Rehabilitation and replacement of water distribution system components considering uncertainties

This paper presents the mathematical development of an integer — nonlinear programming chance — constrained optimization model for the minimum cost rehabilitation/replacement of water distribution system components. Particular attention is given to the handling of uncertainties in the roughness factors and the loading conditions including both the random demand and preassure head requirements.The advantages of the proposed model include the ability to: 1) handle the optimal timing of rehabilitation/replacement for water distribution system components; 2) link a mixed-integer linear program solver, a nonlinear program solver, and a hydraulic simulator into an optimization framework; 3) handle the uncertainties of some of the variables; 4) incorporate various kinds of cost functions; and 5) handle multiple loading conditions.     

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.     

Two-stage fuzzy chance-constrained programming: application to water resources management under dual uncertainties

In this study, a two-stage fuzzy chance-constrained programming (TFCCP) approach is developed for water resources management under dual uncertainties. The concept of distribution with fuzzy probability (DFP) is presented as an extended form for expressing uncertainties. It is expressed as dual uncertainties with both stochastic and fuzzy characteristics. As an improvement upon the conventional inexact linear programming for handling uncertainties in the objective function and constraints, TFCCP has advantages in uncertainty reflection and policy analysis, especially when the input parameters are provided as fuzzy sets, probability distributions and DFPs. TFCCP integrates the two-stage stochastic programming (TSP) and fuzzy chance-constrained programming within a general optimization framework. TFCCP incorporates the pre-regulated water resources management policies directly into its optimization process to analyze various policy scenarios; each scenario has different economic penalty when the promised amounts are not delivered. TFCCP is applied to a water resources management system with three users. Solutions from TFCCP provide desired water allocation patterns, which maximize both the system’s benefits and feasibility. The results indicate that reasonable solutions were generated for objective function values and decision variables, thus a number of decision alternatives can be generated under different levels of stream flows, α-cut levels and fuzzy dominance indices.     

Agricultural effluent control under uncertainty: An inexact double-sided fuzzy chance-constrained model

An inexact double-sided fuzzy chance-constrained programming (IDFCCP) method was developed in this study and applied to an agricultural effluent control management problem. IDFCCP was formulated through incorporating interval linear programming (ILP) into a double-sided fuzzy chance-constrained programming (DFCCP) framework, and could be used to deal with uncertainties expressed as not only possibility distributions associated with both left- and right-hand-side components of constraints but also discrete intervals in the objective function. The study results indicated that IDFCCP allowed violation of system constraints at specified confidence levels, where each confidence level consisted of two reliability scenarios. This could lead to model solutions with high system benefits under acceptable risk magnitudes. Furthermore, the introduction of ILP allowed uncertain information presented as discrete intervals to be communicated into the optimization process, such that a variety of decision alternatives can be generated by adjusting the decision-variable values within their intervals. The proposed model could help decision makers establish various production patterns with cost-effective water quality management schemes under complex uncertainties, and gain in-depth insights into the trade-offs between system economy and reliability.     

Planning of municipal solid waste management systems under dual uncertainties: a hybrid interval stochastic programming approach

In this study, a random-boundary-interval linear programming (RBILP) method is developed and applied to the planning of municipal solid waste (MSW) management under dual uncertainties. In the RBILP model, uncertain inputs presented as interval numbers can be directly communicated into the optimization process; besides, intervals with uncertain lower and upper bounds can be handled through introducing the concept of random boundary interval. Consequently, robustness of the optimization process can be enhanced. To handle uncertainties with such complex presentations, an integrated chance-constrained programming and interval-parameter linear programming approach (ICCP) is proposed. ICCP can help analyze the reliability of satisfying (or risk of violating) system constraints under uncertainty. The applicability of the proposed RBILP and ICCP approach is validated through a case study of MSW management. Violations for capacity constraints are allowed under a range of significant levels. Interval solutions associated with different risk levels of constraint violation are obtained. They can be used for generating decision alternatives and thus helping waste managers to identify desired policies under various environmental, economic, and system-reliability constraints.     

An inexact programming approach for supporting ecologically sustainable water supply with the consideration of uncertain water demand by ecosystems

This paper presents the development and the first application of an inexact quadratic programming (IQP) approach for sustainable water supply under multiple uncertainties. The developed IQP improves conventional nonlinear programming by tackling multiple uncertainties within an individual parameter; IQP is also superior to existing inexact methods due to its reflection of economies of scale and reduction of computational requirements. An interactive solution algorithm with high computational efficiency was also proposed. The application of IQP to long-term planning of a multi-source multi-sector water supply system demonstrated its applicability. The close reflection of system complexities, such as multiple uncertainties, scale economies and dynamic parameters, could enhance the robustness of the optimization process as well as the acceptability of obtained results. Corresponding to varied system conditions and decision priorities, the interval solutions from IQP could help generate a series of long-term water supply strategies under a number of economic, environmental, ecological, and water-security targets.     

