城市供水管网抗震能力评价研究

供水管网是城市重要的基础设施,为了高效合理地评估现有供水管网的抗震能力,本文从结构安全性角度出发,构建了供水管网综合抗震能力评价指标体系和供水管网综合抗震能力评价模型。综合分析管道各项指标,运用主成分分析法得到指标权重并建立供水管道抗震能力模型,基于图论理论构建了供水管网基础抗震能力评价模型。从物理、社会和时间效应三个维度出发,将管网基础抗震能力通过环境评价修正系数进行修正,构建了精细化的供水管网综合抗震能力评价模型,以此表征管网的易损性,为地震灾害风险评估工作提供支持。通过对深圳市供水管网进行抗震能力评价,将结果与传统震害预测结果对比,说明了评价模型的合理性。     

OPERATING A LIMESTONE AQUIFER AS A RESERVOIR FOR A WATER SUPPLY SYSTEM

Abstract

An aquifer can be used not only as water source but also as a regulating reservoir linked to a water supply system, planning the operation of such reservoirs calls for a good knowledge of the characteristics and limitations of the aquifer, an estimate of its natural replenishment and outflows, as weil as the determination of a programme for pumping and artificial recharge.

A limestone aquifer of karstic nature, heavily exploited and artificially recharged, has been studied recently with respect to its storage capacity and responses to a planned scheme of operations established for the national water supply systems.

The physical characteristics of this aquifer, its inflows, outflows and dynamic behaviour, were first determined by geological and hydrological investigations. The dynamic model obtained was then verified and improved by use of a resistor-capacitor electric analog constructed for this purpose. Later on, several operational alternatives were tested on the same analog. An optimization analysis was performed on a simplified single cell model representing the aquifer system. The methodology of such integrational operation is discussed in light of the results obtained.     

Optimal design of snow stake networks to estimate snow depth in an alpine mountain range

Monitoring and estimation of snow depth in alpine catchments is needed for a proper assessment of management alternatives for water supply in these water resources systems. The distribution of snowpack thickness is usually approached by using field data that come from snow samples collected at a given number of locations that constitute the monitoring network. Optimal design of this network is required to obtain the best possible estimates. Assuming that there is an existing monitoring network, its optimization may imply the selection of an optimal network as a subset of the existing one (if there are no funds to maintain them) or enlarging the existing network by one or more stations (optimal augmentation problem). We propose an optimization procedure that minimizes the total variance in the estimate of snowpack thickness. The novelty of this work is to treat, for the first time, the problem of snow observation network optimization for an entire mountain range rather than for small catchments as done in the previous studies. Taking into account the reduced data available, which is a common problem in many mountain ranges, the importance of a proper design of these observation networks is even larger. Snowpack thickness is estimated by combining regression models to approach the effect of the explanatory variables and kriging techniques to consider the influence of the stakes location. We solve the optimization problems under different hypotheses, studying the impacts of augmentation and reduction, both, one by one and in pairs. We also analyse the sensitivity of results to nonsnow measurements deduced from satellite information. Finally, we design a new optimal network by combining the reduction and augmentation methods. The methodology has been applied to the Sierra Nevada mountain range (southern Spain), where very limited resources are employed to monitor snowfall and where an optimal snow network design could prove critical. An optimal snow observation network is defined by relocating some observation points. It would reduce the estimation variance by around 600 cm² (15%).     

The relationship between runoff rate and lag time and the effects of surface treatments at the plot scale

Abstract

The manner in which both the seasonal and regional variations in storm duration, intensity and inter-storm period manifest in the runoff response of agricultural water supply catchments is investigated. High-resolution rainfall data were analysed for a network of 17 raingauges located across the semiarid (200–500 mm year^?1) agricultural districts of southwest Western Australia. Seasonal variations in mean storm duration, mean rainfall intensity and mean inter-storm period were modelled using simple periodic functions whose parameters were then also regressed with geographic and climatic indices to create spatial fields for each of these statistics. Based on these mean values, a continuous rainfall time series can be synthesized for any location within the region, with the rainfall depth within each storm being downscaled to 5-min time steps using a bounded random cascade model. Runoff from six different catchment surface treatments (“engineered” catchments) was simulated using a conceptual water-balance model, validated using rainfall—runoff data from an experimental field site. The expected yield of the various catchment types at any other location within the study region is then simulated using the above rainfall—runoff model and synthetic rainfall and potential evaporation time series under a range of climatic settings representative of regional climate variation. The resulting coupled model can be used to estimate the catchment area required to yield an acceptable volume of runoff for any location and dam capacity, at a specified reliability level, thus providing a tool for water resource managers to design engineered catchments for water supply. Although the model presented is specific for Western Australia's southwest region, the methodology itself is applicable to other locations.     

