Search

This paper presents a new valve positioning algorithm (VPA) for water network pressure reduction. The proposed algorithm is a graph theory-based algorithm combined with EPANET2.2 to position pressure-reducing valves (PRVs) at the most effective locations, having the most effect on downstream pressure reduction, and then calculates the setpoint for locally controlled valves. Each algorithm solution positions one valve on the edge with the highest-pressure reduction indicator calculated using the depth-first search algorithm. The setpoint is then calculated according to downstream pressure differences to the minimum service pressure to be supplied. The PRV location and setpoint found by the algorithm may be used to install and set locally controlled PRVs or serve as a guide for positioning remote-controlled PRVs. The algorithm is demonstrated on a simple example network using demand-driven analysis (DDA), pressure-driven analysis (PDA), and genetic algorithms (GA) and on a medium and a large example application (DDA). Any number of PRVs may be installed using the algorithm, which returns stable results and very short solution times while maintaining minimum service pressure requirements to the critical consumers. [ABSTRACT FROM AUTHOR]

Reservoir systems are essential for water resources management. The application and development of optimization techniques for optimal reservoir operation is therefore a valuable undertaking. This paper presents a modified firefly algorithm (MFA) and applies it to optimally solve reservoir operation problems. Three well-known benchmark multireservoir operation problems are optimized for energy production. The results of the MFA are compared with results obtained with other mathematical programming approaches, such as linear programming (LP), differential dynamic programming (DDP), and discrete DDP (DDDP), the genetic algorithm (GA), the multicolony ant algorithm (MCAA), the honey-bee mating optimization (HBMO) algorithm, the water cycle algorithm (WCA), the bat algorithm (BA), and the biogeography-based optimization (BBO) algorithm. The MFA was found to be more effective than alternative optimization methods in solving the test problems demonstrating its strong potential to tackle multireservoir operation problems. This paper's results indicate that the MFA differed by 0.01 and 0.79% with the LP global optimal solutions of a continuous four-reservoir problem (CFP) and a continuous 10-reservoir problem (CTP), respectively. The objective function of a discrete four-reservoir problem (DFP) obtained with the MFA is equal to the LP's objective function. This paper demonstrates that the MFA is a competitive optimization method with which to solve a variety of reservoir operation problems. [ABSTRACT FROM AUTHOR]

A noncrossover dither creeping mutation-based genetic algorithm (CMBGA) for pipe network optimization has been developed and is analyzed in this paper. This CMBGA differs from the classic genetic algorithm (GA) optimization in that it does not utilize the crossover operator; instead, it only uses selection and a proposed dither creeping mutation operator. The creeping mutation rate in the proposed dither creeping mutation operator is randomly generated in a range throughout a GA run, rather than being set to a fixed value. In addition, the dither mutation rate is applied at an individual chromosome level rather than at the generation level. The dither creeping mutation probability is set to take values from a small range that is centered about (where = number of decision variables of the optimization problem being considered). This is motivated by the fact that a mutation probability of approximately previously has been demonstrated to be an effective value and is commonly used for the GA. Two case studies are used to investigate the effectiveness of the proposed CMBGA. An objective of this paper is to compare the performance of the proposed CMBGA with four other GA variants and other published results. The results show that the proposed CMBGA exhibits considerable improvement over the considered GA variants, and comparable performance with respect to other previously published results. Two big advantages of the CMBGA are its simplicity and the fact that it requires the tuning of fewer parameters compared with other GA variants. [ABSTRACT FROM AUTHOR]