Multiobjective fuzzy optimization for sustainable groundwater management using particle swarm optimization and analytic element method

Groundwater management involves conflicting objectives as maximization of discharge contradicts the criteria of minimum pumping cost and minimum piping cost. In addition, available data contains uncertainties such as market fluctuations, variations in water levels of wells and variations of ground water policies. A fuzzy model is to be evolved to tackle the uncertainties, and a multiobjective optimization is to be conducted to simultaneously satisfy the contradicting objectives. Towards this end, a multiobjective fuzzy optimization model is evolved. To get at the upper and lower bounds of the individual objectives, particle Swarm optimization (PSO) is adopted. The analytic element method (AEM) is employed to obtain the operating potentio metric head. In this study, a multiobjective fuzzy optimization model considering three conflicting objectives is developed using PSO and AEM methods for obtaining a sustainable groundwater management policy. The developed model is applied to a case study, and it is demonstrated that the compromise solution satisfies all the objectives with adequate levels of satisfaction. Sensitivity analysis is carried out by varying the parameters, and it is shown that the effect of any such variation is quite significant. Copyright © 2015 John Wiley & Sons, Ltd.     

Water quality management in a wetland system using an inexact left-hand-side chance-constrained fuzzy multi-objective approach

Water quality management is a significant item in the sustainable development of wetland system, since the environmental influences from the economic development are becoming more and more obvious. In this study, an inexact left-hand-side chance-constrained fuzzy multi-objective programming (ILCFMOP) approach was proposed and applied to water quality management in a wetland system to analyze the tradeoffs among multiple objectives of total net benefit, water quality, water resource utilization and water treatment cost. The ILCFMOP integrates interval programming, left-hand-side chance-constrained programming, and fuzzy multi-objective programming within an optimization framework. It can both handle multiple objectives and quantify multiple uncertainties, including fuzziness (aspiration level of objectives), randomness (pollutant release limitation), and interval parameters (e.g. water resources, and wastewater treatment costs). A representative water pollution control case study in a wetland system is employed for demonstration. The optimal schemes were analyzed under scenarios at different probabilities (p _i , denotes the admissible probability of violating the constraint i). The optimal solutions indicated that, most of the objectives would decrease with increasing probability levels from scenarios 1 to 3, since a higher constraint satisfaction probability would lead to stricter decision scopes. This study is the first application of the ILCFMOP model to water quality management in a wetland system, which indicates that it is applicable to other environmental problems under uncertainties.     

Direct differentiation method for response sensitivity analysis of a bounding surface plasticity soil model

Finite element (FE) response sensitivity analysis is an important component in gradient-based structural optimization, reliability analysis, system identification, and FE model updating. In this paper, the FE response sensitivity analysis methodology based on the direct differentiation method (DDM) is applied to a bounding surface plasticity material model that has been widely used to simulate nonlinear soil behavior under static and dynamic loading conditions. The DDM-based algorithm is derived and implemented in the general-purpose nonlinear finite element analysis program OpenSees. The algorithm is validated through simulation of the nonlinear cyclic response of a soil element and a liquefiable soil site at Port Island, Japan, under earthquake loading. The response sensitivity results are compared and validated with those obtained from Forward Finite Difference (FFD) analysis. Furthermore, the results are used to determine the relative importance of various soil constitutive parameters to the dynamic response of the system. The DDM-based algorithm is demonstrated to be accurate and efficient in computing the FE response sensitivities, and has great potential in the sensitivity analysis of nonlinear dynamic soil-structure systems.     

Assessment of episodic acidification in Sierra Nevada,California

Monte-Carlo simulations were used to assess the extent of shortterm alkalinity depressions occuring in Sierra Nevada lakes due to acidic deposition events. The Episodic Event Model (EEM) was used to simulate spring snowmelt events. Snow course data, precipitation data and lake acidification surveys were used to derive values for the EEM parameters. Spring snowmelt events were shown to have great impacts on the water quality of Sierran lakes. Lakes are likely to be most affected by the early-spring snowmelt event because the epilimnion depth is at a minimum, which indicates minimum dilution. Under annual average loading conditions, no Sierran lake has been reported as acidic although 29% of the lakes have alkalinities less than 40 µeq/L indicating a sensitivity to acidification. In simulations of early-spring snowmelt events, using present-day acidic loading conditions, it was estimated 79% ± 9% of the lakes would experience shortterm lake alkalinity depressions to levels less than 40 µeq/L. The results provided by the model simulations are valuable in establishing upper and lower limits on the extent of possible episodic acidification to lake-resources-at-risk. The most critical parameters controlling the magnitude of lake alkalinity depressions during snowmelt episodic events are a) the lake area to watershed area ratio — a measure of input loading, and b) the epilimnion volume — a measure of dilution and mixing.     

