Assessment of reliability in water distribution networks using entropy based measures

Entropy based expressions for measurement of reliability and redundancy have recently been reported. These measures approach assessment of the reliability of the distribution network from the intrinsic redundancy of the network layout. The paper extends earlier work on entropy functions by including a more explicit statement of the alternate paths available in the network and by recognizing that under certain circumstances, e.g., failure of some part of the network work, an outflow link from a node under normal working condition may become an inflow link to the same node. The measures are assessed by comparison with parameters measuring Nodal Pair Reliability and percentage of flow supplied at adequate pressure for a range of networks and link failure conditions in this networks. The entropy measures are shown to reflect changes in the network reliability, as measured by these two comparative parameters, very well.     

Assessment of reliability in water distribution networks using entropy based measures

Entropy based expressions for measurement of reliability and redundancy have recently been reported. These measures approach assessment of the reliability of the distribution network from the intrinsic redundancy of the network layout. The paper extends earlier work on entropy functions by including a more explicit statement of the alternate paths available in the network and by recognizing that under certain circumstances, e.g., failure of some part of the network work, an outflow link from a node under normal working condition may become an inflow link to the same node. The measures are assessed by comparison with parameters measuring Nodal Pair Reliability and percentage of flow supplied at adequate pressure for a range of networks and link failure conditions in this networks. The entropy measures are shown to reflect changes in the network reliability, as measured by these two comparative parameters, very well.     

利用反投影方法计算陆地有限水体气枪震源激发过程

陆地大容量气枪震源可用于区域尺度地球物理结构及变化的勘测.对气枪震源在有限水体内震源过程的研究,有助于我们深入了解气枪信号机制及气枪信号不同震相产生的成因.然而由于有限水体中大容量气枪激发的复杂物理过程,特别是复杂固液界面的存在,目前却没有较好的理论模型用以描述陆地气枪震源信号的能量传播特征.反投影方法是一种计算震源破裂过程的方法,能在动力学参数有限的情况下得到较优的结果.本文以此提出了一种修改的反投影方法,以用于模拟气枪震源激发过程中的能量分布图像.将气枪的激发分解为五个简化的物理过程,并分别建立数值模型,计算每个模型的理论波场,用作新方法的输入数据.通过对五个数值模型的数据计算发现,该方法可以准确地得到气枪激发后水体中的波场形态的变化,能够分辨出气枪激发过程中的气枪激发、气泡上浮、气泡震荡等过程,刻画能量分布特征,并且发现水听器足够密集与合理布置能达到更好的效果.该方法可以为气枪震源的快速评估及气枪震源的有效应用提供参考.

     

陆地水体气枪震源探测技术回顾与进展

地下介质应力场的状态及其变化监测研究,可促进对地震物理过程的认识和理解.高精度波速测量为微弱的应力变化监测提供了可靠的技术手段,但高重复性的震源是研究地下介质微弱变化的关键.近十几年来的探索和实验表明,陆地水体气枪震源是一种较为理想的重复性人工震源,具有高度可重复、绿色环保和测量精度高等特点.本文对陆地水体气枪震源探测技术的发展历程进行了回顾,介绍和总结了陆地水体气枪震源技术的系统构成、性能特点及相关应用,主要包括在地震前后物理过程、断裂带介质变化和工业开采区生产监测等方面的进展,探讨了目前在推广应用中面临的问题及重点发展方向,为利用陆地水体气枪震源技术开展地下介质动态监测研究提供了佐证.

     

内蒙古地区深部水源瞬变电磁法探测

在干旱少雨,地表水资源紧缺的地区,寻找深部水资源已经成为经济可持续发展首要解决的问题,而如何快速、准确的实现深部水源的定位探测却鲜有文章涉及.本文利用瞬变电磁法在煤矿水文地质勘探中取得的探测经验,通过总结该探测方法的关键性技术,应用拟MT反演技术对探测资料进行解释,在内蒙古酸刺沟煤矿工业广场附近成功探测到两处深部低阻异常区,经过钻探验证,出水量均达到80m3/h,实现了深部水源的定位探测,为该煤矿水源井工程的建设提供了可靠的供水水源,解决了该煤矿生产、生活用水的问题.说明:采用瞬变电磁法探测技术探测深部水源,是一种较为理想且有效的探测方法,可以在地表水资源匮乏的地区推广应用.     