This paper presents a case study on the application of a dynamic framework for the intelligent control of flooding in the Boise River system in Idaho. This framework couples a robust and numerically efficient hydraulic routing approach with the popular multi-objective nondominated sorting genetic algorithm II (NSGA-II). The novelty of this framework is that it allows for controlled flooding when the conveyance capacity of the river system is exceeded or is about to exceed. Controlled flooding is based on weight factors assigned to each reach of the system, depending on the amount of damage that would occur, should a flood occur. For example, an urban setting would receive a higher weight factor than a rural or agricultural area. The weight factor for a reach does not need to be constant as it can be made a function of the flooding volume (or water stage) in the reach. The optimization algorithm minimizes flood damage by favoring low-weighted floodplain areas (e.g., rural areas) rather than high-weighted areas (e.g., urban areas) for the overbank flows. The proposed framework has the potential to improve water management and use of flood-prone areas in river systems, especially of those systems subjected to frequent flooding. This work is part of a long-term project that aims to develop a reservoir operation model that combines short-term objectives (e.g., flooding) and long-term objectives (e.g., hydropower, irrigation, water supply). The scope of this first paper is limited to the application of the proposed framework to flood control. Results for the Boise River system show a promising outcome in the application of this framework for flood control. [ABSTRACT FROM AUTHOR]

This paper reports the use of many-objective optimization for water distribution system (WDS) design or rehabilitation problems. The term many-objective optimization refers to optimization with four or more objectives. The increase in the number of objectives brings new challenges for both optimization and visualization. This study uses a multiobjective evolutionary algorithm termed the epsilon Nondominated Sorted Genetic Algorithm II (-NSGAII) and interactive visual analytics to reveal and explore the tradeoffs for the Anytown network problem. The many-objective formulation focuses on a suite of six objectives, as follows: (1) capital cost, (2) operating cost, (3) hydraulic failure, (4) leakage, (5) water age, and (6) fire-fighting capacity. These six objectives are optimized based on decisions related to pipe sizing, tank siting, tank sizing, and pump scheduling under five different loading conditions. Solving the many-objective formulation reveals complex tradeoffs that would not be revealed in a lower-dimensional optimization problem. Visual analytics are used to explore these complex tradeoffs and identify solutions that simultaneously improve the overall WDS performance but with reduced capital and operating costs. This paper demonstrates that a many-objective visual analytics approach has clear advantages and benefits in supporting more informed, transparent decision-making in the WDS design process. [ABSTRACT FROM AUTHOR]

Multicropping is the practice of growing two or more crops in the same space during a single growing season. Planning rules are mathematical equations that use previous experiences of a water resource system to balance the system's water supply and demand, and calculate multicrop areas in various periods. In this paper, linear and nonlinear planning rules are developed for optimal multicrop irrigation areas associated with reservoir operation policies in a reservoir-irrigation system. Reservoir operations are related to water allocations to each irrigated area by considering inflow and storage volume of the reservoir as the water supply in a monthly operation period. Evolutionary algorithms (EAs) can determine optimal multicropping patterns planning rules by considering various mathematical patterns. In this paper, three EAs, namely, (1) genetic algorithm (GA), (2) particle swarm optimization (PSO), and (3) shuffled frog leaping algorithm (SFLA) are employed and compared to maximize the total net benefit of the water resource system by supplying irrigation water for a proposed multicropping pattern over the planning horizon. Results show that the SFLA achieves the best solution, with the maximum value of the objective function in both linear and nonlinear planning rules compared to the GA and PSO. Moreover, the best yield of nonlinear rules is 45.52% better (higher) than that obtained by linear rules. [ABSTRACT FROM AUTHOR]

Intensive heuristic and metaheuristic research efforts have focused on the Pareto front optimization of discrete time-cost trade-off problem (DTCTP). However, very little success has been achieved in solving the problem for medium and large-scale projects. This paper presents a new particle swarm optimization method to achieve an advancement in the Pareto front optimization of medium and large-scale construction projects. The proposed Pareto front particle swarm optimizer (PFPSO) is based on a multiobjective optimization environment with novel particle representation, initialization, and position-updating principles that are specifically designed for simultaneous time-cost optimization of large-scale projects. PFPSO brings several benefits for the discrete time-cost optimization, such as an adequate representation of the discrete search space, fast convergence properties, and improved Pareto front optimization capabilities. The computational experiment results reveal that the new particle swarm optimization method outperforms the state-of-the-art methods, both in terms of the number of Pareto front solutions and computation time, especially for medium and large-scale problems. A large number of nondominated solutions are achieved within seconds for the first time, for a problem including 720 activities. The proposed Pareto front particle swarm optimizer provides a fast and effective method for optimal scheduling of construction projects. [ABSTRACT FROM AUTHOR]