δ-Form approximating problem for a conjunctive water resource management model

A nonlinear coupled simulation and optimization model is formulated to find the optimal operating policies with a minimal cost for the conjunctive management of hydraulically interacting surface and ground water supplies. The term representing the pumpage cost in the objective function of the management model causes nonlinearity that cannot be put in a quadratic form as in the traditional approach, since drawdown in that term is a function of well pumpage and other parameters. To eliminate the nonlinearity, δ-form approximating model is formulated with a particular emphasis on the concave function of the transformed objective function. The original problem in a nonlinear form is solved by coupling the solver with a random number generator to provide the model with a different initial guess each time. However, the δ-form approximating problem is formulated as a linear mixed integer programming model. Optimal operating policies and the operation costs obtained from both models are found to be in good agreement.     

Optimal annual operation of a water supply and distribution system

A model for determining the optimal operation of Israel's National Water System over 1 year, with monthly time increments, is described. The water system contains the Main National Carrier — some 250 km long from the Kinneret (Sea of Galillee) in the north to the Negev region in the south — and some 25 regional water systems connected to it. Water is taken from the Kinneret and from two aquifers. Water transfers may take place between the National Carrier and regional systems, and between interconnected regional systems.The mathematical model of the system represents its monthly production and transfer capacities. Given the monthly demands which have to be met and the hydraulic constraints the model determines the month-by-month operating plan which minimizes energy costs over the year.The optimization model is formulated as a linear program. This necessitates several types of approximations and linearizations, which are discussed in detail. The optimal operating plan for 1977–78 is compared with the actual operation of that year and conclusions are drawn from the comparison concerning the practicality and adequacy of the model's output and the potential for effective operation and for energy savings.     

An interval-parameter multi-stage stochastic programming model for water resources management under uncertainty

In this study, an interval-parameter multi-stage stochastic linear programming (IMSLP) method has been developed for water resources decision making under uncertainty. The IMSLP is a hybrid methodology of inexact optimization and multi-stage stochastic programming. It has three major advantages in comparison to the other optimization techniques. Firstly, it extends upon the existing multi-stage stochastic programming method by allowing uncertainties expressed as probability density functions and discrete intervals to be effectively incorporated within the optimization framework. Secondly, penalties are exercised with recourse against any infeasibility, which permits in-depth analyses of various policy scenarios that are associated with different levels of economic consequences when the promised water-allocation targets are violated. Thirdly, it cannot only handle uncertainties through constructing a set of scenarios that is representative for the universe of possible outcomes, but also reflect dynamic features of the system conditions through transactions at discrete points in time over the planning horizon. The developed IMSLP method is applied to a hypothetical case study of water resources management. The results are helpful for water resources managers in not only making decisions of water allocation but also gaining insight into the tradeoffs between environmental and economic objectives.     

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.     

A Nonlinear Model of Contaminant Transformations in an Aquatic Environment

A nonlinear model of the decomposition of a multicomponent contaminant is developed with allowance made for the distribution of components over their reactivity. Model application is illustrated by the analysis of published empirical data on organic matter biodegradation in water column and bottom sediments, in an activated-sludge bioreactor, and the biological absorption and enzymatic destruction of organic matter by macrophytes. Photodestruction, chemical destruction, and sedimentation are also considered. The nonlinear model is compared with a first-order reaction equation.__________Translated from Vodnye Resursy, Vol. 32, No. 3, 2005, pp. 322–336.Original Russian Text Copyright © 2005 by Dolgonosov, Gubernatorova.     

An inexact fuzzy-chance-constrained two-stage mixed-integer linear programming approach for flood diversion planning under multiple uncertainties

In this study, an inexact fuzzy-chance-constrained two-stage mixed-integer linear programming (IFCTIP) approach is developed for flood diversion planning under multiple uncertainties. A concept of the distribution with fuzzy boundary interval probability is defined to address multiple uncertainties expressed as integration of intervals, fuzzy sets and probability distributions. IFCTIP integrates the inexact programming, two-stage stochastic programming, integer programming and fuzzy-stochastic programming within a general optimization framework. IFCTIP incorporates the pre-regulated water-diversion policies directly into its optimization process to analyze various policy scenarios; each scenario has different economic penalty when the promised targets are violated. More importantly, it can facilitate dynamic programming for decisions of capacity-expansion planning under fuzzy-stochastic conditions. IFCTIP is applied to a flood management system. Solutions from IFCTIP provide desired flood diversion plans with a minimized system cost and a maximized safety level. The results indicate that reasonable solutions are generated for objective function values and decision variables, thus a number of decision alternatives can be generated under different levels of flood flows.     