基于模拟退火算法的供水管网抗震优化设计

本文以管网造价为优化目标,管网拓扑结构为优化参数,管网节点最低可靠度为约束条件,建立了供水管网抗震拓扑优化模型。在供水管网功能可靠性分析方法的基础上,结合单元概率重要度分析方法,利用模拟退火算法提出了供水管网的抗震拓扑优化方法,并对一典型供水管网进行了拓扑优化分析。分析表明,对于管网抗震拓扑优化这样一个组合优化问题,模拟退火算法提供了一类很好的途径,以此为基础进行供水管网抗震可靠性优化设计具有很好的效果。     

Quantitative detection of fluid distribution using time-lapse seismic

Although previous seismic monitoring studies have revealed several relationships between seismic responses and changes in reservoir rock properties, the quantitative evaluation of time‐lapse seismic data remains a challenge. In most cases of time‐lapse seismic analysis, fluid and/or pressure changes are detected qualitatively by changes in amplitude strength, traveltime and/or Poisson's ratio. We present the steps for time‐lapse seismic analysis, considering the pressure effect and the saturation scale of fluids. We then demonstrate a deterministic workflow for computing the fluid saturation in a reservoir in order to evaluate time‐lapse seismic data. In this approach, we derive the physical properties of the water‐saturated sandstone reservoir, based on the following inputs: V_P, V_S, ρ and the shale volume from seismic analysis, the average properties of sand grains, and formation‐water properties. Next, by comparing the in‐situ fluid‐saturated properties with the 100% formation‐water‐saturated reservoir properties, we determine the bulk modulus and density of the in‐situ fluid. Solving three simultaneous equations (relating the saturations of water, oil and gas in terms of the bulk modulus, density and the total saturation), we compute the saturation of each fluid. We use a real time‐lapse seismic data set from an oilfield in the North Sea for a case study.     

Estimation of soil water content in watershed using artificial neural networks

Soil water content (SWC) is an important factor in transfer processes between soil and air, contributing to water and energy balances, and quantifying it remains a challenge. This study uses artificial neural networks (ANNs) to analyse spatial and temporal variation of SWC in a Brazilian watershed, based on climate information, soil physical properties and topographic variables. Thirty eight input variables were tested in 200 models. The outputs were compared with 650 gravimetric moisture measurements collected at 26 points (25 field studies). The results show that it is possible to estimate SWC efficiently (Nash-Sutcliffe statistic, NS = 0.77) using topographic data, soil physical properties and rainfall. If only climate information is considered, modelling is less efficient (NS = 0.28). Using many variables does not necessarily improve performance. Alternatively, SWC can be estimated by simplified models using rainfall and topographic maps information, although the performance is less good (NS = 0.65).     

Scaling soil water retention functions using particle-size distribution

The presented study explores the prediction of soil water retention and its variability from soil texture and bulk density measurements, using a physically-based scaling technique. Specifically, the Arya–Paris (AP) physico-empirical model is applied to two soil datasets that are collected from two catchments located in different areas of Southern Italy. Laboratory-measured soil water retention functions are scaled to characterize soil variability. The laboratory-measured and AP-predicted reference water retention functions are compared by evaluating the lognormal distribution of derived scaling factors, relative to the mean reference retention function. Since the scaling theory assumes geometric similitude for the investigated soils, successful application of using particle-size distribution to estimate soil water retention requires separation of soils with different textures, using variance analysis. We conclude that variability in soil water retention can be determined from limited soil water retention data using the scaling approach when combined with particle-size distribution measurements. This method can potentially be used as an effective tool for identifying soil hydrologic response at catchment scales.     