Design and Optimization of a Ground Water Monitoring System Using GIS and Multicriteria Decision Analysis

A GIS-based methodology has been developed to design a ground water monitoring system and implemented for a selected area in Mae-Klong River Basin, Thailand. A multicriteria decision-making analysis has been performed to optimize the network system based on major criteria which govern the monitoring network design such as minimization of cost of construction, reduction of kriging standard deviations, etc. The methodology developed in this study is a new approach to designing monitoring networks which can be used for any site considering site-specific aspects. It makes it possible to choose the best monitoring network from various alternatives based on the prioritization of decision factors.     

Global Optimal Design of Ground Water Monitoring Network Using Embedded Kriging

We present a methodology for global optimal design of ground water quality monitoring networks using a linear mixed-integer formulation. The proposed methodology incorporates ordinary kriging (OK) within the decision model formulation for spatial estimation of contaminant concentration values. Different monitoring network design models incorporating concentration estimation error, variance estimation error, mass estimation error, error in locating plume centroid, and spatial coverage of the designed network are developed. A big-M technique is used for reformulating the monitoring network design model to a linear decision model while incorporating different objectives and OK equations. Global optimality of the solutions obtained for the monitoring network design can be ensured due to the linear mixed-integer programming formulations proposed. Performances of the proposed models are evaluated for both field and hypothetical illustrative systems. Evaluation results indicate that the proposed methodology performs satisfactorily. These performance evaluation results demonstrate the potential applicability of the proposed methodology for optimal ground water contaminant monitoring network design.     

基于微粒群算法的供水管网抗震优化设计

以管网年费用折算值为优化目标、管网拓扑结构与管径为优化参数、管网节点最低可靠度为约束条件,建立了供水管网抗震优化设计模型。利用微粒群算法对这一模型进行了求解,该算法以管网作为微粒个体,通过不断地更新微粒的位置来搜索最优的管网结构,直到最后给出优化的管网结构。利用上述方法对一典型供水管网进行了抗震优化设计分析,给出了3种不同节点最低可靠度约束条件下的优化改造方案。     

Stochastic evaluation of the impact of sewer inlets’ hydraulic capacity on urban pluvial flooding

Sewer inlet structures are vital components of urban drainage systems and their operational conditions can largely affect the overall performance of the system. However, their hydraulic behaviour and the way in which it is affected by clogging is often overlooked in urban drainage models, thus leading to misrepresentation of system performance and, in particular, of flooding occurrence. In the present paper, a novel methodology is proposed to stochastically model stormwater urban drainage systems, taking the impact of sewer inlet operational conditions (e.g. clogging due to debris accumulation) on urban pluvial flooding into account. The proposed methodology comprises three main steps: (i) identification of sewer inlets most prone to clogging based upon a spatial analysis of their proximity to trees and evaluation of sewer inlet locations; (ii) Monte Carlo simulation of the capacity of inlets prone to clogging and subsequent simulation of flooding for each sewer inlet capacity scenario, and (iii) delineation of stochastic flood hazard maps. The proposed methodology was demonstrated using as case study design storms as well as two real storm events observed in the city of Coimbra (Portugal), which reportedly led to flooding in different areas of the catchment. The results show that sewer inlet capacity can indeed have a large impact on the occurrence of urban pluvial flooding and that it is essential to account for variations in sewer inlet capacity in urban drainage models. Overall, the stochastic methodology proposed in this study constitutes a useful tool for dealing with uncertainties in sewer inlet operational conditions and, as compared to more traditional deterministic approaches, it allows a more comprehensive assessment of urban pluvial flood hazard, which in turn enables better-informed flood risk assessment and management decisions.     

多目标优化下平原河网引调水改善水环境效果评估

引调水是改善平原河网地区水环境的重要方法之一,通过构建太湖流域走马塘东南片平原河网区一维水动力水质数学模型,研究不同引调水方案对区域水环境改善效果,确定引调水过程中的异质性因子.从决策目标、水质指标、空间指标3个层面综合考虑,构建环境效益与经济效益结合的多目标函数及评价体系,对引调水方案进行评估优选.结果表明:引调水流量较大时,能够在一定程度上改善区域水环境状况,规划方案下引调水5 d后,高锰酸盐指数、氨氮、总磷的平均改善率分别为30.7%、22.2%、26.4%;引调水时,区域河网中不同空间点位、不同水质指标之间的水质改善过程与效果都存在一定异质性;引调水水量、调度模式及污染源分布都会对调水后的河网区水质产生差异性影响;本研究建立的多目标评价体系较现有方法能够有效涵盖引调水中存在的异质性因子,从多个目标层面优选引调水方案,实现水量水质综合优化调控,为平原河网地区水环境长效管理与科学决策提供理论参考.     

