Generalized strain probing of constitutive models

Advanced material constitutive models are used to describe complex soil behaviour. These models are often used in the solution of boundary value problems under general loading conditions. Users and developers of constitutive models need to methodically investigate the represented soil response under a wide range of loading conditions. This paper presents a systematic procedure for probing constitutive models. A general incremental strain probe, 6D hyperspherical strain probe (HSP), is introduced to examine rate‐independent model response under all possible strain loading conditions. Two special cases of HSP, the true triaxial strain probe (TTSP) and the plane‐strain strain probe (PSSP), are used to generate 3‐D objects that represent model stress response to probing. The TTSP, PSSP and general HSP procedures are demonstrated using elasto‐plastic models. The objects resulting from the probing procedure readily highlight important model characteristics including anisotropy, yielding, hardening, softening and failure. The PSSP procedure is applied to a Neural Network (NN) based constitutive model. It shows that this probing is especially useful in understanding NN constitutive models, which do not contain explicit functions for yield surface, hardening, or anisotropy. Copyright © 2004 John Wiley & Sons, Ltd.     

Reliability assessment of serviceability performance of braced retaining walls using a neural network approach

In urban environments, one major concern with deep excavations in soft clay is the potentially large ground deformations in and around the excavation. Excessive movements can damage adjacent buildings and utilities. There are many uncertainties associated with the calculation of the ultimate or serviceability performance of a braced excavation system. These include the variabilities of the loadings, geotechnical soil properties, and engineering and geometrical properties of the wall. A risk‐based approach to serviceability performance failure is necessary to incorporate systematically the uncertainties associated with the various design parameters. This paper demonstrates the use of an integrated neural network–reliability method to assess the risk of serviceability failure through the calculation of the reliability index. By first performing a series of parametric studies using the finite element method and then approximating the non‐linear limit state surface (the boundary separating the safe and ‘failure’ domains) through a neural network model, the reliability index can be determined with the aid of a spreadsheet. Two illustrative examples are presented to show how the serviceability performance for braced excavation problems can be assessed using the reliability index. Copyright © 2005 John Wiley & Sons, Ltd.     

LabVIEW设计中压力传感器的RBF神经网络温度补偿

在石油平台注水压力监测系统设计中 ,采用LabVIEW虚拟仪器平台 ,嵌入逼近能力强和收敛速度快的RBF神经网络 ,以人工环境实验数据为样本进行训练 ,实现了压力传感器的智能网络温度补偿。结果显示 ,此方法能够在压力、温度变化较大的恶劣环境下 ,获得很高的补偿精度。     

基于网络药理学探究补阳还五汤治疗颈椎病的作用机制

目的：基于网络药理学与分子对接技术分析补阳还五汤治疗颈椎病的作用机制。方法：使用TCMSP、化学专业数据库获取补阳还五汤的活性成分,并对潜在靶点进行预测及规范。分别从PharmGKB、DisGeNET、OMIM、GeneCards数据库中得到颈椎病疾病靶点,利用韦恩图获取补阳还五汤与颈椎病的交集靶点。通过CytoScape软件构建中药-活性成分-疾病靶点和蛋白质-蛋白质相互作用（PPI）网络,获得核心有效成分与关键靶点,利用David数据库对潜在靶点进行富集分析。最后运用AutoDock Vina软件对补阳还五汤核心有效成分与关键靶点进行分子对接验证。结果：共获得补阳还五汤治疗颈椎病的有效活性成分97个,包括槲皮素、山柰酚、黄芩素、木犀草素等;交集靶点64个,关键靶点有白细胞介素-6（IL-6）、肿瘤坏死因子（TNF）、丝氨酸/苏氨酸蛋白激酶1（AKT1）、白细胞介素-1B（IL-1B）等;主要涉及肿瘤坏死因子（TNF）、白细胞介素-17（IL-17）、磷脂酰肌醇3-激酶（PI3K）/蛋白激酶B（Akt）等信号通路,分子对接结果显示核心有效成分与关键靶点之间结合紧密,为补阳还五汤治疗颈椎病提供相应条件。结论：该研究在总体上预测了补阳还五汤治疗颈椎病的活性成分、靶点和信号通路,作用途径广泛,为下一步的临床应用提供参考及思路。     

Benthic Metabolism and Standing Stock in the Central and Northern Deep Red Sea

Abstract. Benthic metabolism and standing stocks were investigated in the deep Red Sea between 21^o and 27^oN, Activity was assessed by the determination of respiration rates with a shipboard method and by calculating oxygen consumption from the activity in the electron transport system. We attempted to compare results from different latitudes within the warm Red Sea and with data from cold Atlantic environments. Our investigations were part of an environmental risk assessment to evaluate future mining of metalliferous sediments from the Atlantis II Deep.     

烟台市污水处理深海排放工程设计方案研究与选择

沿海城市污水处理深海排放工程在我国起步较晚，烟台市污水处理深海排放工程我国目前规模最大的同类工程，且已进入施工阶段，本文详细阐述了该工程及水工设计方案，在我国城市污水２深海排放工程领域作了积极有益的探索。     

基于GIS的城市交通网络要素可视化表达方法研究

城市交通网络要素的可视化表达是GIS—T研究和应用领域中的一个难题,本文在对目前流行的交通网络数据模型进行系统研究的基础上,选择了动态分段技术下的结点-弧段模型,对交通网络复杂要素进行可视化表达,并对城市交通网络要素的可视化表达方法和形式进行了分析,最后结合交通网络复杂要素的可视化表达,设计和创建了具体的数据结构表和数据结构图。     

区域似大地水准面确定的最小二乘支持向量机方法

支持向量机(SVM)是近年来发展起来的机器学习的新方法,它较好地解决小样本、非线性、高维数、局部极小点等实际问题.文中研究支持向量机的拓展算法--最小二乘支持向量机(LSSVM),并将其应用于确定大面积复杂似大地水准面.通过工程实例并与神经网络模型和二次曲面多项式拟合模型相比较,验证确定区域似大地水准面的LSSVM方法的有效性.     

矿区控制网布网方案的选择

用计算机模拟法,讨论了控制网不同观测量的平差结果,提出了选择矿区控制网方案的原则.     

Artificial neural network and liquefaction susceptibility assessment: a case study using the 2001 Bhuj earthquake data,Gujarat, India

This study pertains to prediction of liquefaction susceptibility of unconsolidated sediments using artificial neural network (ANN) as a prediction model. The backpropagation neural network was trained, tested, and validated with 23 datasets comprising parameters such as cyclic resistance ratio (CRR), cyclic stress ratio (CSR), liquefaction severity index (LSI), and liquefaction sensitivity index (LSeI). The network was also trained to predict the CRR values from LSI, LSeI, and CSR values. The predicted results were comparable with the field data on CRR and liquefaction severity. Thus, this study indicates the potentiality of the ANN technique in mapping the liquefaction susceptibility of the area.