In nonlinear construction optimization problems, the capability of current optimization algorithms to find an optimal solution is usually limited by their inability to evaluate the effects of changing the value of each decision variable on reaching the optimal solution. This paper presents fundamental research aimed at developing a novel evolutionary optimization algorithm, named Electimize, that mimics the behavior of electrons flowing, through electric circuit branches with the least electric resistance. In the proposed algorithm, solutions are represented by electric wires and are evaluated on two levels: a global level, using the objective function, and a local level, evaluating the potential of each generated value for every decision variable. The paper presents (1) the research philosophy and scope, (2) the research methodology, and (3) the development of the algorithm. The proposed algorithm has been validated and applied successfully to an NP-hard cash flow optimization problem. The algorithm was able to find a better optimal solution and identified ten alternative optimal solutions for the same problem. This should prove useful in enhancing the optimization of complex large-scale problems. [ABSTRACT FROM AUTHOR]

This paper deals with methods aimed at the effective and efficient detection of accidental and/or intentional contaminant intrusion(s) in water distribution systems. The objective of this paper is to present a methodology entitled sensors local optimal transformation system (SLOTS) to address both single-objective and multiobjective sensor layout problems. SLOTS is tested on two benchmark water distribution networks used for the Battle of the Water Sensors Networks challenge (BWSN), held as part of the Water Distribution Systems Analysis Symposium, in Cincinnati in 2006. The objectives considered are detection likelihood and the expected population affected prior to detection. The results obtained demonstrate that SLOTS sensor placements are often near optimal. For both single-objective and multiobjective cases, SLOTS is shown to be capable of identifying placements which are consistently better performing than one of the best BWSN methodologies, the greedy algorithm. [ABSTRACT FROM AUTHOR]

In this paper, the use of a hybrid evolutionary optimization algorithm is proposed for global optimization of pavement structural parameters through inverse modeling. Shuffled complex evolution (SCE) is a population-based stochastic optimization technique combining the competitive complex evolution with the controlled random search, the implicit clustering, and the complex shuffling. Back-calculation of pavement layer moduli is an ill-posed inverse engineering problem, which involves searching for the optimal combination of pavement layer stiffness solutions in an unsmooth, multimodal, complex search space. SCE is especially considered a robust and efficient approach for global optimization of multimodal functions. A desirable characteristic of the SCE algorithm is that it uses information about the nature of the response surface, extracted using the deterministic Simplex geometric shape, to direct the search into regions with higher posterior probability. The hybrid back-calculation system described in this paper combines the robustness of the SCE in global optimization with the computational efficiency of neural networks and advanced pavement system characterization offered by employing finite-element models. This is the first time the SCE approach is applied to real-time nondestructive evaluation of pavement systems required in the routine maintenance and rehabilitation activities for sustainable transportation infrastructure. [ABSTRACT FROM AUTHOR]

Considerable research has been carried out on the optimization of water distribution systems (WDSs) over the last three decades. In previous research, attention has mainly focused on the minimization of cost, due to the high expenditure associated with the construction and maintenance of such systems. However, the impacts of WDSs on the environment usually have not been considered adequately. The recent increasing awareness of sustainability and climate change, especially global warming, has led to research where greenhouse gas (GHG) emissions are considered. In the study described in this paper a multiobjective genetic algorithm for WDS optimization has been used as an explorative tool to investigate the trade-offs between the traditional economic objective of minimizing costs and an additional environmental objective of minimizing GHG emissions. The impacts of minimizing GHG emissions on the results of WDS optimization have been explored for a case study in this paper. The results indicate that the inclusion of GHG emission minimization as one of the objectives results in significant trade-offs between the economic and environmental objectives. Furthermore, a sensitivity analysis has been conducted by using different discount rates in a present value analysis for computing both ongoing costs and GHG emissions. The results obtained show that the Pareto-optimal front is very sensitive to the discount rates used. As a result, the selection of discount rates has a significant impact on final decision making. [ABSTRACT FROM AUTHOR]