A fuzzy approach to reliability based design of storm water drain network

This paper proposes an approach to estimate reliability of a storm water drain (SWD) network in fuzzy framework. It involves: (i) use of proposed fuzzy Monte-Carlo simulation (FMCS) methodology to estimate fuzzy reliability of conduits in the network, (ii) construction of a reliability block diagram (RBD) for the network (system) using suggested guidelines, and (iii) use of the RBD and reliability estimates of the conduits in the network to compute system reliability based on a proposed procedure. In addition, a system reliability based methodology is proposed for design/retrofitting of SWD network by optimization of its conduit dimensions. Conventionally used reliability analysis approaches assume that the cumulative distribution function (CDF) of performance function (marginal safety) of conduits follows Gaussian distribution, which cannot be ensured in the real world scenario. The proposed approach alleviates the need for making such assumptions and can account for linguistic ambiguity in variables defining the performance function. Effectiveness of the proposed approach is demonstrated on a hypothetical SWD network and a real network in Bangalore, India. Comparison of the results obtained from the proposed approach with those from conventional Monte-Carlo simulation (MCS) reliability assessment approach indicated that the estimate of system reliability and conduit reliability are higher with FMCS approach. Consequently, conduit dimensions required to attain required system (network) reliability could be expected to be lower when FMCS approach is used for designing or retrofitting a system.     

Identification of optimal strategies for agricultural nonpoint source management in Ulansuhai Nur watershed of Inner Mongolia,China

In this study, an environmental-friendly modeling system was developed and applied to an agriculture nonpoint source (AGNPS) management in Ulansuhai Nur watershed. In this system, water environmental capacity, credibility-based chance-constrained programming (CCCP), and AGNPS optimization models were integrated into a general modeling framework. It could be used to calculate water environmental capacity of total nitrogen and total phosphorus in Ulansuhai Nur watershed, which could consequentially provide input data for the developed AGNPS optimization model. Also, the inherent uncertainties in estimating water environmental capacities that can be expressed as possibilistic distributions were reflected and addressed based on computational results of three widely used methods. Such uncertainties were consequentially transferred to the proposed CCCP model based on the adoption of multiple credibility satisfactory levels, significantly facilitating objectivity reflection of decision alternatives. The developed modeling system was then applied to Ulansuhai Nur watershed of Inner Mongolia, a semi-arid river basin in northwestern China. Optimal strategies for AGNPS management in Ulansuhai Nur watershed were generated with consideration of the maximum total agricultural income under multiple policy scenarios. The results showed that the total agricultural income would increase with point source pollution being cut down, and would decrease with rising credibility levels, representing decreasing system violation risks. It was indicated that the higher of total nitrogen/phosphorus discharge being less than water environmental capacity of Ulansuhai Nur, the lower the total agriculture incomes. The proposed methods could help decision makers establish various production patterns with cost-effective agriculture nonpoint source management schemes in the basin of Ulansuhai Nur, and gain in-depth insights into the trade-offs between total agricultural incomes and system reliabilities.     

Seismic reliability assessment of urban water networks

We present a framework for the seismic risk assessment of water supply networks, operating in either normal or abnormal conditions. We propose a methodology for assessing the reliability of water pipe networks combining data of past non‐seismic damage and the vulnerability of the network components against seismic loading. Historical data are obtained using records of damages that occur on a daily basis throughout the network and are processed to produce‘survival curves’, depicting their estimated survival rate over time. The fragility of the network components is assessed using the approach suggested in the American Lifelines Alliance guidelines. The network reliability is assessed using graph theory, whereas the system network reliability is calculated using Monte Carlo simulation. The methodology proposed is demonstrated both on a simple, small‐scale, network and also on a real‐scale district metered area from the water network of the city of Limassol, Cyprus. The proposed approach allows the estimation of the probability that the network fails to provide the desired level of service and allows the prioritization of retrofit interventions and of capacity‐upgrade actions pertaining to existing water pipe networks. Copyright © 2013 John Wiley & Sons, Ltd.     

