Rehabilitation of a water distribution system using sequential multiobjective optimization models

Identification of the optimal rehabilitation plan for a large water distribution system (WDS) with a substantial number of decision variables is a challenging task, especially when no supercomputer facilities are available. This paper presents an initiative methodology for the rehabilitation of WDS based on three sequential stages of multiobjective optimization models for gradually identifying the best-known Pareto front (PF). A two-objective optimization model is used in the first two stages where the objectives are to minimize rehabilitated infrastructure costs and operational costs. The optimization model in the first stage applies to a skeletonized WDS. The PFs obtained in Stage 1 are further improved in Stage 2 using the same two-objective optimization problem but for the full network. The third stage employs a three-objective optimization model by minimizing the cost of additional pressure reducing valves (PRVs) as the third objective. The suggested methodology was demonstrated through use of a real and large WDS from the literature. Results show the efficiency of the suggested methodology to achieve the optimal solutions for a large WDS in a reasonable computational time. Results also suggest the minimum total costs that will be obtained once maximum leakage reduction is achieved due to maximum possible pipeline rehabilitation without increasing the existing tanks

Predicting Project Success in Residential Building Projects (RBPs) using Artificial Neural Networks (ANNs)

Due to the urban population’s growth and increasing demand for the renewal of old houses, the successful completion of Residential Building Projects (RBPs) has great socioeconomic importance. This study aims to propose a framework to predict the success of RBPs in the construction phase. Therefore, a 3-step method was applied: (1) Identifying and ranking Critical Success Factors (CSFs) involving in RBPs using the Delphi method, (2) Identifying and selecting success criteria and defining the Project Success Index (PSI), and (3) Developing an ANN model to predict the success of RBPs according to the status of CSFs during the construction phase. The model was trained and tested using the data extracted from 121 RBPs in Tehran. The main findings of this study were a prioritized list of most influential success criteria and an efficient ANN model as a Decision Support System (DSS) in RBPs to monitor the projects in advance and take necessary corrective actions. Compared with previous studies on the success assessment of projects, this study is more focused on providing an applicable method for predicting the success of RBPs. Doi: 10.28991/cej-2020-03091612 Full Text: PD

Exploring Critical Success Factors in Urban Housing Projects Using Fuzzy Analytic Network Process

Population growth and increasing trend towards urbanization have caused housing demand to exceed its supply, particularly in urban areas in developing countries. Furthermore, housing industry motivates many subsidiary industries and plays a leading socio-economic role in such countries. Therefore, successful completion of housing projects is of great significance quantitatively and qualitatively.This study aims to propose a framework to evaluate the critical success factors (CSFs) in housing projects considering the interrelationship among factors and criteria. The factors were initially identified through literature review and then refined and categorized using a two-round Delphi method and finally prioritized using fuzzy analytic network process (FANP). To demonstrate the implementation of the proposed model, a case study was carried out on an urban residential building project in Tehran. The framework proposed in this study can be applied as a decision support system for decision makers, project managers and practitioners involved in the housing sector

Stochastic sampling design using a multi-objective genetic algorithm and adaptive neural networks

Copyright © 2009 Elsevier. NOTICE: this is the author’s version of a work that was accepted for publication in Environmental Modelling and Software. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Environmental Modelling and Software, Volume 24 Issue 4 (2009), DOI: 10.1016/j.envsoft.2008.09.013This paper presents a novel multi-objective genetic algorithm (MOGA) based on the NSGA-II algorithm, which uses metamodels to determine optimal sampling locations for installing pressure loggers in a water distribution system (WDS) when parameter uncertainty is considered. The new algorithm combines the multi-objective genetic algorithm with adaptive neural networks (MOGA-ANN) to locate pressure loggers. The purpose of pressure logger installation is to collect data for hydraulic model calibration. Sampling design is formulated as a two-objective optimization problem in this study. The objectives are to maximize the calibrated model accuracy and to minimize the number of sampling devices as a surrogate of sampling design cost. Calibrated model accuracy is defined as the average of normalized traces of model prediction covariance matrices, each of which is constructed from a randomly generated sampling set of calibration parameter values. This method of calculating model accuracy is called the 'full' fitness model. Within the genetic algorithm search process, the full fitness model is progressively replaced with the periodically (re)trained adaptive neural network metamodel where (re)training is done using the data collected by calling the full model. The methodology was first tested on a hypothetical (benchmark) problem to configure the setting requirement. Then the model was applied to a real case study. The results show that significant computational savings can be achieved by using the MOGA-ANN when compared to the approach where MOGA is linked to the full fitness model. When applied to the real case study, optimal solutions identified by MOGA-ANN are obtained 25 times faster than those identified by the full model without significant decrease in the accuracy of the final solution