Cost-effective reduction of water loss is a compelling but challenging task for water utilities. This paper presents a model-based optimization method for leakage detection of water distribution systems. Leakage hotspots are assumed to exist at the model nodes identified. Leakage is represented as pressure-dependent demand simulated as emitter flows at selected model nodes. The leakage detection method is formulated to optimize the leakage node locations and their associated emitter coefficients such that the differences between the model predicted and the field observed values for pressure and flow are minimized. The optimization problem is solved by using a competent genetic algorithm. The leakage detection method is developed as an add-on feature of the optimization-based model calibration tool. This enables engineers to undertake leakage hotspot optimization as an independent task or combine the task with hydraulic model calibration, subject to suitably varied field data. Two case studies are discussed in this paper including an example from literature and a district water system in the United Kingdom. The results obtained illustrate that the optimization model for predicting leakage hotspots can be effective despite the recognized challenges of model calibration and the physical measurement limitations from the pressure and flow surveys also referred to as field tests. It is found that the method is effective at being applied for hydraulic conditions that occur in the early hours of the morning, often on water networks with excess design capacity and where hydraulic gradients are slack and loggers may sometimes be working close to their limits of accuracy. [ABSTRACT FROM AUTHOR]

This paper presents an enhanced design methodology for optimal mixture proportion of concrete composition with respect to accuracy in the case of using prediction models based on a limited database. In proposed methodology, the search space is constrained as the domain defined by a limited database instead of constructing the database covering the region represented by the possible ranges of all variables in the input space. A model for defining the search space which is expressed by the effective region in this paper and evaluating whether a mix proportion is effective is added to the optimization process, yielding highly reliable results. To demonstrate the proposed methodology, a genetic algorithm, an artificial neural network, and a convex hull were adopted as an optimum technique, a prediction model for material properties, and an evaluation model for the effective region, respectively. And then, it was applied to an optimization problem wherein the minimum cost should be obtained under a given strength requirement. Experimental test results show that the mix proportion obtained from the proposed methodology considering the regional characteristics of the database is found to be more accurate and feasible than that obtained from a general optimum technique that does not consider this aspect. [ABSTRACT FROM AUTHOR]

The area of systematic identification of contamination sources in water distribution systems is in its infancy and is rapidly growing. The real water distribution network problem poses many challenges that current methods usually assume away to facilitate manageable method development and testing. Current methods may not readily and efficiently address issues, such as multiple sources, unknown contamination types with different reaction kinetics, use of different types of sensors with varying degree of resolution, dynamically varying demand and sensor information, and uncertainty and errors in the data and measurements. With the aim of addressing these imminent challenges, this paper reports the findings of an ongoing research investigation that develops and tests an evolutionary algorithm-based flexible and generic procedure, which is structured within a simulation-optimization paradigm. This paper describes the specific implementation of the method using evolution strategies (ESs), a population-based heuristic global search algorithm. A key component of designing this source characterization method is to define a compact, but comprehensive, solution encoding structure. The new method is constructed using a tree-based encoding design to enable the representation of variable-length decision vectors and a set of associated genetic operators that enable an efficient search. This algorithm is successfully tested and demonstrated to have consistently good performance for several instances of an illustrative water distribution contamination case study. As the ES-based algorithm conducts a probabilistic search, its robustness is tested using multiple random trials, and the method is shown to exhibit a robust behavior. [ABSTRACT FROM AUTHOR]