A frequency‐dependent and intensity‐dependent macroelement for reduced order seismic analysis of soil‐structure interacting systems

The computational demand of the soil‐structure interaction analysis for the design and assessment of structures, as well as for the evaluation of their life‐cycle cost and risk exposure, has led the civil engineering community to the development of a variety of methods toward the model order reduction of the coupled soil‐structure dynamic system in earthquake regions. Different approaches have been proposed in the past as computationally efficient alternatives to the conventional finite element model simulation of the complete soil‐structure domain, such as the nonlinear lumped spring, the macroelement method, and the substructure partition method. Yet no approach was capable of capturing simultaneously the frequency‐dependent dynamic properties along with the nonlinear behavior of the condensed segment of the overall soil‐structure system under strong earthquake ground motion, thus generating an imbalance between the modeling refinement achieved for the soil and the structure. To this end, a dual frequency‐dependent and intensity‐dependent expansion of the lumped parameter modeling method is proposed in the current paper, materialized through a multiobjective algorithm, capable of closely approximating the behavior of the nonlinear dynamic system of the condensed segment. This is essentially the extension of an established methodology, also developed by the authors, in the inelastic domain. The efficiency of the proposed methodology is validated for the case of a bridge foundation system, wherein the seismic response is comparatively assessed for both the proposed method and the detailed finite element model. The above expansion is deemed a computationally efficient and reliable method for simultaneously considering the frequency and amplitude dependence of soil‐foundation systems in the framework of nonlinear seismic analysis of soil‐structure interaction systems.     

Multistage scenario-based interval-stochastic programming for planning water resources allocation

In this study, a multistage scenario-based interval-stochastic programming (MSISP) method is developed for water-resources allocation under uncertainty. MSISP improves upon the existing multistage optimization methods with advantages in uncertainty reflection, dynamics facilitation, and risk analysis. It can directly handle uncertainties presented as both interval numbers and probability distributions, and can support the assessment of the reliability of satisfying (or the risk of violating) system constraints within a multistage context. It can also reflect the dynamics of system uncertainties and decision processes under a representative set of scenarios. The developed MSISP method is then applied to a case of water resources management planning within a multi-reservoir system associated with joint probabilities. A range of violation levels for capacity and environment constraints are analyzed under uncertainty. Solutions associated different risk levels of constraint violation have been obtained. They can be used for generating decision alternatives and thus help water managers to identify desired policies under various economic, environmental and system-reliability conditions. Besides, sensitivity analyses demonstrate that the violation of the environmental constraint has a significant effect on the system benefit.     

Genetic algorithm for seismic topology optimization of lifeline network systems

Lifeline systems, such as water distribution and gas supply networks, usually cover large areas. For these systems, seismic design is always a difficult problem because of the complexity of large‐scale networks. In this paper, a topology optimization technology for lifeline networks is established. Firstly, in order to speed up the convergence of optimization process, an element investment importance analysis is carried out to evaluate the importance of components to the lifeline network. Then a topology optimization model is established. The aim of the model is to find the least‐cost network topology while the seismic reliability between the sources and each terminal satisfies prescribed reliability constraints. For this optimization problem, a genetic algorithm, which takes network topologies as the individuals of its population, is used to search for the optimal solutions by suitable operators, including selection, crossover and mutation operators. The capacity of the proposed algorithm is illustrated by its applications to a simple example network consisting of 10 nodes and an actual network with 391 nodes located in a large city of China. Copyright © 2008 John Wiley & Sons, Ltd.     

Optimizing Wellfield Operation in a Variable Power Price Regime

Wellfield management is a multiobjective optimization problem. One important objective has been energy efficiency in terms of minimizing the energy footprint (EFP) of delivered water (MWh/m³). However, power systems in most countries are moving in the direction of deregulated markets and price variability is increasing in many markets because of increased penetration of intermittent renewable power sources. In this context the relevant management objective becomes minimizing the cost of electric energy used for pumping and distribution of groundwater from wells rather than minimizing energy use itself. We estimated EFP of pumped water as a function of wellfield pumping rate (EFP‐Q relationship) for a wellfield in Denmark using a coupled well and pipe network model. This EFP‐Q relationship was subsequently used in a Stochastic Dynamic Programming (SDP) framework to minimize total cost of operating the combined wellfield‐storage‐demand system over the course of a 2‐year planning period based on a time series of observed price on the Danish power market and a deterministic, time‐varying hourly water demand. In the SDP setup, hourly pumping rates are the decision variables. Constraints include storage capacity and hourly water demand fulfilment. The SDP was solved for a baseline situation and for five scenario runs representing different EFP‐Q relationships and different maximum wellfield pumping rates. Savings were quantified as differences in total cost between the scenario and a constant‐rate pumping benchmark. Minor savings up to 10% were found in the baseline scenario, while the scenario with constant EFP and unlimited pumping rate resulted in savings up to 40%. Key factors determining potential cost savings obtained by flexible wellfield operation under a variable power price regime are the shape of the EFP‐Q relationship, the maximum feasible pumping rate and the capacity of available storage facilities.     