Stochastic Sampling Design for Water Distribution Model Calibration

Copyright © 2008 International Journal of Civil EngineeringA novel approach to determine optimal sampling locations under parameter uncertainty in a water distribution system (WDS) for the purpose of its hydraulic model calibration is presented. The problem is formulated as a multi-objective optimisation problem under calibration parameter uncertainty. The objectives are to maximise the calibrated model accuracy and to minimise the number of sampling devices as a surrogate of sampling design cost. Model accuracy is defined as the average of normalised traces of model prediction covariance matrices, each of which is constructed from a randomly generated sample of calibration parameter values. To resolve the computational time issue, the optimisation problem is solved using a multi-objective genetic algorithm and adaptive neural networks (MOGA-ANN). The verification of results is done by comparison of the optimal sampling locations obtained using the MOGA-ANN model to the ones obtained using the Monte Carlo Simulation (MCS) method. In the MCS method, an equivalent deterministic sampling design optimisation problem is solved for a number of randomly generated calibration model parameter samples.The results show that significant computational savings can be achieved by using MOGA-ANN compared to the MCS model or the GA model based on all full fitness evaluations without significant decrease in the final solution accuracy

Optimal Rehabilitation Strategy In Water Distribution Systems Considering Reduction In Greenhouse Gas Emissions

Most of water distribution systems (WDS) need rehabilitation due to aging infrastructure leading to decreasing capacity, increasing leakage and consequently low performance of the WDS. However an appropriate strategy including location and time of pipeline rehabilitation in a WDS with respect to a limited budget is the main challenge which has been addressed frequently by researchers and practitioners. On the other hand, selection of appropriate rehabilitation technique and material types is another main issue which has yet to address properly. The latter can affect the environmental impacts of a rehabilitation strategy meeting the challenges of global warming mitigation and consequent climate change. This paper presents a multi-objective optimization model for rehabilitation strategy in WDS addressing the abovementioned criteria mainly focused on greenhouse gas (GHG) emissions either directly from fossil fuel and electricity or indirectly from embodied energy of materials. Thus, the objective functions are to minimise: (1) the total cost of rehabilitation including capital and operational costs; (2) the leakage amount; (3) GHG emissions. The Pareto optimal front containing optimal solutions is determined using Non-dominated Sorting Genetic Algorithm NSGA-II. Decision variables in this optimisation problem are classified into a number of groups as: (1) percentage proportion of each rehabilitation technique each year; (2) material types of new pipeline for rehabilitation each year. Rehabilitation techniques used here includes replacement, rehabilitation and lining, cleaning, pipe duplication. The developed model is demonstrated through its application to a Mahalat WDS located in central part of Iran. The rehabilitation strategy is analysed for a 40 year planning horizon. A number of conventional techniques for selecting pipes for rehabilitation are analysed in this study. The results show that the optimal rehabilitation strategy considering GHG emissions is able to successfully save the total expenses, efficiently decrease the leakage amount from the WDS whilst meeting environmental criteria

A novel approach for water quality management in water distribution systems by multi-objective booster chlorination

