub patch pattern dynamics along climate gradients remain unclear on a large scale. This context aimed to assess the variance of the Reaumuria soongorica patch structure along the precipitation gradient and the factors that affect patch structure formation in the middle and lower Heihe River Basin (HRB). Field investigations on vegetation patterns and heterogeneity in soil properties were conducted during 2014 and 2015. The results showed that patch height, size and plant-to-patch distance were smaller in high precipitation habitats than in low precipitation sites. Climate, soil and vegetation explained 82.5% of the variance in patch structure. Spatially, R. soongorica shifted from a clumped to a random pattern on the landscape towards the MAP gradient, and heterogeneity in the surface soil properties (the ratio of biological soil crust (BSC) to bare gravels (BG)) determined the R. soongorica population distribution pattern in the middle and lower HRB. A conceptual model, which integrated water availability and plant facilitation and competition effects, was revealed that R. soongorica changed from a flexible water use strategy in high precipitation regions to a consistent water use strategy in low precipitation areas. Our study provides a comprehensive quantification of the variance in shrub patch structure along a precipitation gradient and may improve our understanding of vegetation pattern dynamics in the Gobi Desert under future climate change.
Considering the current disadvantages of present offshore wind turbine foundations, a novel anchor foundation with skirt and branches is proposed, called offshore umbrella suction anchor foundation (USAF). A series of experiments and numerical simulations were performed to explore the bearing capacity of the USAF under various kinds of loading modes. The bearing characteristics and the anchor–soil interactions are described in detail for horizontal static loading, horizontal cyclic loading, and an antidrawing (pullout) test in silty soil. In the static loading test, the load–deflection of the anchor under step loading was analyzed and the normalized curve of the load–deflection was obtained to determine the ultimate horizontal bearing capacity of the anchor under normal working conditions. Under horizontal cyclic loading, the relationship between the plastic cumulative deformation and cyclic number was determined. In addition, the responses of USAF were investigated for a low wave frequency and storm surges. In the drawing test, it was found that a “segmentation phenomenon” occurred during the test. Moreover, a method to identify the maximum antidrawing load of USAF was provided based on dynamic mechanics. The numerical results show that the use of anchor branches and skirt can enhance the bearing performance of USAF to a certain degree. However, the anchor branch has a slight positive influence on the bearing performance improvement. The USAF is not only similar to a stiff short pile, but a rotation occurs. The failure envelope under composite loading (V-M) was obtained and the changes associated with changes in the aspect ratio of the internal compartment were clarified. 相似文献
A constitutive model that captures the material behavior under a wide range of loading conditions is essential for simulating complex boundary value problems. In recent years, some attempts have been made to develop constitutive models for finite element analysis using self‐learning simulation (SelfSim). Self‐learning simulation is an inverse analysis technique that extracts material behavior from some boundary measurements (eg, load and displacement). In the heart of the self‐learning framework is a neural network which is used to train and develop a constitutive model that represents the material behavior. It is generally known that neural networks suffer from a number of drawbacks. This paper utilizes evolutionary polynomial regression (EPR) in the framework of SelfSim within an automation process which is coded in Matlab environment. EPR is a hybrid data mining technique that uses a combination of a genetic algorithm and the least square method to search for mathematical equations to represent the behavior of a system. Two strategies of material modeling have been considered in the SelfSim‐based finite element analysis. These include a total stress‐strain strategy applied to analysis of a truss structure using synthetic measurement data and an incremental stress‐strain strategy applied to simulation of triaxial tests using experimental data. The results show that effective and accurate constitutive models can be developed from the proposed EPR‐based self‐learning finite element method. The EPR‐based self‐learning FEM can provide accurate predictions to engineering problems. The main advantages of using EPR over neural network are highlighted. 相似文献