Bayesian approach to contaminant source characterization in water distribution systems: adaptive sampling framework

Bayesian analysis can yield a probabilistic contaminant source characterization conditioned on available sensor data and accounting for system stochastic processes. This paper is based on a previously proposed Markov chain Monte Carlo (MCMC) approach tailored for water distribution systems and incorporating stochastic water demands. The observations can include those from fixed sensors and, the focus of this paper, mobile sensors. Decision makers, such as utility managers, need not wait until new observations are available from an existing sparse network of fixed sensors. This paper addresses a key research question: where is the best location in the network to gather additional measurements so as to maximize the reduction in the source uncertainty? Although this has been done in groundwater management, it has not been well addressed in water distribution networks. In this study, an adaptive framework is proposed to guide the strategic placement of mobile sensors to complement the fixed sensor network. MCMC is the core component of the proposed adaptive framework, while several other pieces are indispensable: Bayesian preposterior analysis, value of information criterion and the search strategy for identifying an optimal location. Such a framework is demonstrated with an illustrative example, where four candidate sampling locations in the small water distribution network are investigated. Use of different value-of-information criteria reveals that while each may lead to different outcomes, they share some common characteristics. The results demonstrate the potential of Bayesian analysis and the MCMC method for contaminant event management.     

On the random error propagation in relatively large networks

Summary A relatively large network is one which covers a large area whereas each observation only involves points within a small local area. For such networks an efficient method is described to establish the lower bounds for the spectral norm of the covariance matrix of the rigorously adjusted co-ordinates. The method utilizes a classification of observations and networks which is thought to be interesting in itself. The weakness of pure angular networks is revealed to stem from the fact that such networks offer little resistance to conformal distortions of the network. A numerical example is given. Asymptotic results for block-type networks are obtained which apply among others to photogrammetric bundle block networks.     

Minimizing the impacts of contaminant intrusion in small water distribution networks through booster chlorination optimization

Contaminant intrusion in a water distribution network (DN) has three basic pre-conditions: source of contaminant (e.g., leaky sewer), a pathway (e.g., water main leaks), and a driving force (e.g., negative pressure). The impact of intrusion can be catastrophic if residual disinfectant (chlorine) is not present. To avoid microbiological water quality failure, higher levels of secondary chlorination doses can be a possible solution, but they can produce disinfectant by-products which lead to taste and odour complaints. This study presents a methodology to identify potential intrusion points in a DN and optimize booster chlorination based on trade-offs among microbiological risk, chemical risk and life-cycle cost for booster chlorination. A point-scoring scheme was developed to identify the potential intrusion points within a DN. It utilized factors such as pollutant source (e.g., sewer characteristics), pollution pathway (water main diameter, length, age, and surrounding soil properties, etc.), consequence of contamination (e.g., population, and land use), and operational factors (e.g., water pressure) integrated through a geographical information system using advanced ArcMap 10 operations. The contaminant intrusion was modelled for E. Coli O156: H7 (a microbiological indicator) using the EPANET-MSX programmer’s toolkit. The quantitative microbial risk assessment and chemical (human health) risk assessment frameworks were adapted to estimate risk potentials. Booster chlorination locations and dosages were selected using a multi-objective genetic algorithm. The methodology was illustrated through a case study on a portion of a municipal DN.     

Error propagation in absolute geodetic networks a continuous approach

Summary Studies of error propagation in geodetic networks of an absolute type have already been carried through by several authors using various mathematical techniques. The geodetic elasticity theory relies on a continuation of the actual, discrete network. The traditional observation and normal equation matrices are substituted by partial differential equations with corresponding boundary conditions. The continuous approach only reflects the global error behaviour opposed to the discrete case, and produces only asymptotic results. An advantage of the method is that we may directly profit from existing mathematical knowledge. The fundamental solution of the partial differential equations acts as a formal covariance function and yields the best linear unbiased estimates for estimable functions of the adjustment parameters. Levelling networks and networks with distance and azimuth measurements are studied in this framework.Invited paper presented at the International Symposium on Optimisation of Design and Computation of Control Networks, Sopron, July 4–8, 1977.     

