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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
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Reliable quantification of savanna vegetation structure is critical for accurate carbon accounting and biodiversity assessment under changing climate and land-use conditions. Inventories of fine-scale vegetation structural attributes are typically conducted from field-based plots or transects, while large-area monitoring relies on a combination of airborne and satellite remote sensing. Both of these approaches have their strengths and limitations, but terrestrial laser scanning (TLS) has emerged as the benchmark for vegetation structural parameterization – recording and quantifying 3D structural detail that is not possible from manual field-based or airborne/spaceborne methods. However, traditional TLS approaches suffer from similar spatial constraints as field-based inventories. Given their small areal coverage, standard TLS plots may fail to capture the heterogeneity of landscapes in which they are embedded. Here we test the potential of long-range (>2000 m) terrestrial laser scanning (LR-TLS) to provide rapid and robust assessment of savanna vegetation 3D structure at hillslope scales. We used LR-TLS to sample entire savanna hillslopes from topographic vantage points and collected coincident plot-scale (1 ha) TLS scans at increasing distances from the LR-TLS station. We merged multiple TLS scans at the plot scale to provide the reference structure, and evaluated how 3D metrics derived from LR-TLS deviated from this baseline with increasing distance. Our results show that despite diluted point density and increased beam divergence with distance, LR-TLS can reliably characterize tree height (RMSE = 0.25–1.45 m) and canopy cover (RMSE = 5.67–15.91%) at distances of up to 500 m in open savanna woodlands. When aggregated to the same sampling grain as leading spaceborne vegetation products (10–30 m), our findings show potential for LR-TLS to play a key role in constraining satellite-based structural estimates in savannas over larger areas than traditional TLS sampling can provide. 相似文献
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基于MODIS数据,以湖北省地级以上城市城区为研究对象,通过对湖北省13个地级以上城市城区边界矢量数据与地表温度因子进行套合处理,采用叠置分析方法对2000、2015、2017年湖北省地级以上城市城区的地表温度进行统计,并分析城镇化建设对城区地表温度产生的影响。 相似文献
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利用内蒙古西部12个台站的地脉动噪声数据,采用噪声谱比法研究台站的场地响应情况。分析表明,台站场地响应按曲线形态可分为3类,且可能受地形地貌、局部构造和台基状况等条件影响。对比分析噪声谱比法与Moya方法的场地响应结果发现,场地响应曲线形态基本一致,只有极少数台站存在明显差异。结果表明,内蒙古西部区域大部分台站的台基状况总体较好,场地响应曲线较为平坦,无明显频率放大点。 相似文献
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The Cassini spacecraft, en route to Saturn, passed close to Jupiter while the Galileo spacecraft was completing its 28th and 29th orbits of Jupiter, thus offering a unique opportunity for direct study of the solar wind-Jovian interaction. Here evidence is given of response of the Jovian magnetopause and bow shock positions to changes of the north-south component of the solar wind magnetic field, a phenomenon long known to occur in equivalent circumstances at Earth. The period analyzed starts with the passage over Cassini of an interplanetary shock far upstream of Jupiter. The shock's arrival at Galileo on the dusk-flank of the magnetosphere caused Galileo to exit into the solar wind. Using inter-spacecraft timing based on the time delay established from the shock arrival at each spacecraft, we point out that Galileo's position with respect to the Jovian bow shock appears to correlate with changes in the disturbed north-south reversing field seen behind the shock. We specifically rule out the alternative of changes in the shape of the bow shock with rotations of the interplanetary magnetic field as the cause. 相似文献