Time–cost trade-off analysis is addressed as an important aspect of any construction project planning and control. Nonexistence of a unique solution makes the time–cost trade-off problems very difficult to tackle. As a combinatorial optimization problem one may apply heuristics or mathematical programming techniques to solve time–cost trade-off problems. In this paper, a new multicolony ant algorithm is developed and used to solve the time–cost multiobjective optimization problem. Pareto archiving together with innovative solution exchange strategy are introduced which are highly efficient in developing the Pareto front and set of nondominated solutions in a time–cost optimization problem. An 18-activity time–cost problem is used to evaluate the performance of the proposed algorithm. Results show that the proposed algorithm outperforms the well-known weighted method to develop the nondominated solutions in a combinatorial optimization problem. The paper is more relevant to researchers who are interested in developing new quantitative methods and/or algorithms for managing construction projects. [ABSTRACT FROM AUTHOR]

The problem of deploying sensors in a large water distribution network is considered, in order to detect the malicious introduction of contaminants. It is shown that a large class of realistic objective functions—such as reduction of detection time and the population protected from consuming contaminated water—exhibits an important diminishing returns effect called submodularity. The submodularity of these objectives is exploited in order to design efficient placement algorithms with provable performance guarantees. The algorithms presented in this paper do not rely on mixed integer programming, and scale well to networks of arbitrary size. The problem instances considered in the approach presented in this paper are orders of magnitude (a factor of 72) larger than the largest problems solved in the literature. It is shown how the method presented here can be extended to multicriteria optimization, selecting placements robust to sensor failures and optimizing minimax criteria. Extensive empirical evidence on the effectiveness of the method presented in this paper on two benchmark distribution networks, and an actual drinking water distribution system of greater than 21,000 nodes, is presented. [ABSTRACT FROM AUTHOR]

This paper addresses the optimal contraflow lane configuration problem arising in the contraflow lane control strategy that has been implemented successfully in practice. The problem is formulated as a bilevel programming model in which the upper level problem is a binary integer programming formulation that aims to minimize the total travel time of a study area, while the lower level problem is a microscopic traffic simulation model that can simulate the dynamic reaction of the drivers resulting from a contraflow lane configuration scheme. A microscopic traffic simulation model is adopted in this study because it is easily handled by traffic engineers. Such an adoption results in inexistence of analytical expression of the objective function in the upper level problem. Accordingly, some conventional analytical solution methods for solving integer programming problems are no longer available for the proposed model. Therefore, this paper develops a variation of genetic algorithm that embeds with the microscopic traffic simulation model as well as a chromosome repairing procedure to find an optimal contraflow lane configuration solution. A case study in Singapore is carried out to evaluate the proposed methodology, in which PARAMICS as the microscopic traffic simulation model is applied. [ABSTRACT FROM AUTHOR]

This paper presents the concept used to construct a complex residential tilt-up-panel structure utilizing three-dimensional (3D) modeling and animations. The residence comprises of 108 precast concrete panels of varying rectangular shapes with “dog legs” and window and door “cutouts” that look like an assembled jigsaw puzzle. The erection and installation procedure called for a maximum panel-to-panel joint tolerance of 1.27 cm(0.5 in.), often in 90° joints between panels. 3D animations were used to experiment with the construction process on the computer screen prior to construction in order to avoid potential costly on-site errors. In addition, the 3D animations were also used as a training tool for the contractors. This paper focuses on describing the methodology used to integrate a crane selection algorithm and optimization model with 3D modeling and animation for the selection, utilization, and location of cranes on construction sites. Analytical optimization processes were used to decrease the traveling time and distance of the selected crane, to improve the crane lifting sequence and to minimize the use of panel casting slabs. [ABSTRACT FROM AUTHOR]

Optimal coordination of traffic signals requires proper activation of timing plans to match current traffic conditions. One of the greatest challenges in configuring a traffic responsive plan selection control mode is how to select only n number of timing plans (restricted by traffic controller memory limitations) to address possibly all traffic conditions/states that can be encountered in the field, for a specific site, but most importantly, how to associate each of these traffic conditions to one of the n timing plans stored in the traffic controller. This paper uses a multiobjective nondominated sorting genetic algorithm to (1) select the best n timing plans to be stored in the controller and (2) group traffic states into m number of clusters, and associate each of the m clusters with one of the stored timing plans. A new performance measure, degree of detachment, developed by the writers to address the clustering requirements, is described in this paper. [ABSTRACT FROM AUTHOR]