Intentional contamination of water distribution networks: developing indicators for sensitivity and vulnerability assessments

Performing a comprehensive risk analysis is primordial to ensure a reliable and sustainable water supply. Though the general framework of risk analysis is well established, specific adaptation seems needed for systems such as water distribution networks (WDN). Understanding of vulnerabilities of WDN against deliberate contamination and consumers’ sensitivity against contaminated water use is very vital to inform decision-maker. This paper presents an innovative step-by-step methodology for developing comprehensive indicators to perform sensitivity, vulnerability and criticality analyses in case of absence of early warning system (EWS). The assessment and the aggregation of these indicators with specific fuzzy operators allow identifying the most critical points in a WDN. Intentional intrusion of contaminants at these points can potentially harm both the consumers as well as water infrastructure. The implementation of the developed methodology has been demonstrated through a case study of a French WDN unequipped with sensors.     

巢湖市水源地铜绿微囊藻(Microcystis aeruginosa)藻团粒径时空分布规律

于2011年4-8月按月对巢湖市水源保护湖区不同水深及不同区位的铜绿微囊藻藻团粒径进行抽样调查,利用统计分析方法,归纳了巢湖水源保护湖区铜绿微囊藻藻团粒径的时空分布规律.湖区铜绿微囊藻藻团出现在4月中旬至5月中旬之间,S1与S2点位表层的藻团粒径与中、底层的均存在显著差异.粒径小于200μm的藻团在各水深的分布都比较均匀,没有明显的趋向性;粒径在200~800μm范围内的藻团更易集中在湖水表层;粒径超过800μm的藻团更易集中在湖水底层.各月份外湖区S2点位藻团粒径水平均高于内湖湾S1点位.由于易受短时气象条件的影响,藻团粒径按月时间尺度变化的规律性不强.藻团形状在整个空间分布上没有显著差异性,但随着季节变化,逐渐由狭长形向规则形变化.藻团粒径的分布范围表明,大型仿生式蓝藻清除设备的过滤筛网对水源湖区铜绿微囊藻藻团的理论过滤效率为99.81%.     

Prediction of the spatial distribution of the modified mercalli intensity using neural networks

This paper presents the development of an adaptive, non-parametric forecast model for the direct prediction of the spatial distribution of the Modified Mercalli Intensity (MMI) corresponding to an earthquake scenario. The model is based on recent advances in neural networks computation, and is constructed through supervised learning using historical earthquake and regional geological data as training sets. A MMI forecast model for moderate earthquakes with magnitudes between 6 and 7 was developed based on data from the Loma Prieta, Coalinga and Morgan Hill earthquakes. For these data sets, the neural networks forecast model is shown to have excellent data synthesis capability; multiple sets of data can be encapsulated by a relatively simple network architecture. Limited comparison of forecasts made by the neural networks model and conventional models demonstrates that improved accuracy can be achieved. Implementation and operational advantages of the neural networks approach such as general input features, minimum preconceived knowledge of the data sets, the ability to learn and to adapt incrementally and the autonomous and automatic synthesis of the structure underlying the data sets, have been illustrated.     

Structural damage detection using the optimal weights of the approximating artificial neural networks

This work presents a novel neural network‐based approach to detect structural damage. The proposed approach comprises two steps. The first step, system identification, involves using neural system identification networks (NSINs) to identify the undamaged and damaged states of a structural system. The partial derivatives of the outputs with respect to the inputs of the NSIN, which identifies the system in a certain undamaged or damaged state, have a negligible variation with different system errors. This loosely defined unique property enables these partial derivatives to quantitatively indicate system damage from the model parameters. The second step, structural damage detection, involves using the neural damage detection network (NDDN) to detect the location and extent of the structural damage. The input to the NDDN is taken as the aforementioned partial derivatives of NSIN, and the output of the NDDN identifies the damage level for each member in the structure. Moreover, SDOF and MDOF examples are presented to demonstrate the feasibility of using the proposed method for damage detection of linear structures. Copyright © 2001 John Wiley & Sons, Ltd.     

Wave propagation in marine sediments and water saturated soils

Summary A unification of the theories of Biot and Weiskopf has been made to form the suitable equations of motion for porous water saturated soils and marine sediments. It has been shown that the velocities of the body waves depend on the direction of propagation. In the vertical direction there are three, one distortional and two dilatational waves. In the horizontal direction there are two dilatational and two distortional waves. Finally, propagation of Love waves and Rayleigh waves have been discussed. Suitable potential functions have been derived to find the frequency equation for Rayleigh waves.