An application of Karmarkar's interior-point linear programming algorithm for multi-reservoir operations optimization

Optimization of multi-reservoir systems operations is typically a very large scale optimization problem. The following are the three types of optimization problems solved using linear programming (LP): (i) deterministic optimization for multiple periods involving fine stage intervals, for example, from an hour to a week (ii) implicit stochastic optimization using multiple years of inflow data, and (iii) explicit stochastic optimization using probability distributions of inflow data. Until recently, the revised simplex method has been the most efficient solution method available for solving large scale LP problems. In this paper, we show that an implementation of the Karmarkar's interior-point LP algorithm with a newly developed stopping criterion solves optimization problems of large multi-reservoir operations more efficiently than the simplex method. For example, using a Micro VAX II minicomputer, a 40 year, monthly stage, two-reservoir system optimization problem is solved 7.8 times faster than the advanced simplex code in MINOS 5.0. The advantage of this method is expected to be greater as the size of the problem grows from two reservoirs to multiples of reservoirs. This paper presents the details of the implementation and testing and in addition, some other features of the Karmarkar's algorithm which makes it a valuable optimization tool are illuminated.     

Analyzing urban water supply through an acceptability-index-based interval approach

Incorporation of uncertainties within an urban water supply management system has been a challenging topic for many years. In this study, an acceptability-index-based two-step interval programming (AITIP) model was developed for supporting urban water supply analysis under uncertainty. AITIP improved upon the traditional two-step interval programming (TIP) through incorporating the acceptability level of constraints violation into the optimization framework. A four-layer urban water supply system, including water sources, treatment facilities, reservoirs, and consuming zones, was used to demonstrate the applicability of proposed method. The results indicated that an AITIP model was valuable to help understand the effects of uncertainties related to cost, constraints and decision maker’s judgment in the water supply network, and capable of assisting urban water managers gain an in-depth insight into the tradeoffs between system cost and constraints-violation risk. Compared with TIP, the solutions from AITIP were of lower degree of uncertainty, making it more reliable to identify effective water supply patterns by adjusting decision variable values within their solution intervals. The study is useful in helping urban water managers to identify cost-effective management schemes in light of uncertainties in hydrology, environment, and decisions. The proposed optimization approach is expected to be applicable for a wide variety of water resources management problems.     

An application of Karmarkar's interior-point linear programming algorithm for multi-reservoir operations optimization

Optimization of multi-reservoir systems operations is typically a very large scale optimization problem. The following are the three types of optimization problems solved using linear programming (LP): (i) deterministic optimization for multiple periods involving fine stage intervals, for example, from an hour to a week (ii) implicit stochastic optimization using multiple years of inflow data, and (iii) explicit stochastic optimization using probability distributions of inflow data. Until recently, the revised simplex method has been the most efficient solution method available for solving large scale LP problems. In this paper, we show that an implementation of the Karmarkar's interior-point LP algorithm with a newly developed stopping criterion solves optimization problems of large multi-reservoir operations more efficiently than the simplex method. For example, using a Micro VAX II minicomputer, a 40 year, monthly stage, two-reservoir system optimization problem is solved 7.8 times faster than the advanced simplex code in MINOS 5.0. The advantage of this method is expected to be greater as the size of the problem grows from two reservoirs to multiples of reservoirs. This paper presents the details of the implementation and testing and in addition, some other features of the Karmarkar's algorithm which makes it a valuable optimization tool are illuminated.     

Probabilistic leak detectability assessment via state estimation in water transport networks

Leak detectability or leakage awareness refers to the capability of sensing losses from a water supply system. Several methods exist in the technical literature to tackle this problem, but only few address it with a state estimation approach. The aim of this paper is to present a new methodology that enables probabilistic assessment of the extent to which water loss could be detected using state estimation by only analysing a single hydraulic state, i.e. one time period. Significant leaks are sensed by identifying unusually high normalised state estimation residuals, which can be identified based on the largest normalised residual test. More specifically, the probability of detecting leaks is computed here by working with the multivariate distribution among measurements and estimates to take into account the noisy nature of measurements with an analytical approach rather than with sampling experiments, which are time-consuming. The methodology set out herein also provides a procedure to systematically assess the minimum leak that could be detected in different parts of the network for a specific measurement setting and operating condition. The method has been applied to a water transport network case study to show its potential and to highlight the usefulness of such a tool for practitioners. The limitations of such a methodology are also discussed, including its possible use for on-line leak detection strategies.