The network signal design problem (NSDP) seeks the optimal deployment of traffic signals in a growing urban area. This paper is especially concerned with how signals may be optimally deployed over a very long period of time for the purpose of creating realistic networks for travel forecasting. The NSDP is very difficult to solve for long-range problems because of the large number of possible solutions, the high cost of evaluating the merits of just a single solution, and the complexities of how signal delay affects traffic patterns and how traffic patterns affect signal delay. The paper describes the NSDP, introduces a reasonable set of simplifications based on transportation planning and traffic engineering practice, describes experiences with a possible heuristic algorithm for problem solution, and contrasts this method with current planning practice and other research. The long-range algorithm embeds a “strategic” algorithm for finding an optimal deployment for a single time period with constant travel demands. The strategic algorithm draws upon two well-known techniques of combinatorial optimization: a greedy constructive search coupled with a restricted neighborhood search. The strategic algorithm was able to find exact solutions on a small test network with eight stop-controlled intersections. The long-range algorithm is demonstrated on a full-sized planning network with about 380 stop-controlled intersections that could be signalized. [ABSTRACT FROM AUTHOR]

This paper presents an innovative model for scheduling, resource planning, and cost optimization of large construction and/or maintenance programs that involve multiple distributed sites. The proposed distributed scheduling model (DSM) employs an information system to store data related to various work sites, activities’ optional construction methods, and available resources. The DSM also incorporates a scheduling algorithm that is resource focused, and maintains crew work continuity under any sequence of distributed sites. To minimize cost and meet project time and resource constraints, the DSM uses genetic algorithms to determine the optimum set of construction methods and the optimum routing order among sites. Details of the DSM formulation are presented in this paper and the benefits to municipalities and contractors are outlined. [ABSTRACT FROM AUTHOR]

This paper presents a coupled self-adaptive multiobjective differential evolution and network flow algorithm for the optimal operation of complex multipurpose reservoir systems. The developed algorithm (i.e., self-adaptive multiobjective differential evolution) is compared to nondominated sorting genetic algorithm II using a set of common test problems and a real-world case study. An out-of- kilter method for minimal-cost flow problems is used to optimize the water resource system from self-adaptive multiobjective differential evolution inputs driven by the evolutionary process. Self-adaptive multiobjective differential evolution is then used to evaluate objective functions based on the outputs from out-of-kilter algorithm and the process continues until the stop criterion is met. The advantages of the proposed approach include (1) flexible evolutionary algorithms for solving highly complex objective function, and (2) efficient network flow method for dealing with large and highly constrained problems. The case study includes one part of a complex water supply system located in southwestern Brazil that provides water for almost 20 million people in Sao Paulo metropolitan area. The objectives of the case study include minimization of demand shortage (the difference between demand for water and available water supply), maximization of water quality (or minimization of the deviation from the water quality standards), and minimization of pumping cost. The coupled model is applied to the case study using one inflow scenario representing a drought period with inflows below historical average. Multiobjective analyses are performed by comparing two pairs of objective functions, as follows: (1) minimization of demand shortage versus minimization of pumping cost, and (2) minimization of demand shortage versus minimization of the deviation from the water quality standards. The problem constraints include reservoir capacity, capacity of tunnels, channel flow limitations, and minimum downstream release for all reservoirs within the system. The proposed coupled model (self-adaptive multiobjective differential evolution and out-of-kilter) is outperforming both pure self-adaptive multiobjective differential evolution and nondominated sorting genetic algorithm II, as it requires significantly smaller number of generations to derive the Pareto front. In addition, the proposed approach is capable of handling larger problems without major computational burden. The coupled model and self-adaptive multiobjective differential evolution also converge closer to, and provide better coverage of the true Pareto front than, nondominated sorting genetic algorithm II. [ABSTRACT FROM AUTHOR]

