Identification of physical processes inherent in artificial neural network rainfall runoff models

The emergence of artificial neural network (ANN) technology has provided many promising results in the field of hydrology and water resources simulation. However, one of the major criticisms of ANN hydrologic models is that they do not consider/explain the underlying physical processes in a watershed, resulting in them being labelled as black‐box models. This paper discusses a research study conducted in order to examine whether or not the physical processes in a watershed are inherent in a trained ANN rainfall‐runoff model. The investigation is based on analysing definite statistical measures of strength of relationship between the disintegrated hidden neuron responses of an ANN model and its input variables, as well as various deterministic components of a conceptual rainfall‐runoff model. The approach is illustrated by presenting a case study for the Kentucky River watershed. The results suggest that the distributed structure of the ANN is able to capture certain physical behaviour of the rainfall‐runoff process. The results demonstrate that the hidden neurons in the ANN rainfall‐runoff model approximate various components of the hydrologic system, such as infiltration, base flow, and delayed and quick surface flow, etc., and represent the rising limb and different portions of the falling limb of a flow hydrograph. Copyright © 2004 John Wiley & Sons, Ltd.     

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。     

基于神经网络的平底结构砰击压力预报

对利用神经网络预报平底结构入水砰击压力的方法进行了探讨。首先利用仿真软件计算各种情况下平底结构入水所产生的砰击压力，以此形成训练神经网络的数据集。其次利用数据集对三层反馈式网络进行了训练，讨论了不同隐含层节点数对该非线性系统的拟合能力，并且对梯度下降法、动量修正法和基于优化的LM算法的有效性和精度进行了比较，最后得出了适合平底结构入水砰击系统的网络结构。     

一种基于异步串行口的IBM-PC局部网

在综合的科学考察船上以异步通讯口实现的IBM-PC局部网具有较好的性能价格比。本文介绍了该网的联网环境、网络系统的组成及其拓扑结构、网络的服务功能以及网络软、硬件的安装和配置。     

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。     

基于AHP和BP神经网络的深部地热水可持续开发能力评价

采用资料完整性、开采潜力、回灌量、平均水压下降速率、地面沉降速率、水温、水质、地热井布局8项指标构建天津地热可持续开发能力评价指标体系;运用层次分析法确定了各项指标的权重,建立起评价因素集和评语集,给出了归一化数值;建立了天津地热可持续开发能力的BP神经网络模型,以层次分析法得出的结果作为样本,对BP网络进行了训练和测试,实例评价结果表明了AHP和BP神经网络方法的可行性,为地热资源的可持续开发能力评价提供了一种新的评价方法。