Design,simulation, and large‐scale testing of an innovative vibration mitigation device employing essentially nonlinear elastomeric springs

This study proposes an innovative passive vibration mitigation device employing essentially nonlinear elastomeric springs as its most critical component. Essential nonlinearity denotes the absence (or near absence) of a linear component in the stiffness characteristics of these elastomeric springs. These devices were implemented and tested on a large‐scale nine‐story model building structure. The main focus of these devices is to mitigate structural response under impulse‐like and seismic loading when the structure remains elastic. During the design process of the device, numerical simulations, optimizations, and parametric studies of the structure‐device system were performed to obtain stiffness parameters for the devices so that they can maximize the apparent damping of the fundamental mode of the structure. Pyramidal elastomeric springs were employed to physically realize the optimized essentially nonlinear spring components. Component‐level finite element analyses and experiments were conducted to design the nonlinear springs. Finally, shake table tests using impulse‐like and seismic excitation with different loading levels were performed to experimentally evaluate the performance of the device. Experimental results demonstrate that the properly designed devices can mitigate structural vibration responses, including floor acceleration, displacement, and column strain in an effective, rapid, and robust fashion. Comparison between numerical and experimental results verified the computational model of the nonlinear system and provided a comprehensive verification for the proposed device. Copyright © 2014 John Wiley & Sons, Ltd.     

Sensitivity,responsivity, stability and irreversibility as multiple objectives in civil systems

This paper recommends the consideration of sensitivity, stability, risk, and irreversibility as objective functions in water resource management models within the framework of multiobjective analysis. Six major sources of uncertainties and errors in systems modeling are identified. They are associated with the following model characteristics: model structure (topology), model parameters, model scope or focus, data, optimization technique, and human subjectivity. In particular, the major objective of this paper is to set the stage for the development of an analytical and operational multiobjective framework which will provide decision-makers and plamers with alternatives that consider systems' sensitivity, responsivity, stability and irreversibility along with cost and other performance indices as multiple objectives. This type of a framework should have a very wide spectrum of applications in water and related land resources, environmental studies, energy, and others. The Surrogate Worth Trade-off method is proposed for the solution of the resulting multiobjective optimization problem.     

Optimization and capacity expansion of a water distribution system

This paper develops an iterative procedure for capacity expansion studies for water distribution systems. We propose a methodology to analyze an existing water distribution system and identify the potential bottlenecks in the system. Based on the results, capacity expansion alternatives are proposed and evaluated for improving the efficiency of water supply. The methodology includes a network flow based optimization model, four evaluation indices, and a series of evaluation steps. We first use a directed graph to configure the water distribution system into a network. The network flow based model optimizes the water distribution in the system so that different expansion alternatives can be evaluated on a comparable basis. This model lends itself to linear programming (LP) and can be easily solved by a standard LP code. The results from the evaluation tool help to identify the bottlenecks in the water distribution system and provide capacity expansion alternatives. A useful complementary tool for decision making is composed of a series of evaluation steps with the bottleneck findings, capacity expansion alternatives, and the evaluation of results. We apply the proposed methodology to the Tou-Qian River Basin, located in the northern region of Taiwan, to demonstrate its applicability in optimization and capacity expansion studies.     

Dynamic investigation of a hybrid suspension and cable‐stayed bridge

Ambient and forced vibration tests were carried out on the Beauharnois bridge, a unique, 177‐m combined suspension and cable‐stayed structure near Montreal, Canada. A rehabilitation program was completed on the bridge during which the deck was completely rebuilt with an orthotropic slab on two steel trusses. The rehabilitation program also included the addition of two pairs of stay cables on both towers, creating a hybrid suspension system. The paper presents a series of dynamic tests performed to evaluate the dynamic properties and the dynamic amplification factor (DAF) for the rehabilitated bridge. The experimental program involved the measurement of vertical, transverse, and longitudinal acceleration responses of the deck and tower under ambient and controlled traffic loads. Displacement, strain, and integrated acceleration DAFs were computed under different loading conditions. Modal properties were evaluated and used to correlate a three‐dimensional finite element model for the bridge, including non‐linear cable behaviour. The paper discusses the experimental setup as well as the techniques used to evaluate vibration frequencies, mode shapes, and the DAF. Correlation of numerical dynamic properties and experimental results is also presented. Copyright © 2000 John Wiley & Sons, Ltd.