The effort to manage irrigation water use through the adoption and implementation of agricultural best management practices (BMPs) is crucial for meeting water bodies' sustainability in water-deficient agricultural areas. This paper presents the development of an efficient decision support tool able to suggest the optimal location for placing high- and low-tech irrigation BMPs, such as deficit irrigation, conveyance efficiency improvement, precision irrigation, and wastewater reuse, and demonstrates its application in the water-scarce Pinios river basin in central Greece. The tool uses the SWAT (soil and water assessment tool) model as the BMP and hydrologic simulator and a multiobjective genetic algorithm, which, based on calculated cost data, optimizes the cost-effectiveness of management schemes across the landscape. From the analysis of the results produced and the optimal river basin BMP configurations under the current and a volumetric water pricing scenario and under a variation of the cost parameters, the study concludes that a number of different BMPs at an affordable implementation cost, along with a tiered water pricing system that could address socioeconomic heterogeneities, would form a sustainable action plan against desertification in the highly water-deficient Pinios basin. The methodology and tool are considered to be easily applicable in other river basins and could be used to assist in more cost-effective implementation of environmental legislation. [ABSTRACT FROM AUTHOR]

The design of a water distribution network (WDN) is conceptually a multiobjective problem characterized by stringent constraints. Even in its most simple formulation, which requires only the maintenance of minimum pressures, it is difficult to solve, and the simplifications introduced often impair the practicality of the solutions. The paper aims at integrating an engineering approach to the optimization problem by generating pseudofronts in the hydraulically feasible region of the search space. The optimization algorithms provide an entirely feasible trade-off between cost and considered constraints, which may be directly used by a hypothetical decision maker. This engineering approach requires a fast tool to search for feasible solutions: the recently developed genetic heritage by stochastic evolution transmission (GHEST) algorithm, used herein, has proved to be effective and efficient in solving the optimal design problem of WDNs. Tests are carried out on two large-size water distribution networks (Balerma and Modena), using the GHEST algorithm, modified to account for several objectives. [ABSTRACT FROM AUTHOR]

An effective strategy in reducing train-induced vibrations on neighboring structures is to use open trenches on both sides of the railway lines. This paper investigates the effect of the trench's shape on its performance. For this purpose, a V-shaped trench was first modeled using finite-element code, and its efficiency was confirmed in reducing the induced vibrations caused by passing the MD36 passenger bogie in comparison with a rectangular-shaped trench. In this regard, results indicate that the maximum values of the amplitude reduction ratio () for the ground displacement before the V-shaped trench for train speeds of were 1.17 and 1.28, respectively, which were less than the value of 2 obtained for the rectangular trench. In the final stage, an optimization algorithm was used to present the procedure of calculating optimized shape of the proposed V-shaped trench for various train speeds. [ABSTRACT FROM AUTHOR]

The quality of hot mix asphalt is affected by the quality and consistency of input aggregates and the control of the production process. To improve the quality of hot mix asphalt, both the aggregates gradation and the process variables must be considered. Current practice involves taking samples from actual production and analyzing them in the lab. The entire process can take two hours, which, along with being expensive, is not amenable to real time online process control or even in knowing how much product is actually out of specification. In this paper, an online control system is proposed that can be used to significantly decrease the analysis time and adjust production by using discrete time stochastic simulation combined with algorithmic optimization. Additionally, the system can readily show when a mix is out of specification without lag time or physical experimentation. Results show that this approach can effectively control the production process resulting in improved quality. This is the first known such application in hot mix asphalt online process control. [ABSTRACT FROM AUTHOR]