Copyright © 2012 International Journal of Civil EngineeringCompared to conventional chlorination methods which apply chlorine at water treatment plant, booster chlorination has almost solved the problems of high dosages of chlorine residuals near water sources and lack of chlorine residuals in the remote points of a water distribution system (WDS). However, control of trihalomethane (THM) formation as a potentially carcinogenic disinfection by-product (DBP) within a WDS has still remained as a water quality problem. This paper presents a two-phase approach of multi-objective booster disinfection in which both chlorine residuals and THM formation are concurrently optimized in a WDS. In the first phase, a booster disinfection system is formulated as a multi-objective optimization problem in which the location of booster stations is determined. The objectives are defined as to maximize the volumetric discharge with appropriate levels of disinfectant residuals throughout all demand nodes and to minimize the total mass of disinfectant applied with a specified number of booster stations. The most frequently selected locations for installing booster disinfection stations are selected for the second phase, in which another two-objective optimization problem is defined. The objectives in the second problem are to minimize the volumetric discharge avoiding THM maximum levels and to maximize the volumetric discharge with standard levels of disinfectant residuals. For each point on the resulted trade-off curve between the water quality objectives optimal scheduling of chlorination injected at each booster station is obtained. Both optimization problems used NSGA-II algorithm as a multi-objective genetic algorithm, coupled with EPANET as a hydraulic simulation model. The optimization problems are tested for different numbers of booster chlorination stations in a real case WDS. As a result, this type of multi-objective optimization model can explicitly give the decision makers the optimal location and scheduling of booster disinfection systems with respect to the trade-off between maximum safe drinking water with allowable chlorine residual levels and minimum adverse DBP levels

Leakage control in water distribution networks by using optimal pressure management: A case study

More than thirty percent of drinking water loss through water distribution systems (WDS) in Iran has made a major concern for Iranian water utilities, which has been located in a semi-arid area. In many cases, invisible leakage in the pipelines of WDS, particularly old WDSs, causes a high proportion of total annual losses. Pressure management is a well known and useful tool for reducing invisible leakage since leak is a pressure dependent function. This paper presents an optimization model for pressure management by optimizing pressure reducing valves (PRV) locations and settings. The PRVs location and setting is formulated here as an optimization problem with: (1) the objective function of maximizing the coverage of end-node users’ pressures in an appropriate range; (2) the objective function of minimizing total leakage in the network. Genetic algorithm is used here as the optimization model, in which EPANET toolkit is used as the simulation engine. The model is applied to a case study in Iran known as Mahalat WDS which has assigned a high value of water loss in the networks. The results show that the system is able to considerably moderate the high pressure values and total value of drinking water which is annually lost

A comparative study of stochastic and deterministic sampling design for model calibration

This paper presents and compares two approaches, stochastic and deterministic sampling design, for the purpose of calibrating water distribution system model. Both approaches use a multi-objective genetic algorithm known as NSGA-II to identify the whole Paretooptimal front of optimal solutions. The relevant objective functions are to maximize the calibrated model accuracy and to minimize the number of sampling devices as a surrogate of sampling design cost. In the deterministic approach, optimal solutions are identified based on the assumed values for calibration parameters. However, the uncertainty of calibration parameters is taken into account in the stochastic approach with some pre-defined probability density functions. Two different stochastic approaches, including noisy fitness function and Monte Carlo simulation, are considered in this study. The efficacy of considering stochastic sampling design rather than deterministic one is assessed by evaluating their objective functions in the simulation of 10000 sampling design problems, each of which is constructed with randomly generated calibration parameters. The stochastic approach is first test on an artificial case study. Then it is applied to a real world water distribution system known as Mahalat model in the central part of Iran. The results of comparison show significant improvements in optimal solutions when using stochastic approaches of sampling design

Leakage management for water distribution system in GIS environment

Proceedings of the 2006 World Environmental and Water Resources Congress, held in Omaha, Nebraska, May 21-25, 2006. Sponsored by the Environmental and Water Resources Institute of ASCE. This collection contains 461 papers covering a wide spectrum of topics important to water resources and environmental engineering professionals. Topics include: irrigation and drainage; urban and natural watershed management; hydrology; sustainable development in water, wastewater and stormwater; hydraulics; structures, waterways and measurement and experimental methods; adaptive management in water and natural resources; planning and management; climate, meteorology and water resources; computational hydraulics; environmental processes; evolutionary computations; ASCE/EWRI standards; education and research; groundwater hydrology, quality, and management; international water resources issues; river and wetlands restoration; and applied research in water, wastewater and stormwater