Weathering steel with a natural corrosion-resistant feature has been widely applied to the structural components of steel bridges. However, severe surface corrosion damage has been frequently observed in the weathering steels of bridges, which causes the performance degradation of the structure. Conventional visual classification approaches are time-consuming and subjective and cannot provide quantitative evaluation effectively and efficiently. This paper presents a new imaging-based intelligent method for quantitatively rating the corrosion states of weathering steel bridges. Images are characterized by image texture analysis using two-dimensional wavelet decomposition, from which both the local and global energy distributions of each detail subimage are extracted as representative features. To enhance the performance of a support vector machine (SVM) in corrosion state classification, a particle swarm optimization algorithm (PSO) is developed to obtain the optimal parameters of the SVM. A comparative study indicates that PSO-SVM can achieve better classification accuracy rates than artificial neural network. Numerical results demonstrate that this study provides an effective approach to imaging-based rating by integrating wavelet transform and PSO-SVM techniques for allocating the condition state of corroded weathering steel. [ABSTRACT FROM AUTHOR]

Determining the global shape of a deploying structure and the section profiles of its members is a challenging design problem. Geometry, meaning the lengths and relative angles of members, is critical to achieving stable deployment to a desired span, while the design must also satisfy structural capacity demands at each stage of deployment. This paper explores the potential role of formal structural optimization in designing feasible and structurally efficient deploying steel structures composed of linkage elements. Both stochastic search and gradient-based algorithms are used to explore the design space and identify minimum weight solutions that satisfy kinematic and structural constraints. The proposed methodology is tested on the case study of a deploying pantograph. This strategy has the potential to be implemented for a wide range of deploying structures, including retractable roofs, rapidly expandable shelters, deploying space structures, and movable bridges. [ABSTRACT FROM AUTHOR]

This paper presents a method to locate the global critical slip surface for slope profiles with complex geology. In this method, a readily available concise algorithm based on the Morgenstern-Price method is used to efficiently calculate the factor of safety for a slip surface. This algorithm is combined with an optimization technique to search for global critical slip surfaces based on a set of random initial slip surfaces. The proposed method is applied to analyze three example problems involving slope profiles with various degrees of complexity. This study indicates that the proposed global search strategy can locate the global critical slip surface regardless of the initial slip surface for slope profiles with low to moderate complexity. For more complex geologic slope profiles, however, the proposed search strategy may only be able to identify locally critical slip surfaces depending on the initial slip surface. To ensure that the global critical slip surfaces are identified, more random initial slip surfaces need to be considered. A sound engineering judgment may be combined with the global search algorithm presented in this study to locate global critical slip surfaces more efficiently. [ABSTRACT FROM AUTHOR]

In recent years, a number of evolutionary algorithms have been proposed for optimizing the design and operation of water distribution systems (WDSs). These evolutionary algorithms include genetic algorithms, ant colony optimization, particle swarm optimization, the shuffled leaping frog algorithm, and differential evolution. Although there have been some comparisons made of the performance of the various algorithms, very few of these comparisons have been carried out in a completely rigorous manner. The main aim of this paper is to introduce a methodology for the rigorous comparison of various algorithms for the optimum design of water distribution systems. The methodology involves comparing the various algorithms in terms of (1) the best solution obtained; (2) the speed of convergence; and (3) the spread and consistency of the solutions obtained over a number of random starting seeds and numbers of evaluations. As a demonstration of the methodology, the techniques of genetic algorithms (GA), particle swarm optimization (PSO), and differential evolution (DE) are applied to two frequently used WDS case studies, namely the New York Tunnels and Hanoi water networks. In addition, the techniques are applied to a real-size water distribution system consisting of 476 pipes. The results obtained show that the algorithm performances depend on the specific problem and the number of function evaluations allowed. Moreover, it is shown that correct calibration is an essential phase for a fair comparison of evolutionary algorithms. In fact, the best parameters are a function of the problem characteristics, of the objective function and of the variants in the algorithm operators. Therefore the adoption of configurations tested on slightly different versions of the algorithms can lead to quite different results. [ABSTRACT FROM AUTHOR]

The light weight deflectometer (LWD) is a portable, nondestructive testing device that can estimate pavement layer parameters, namely moduli. Conventional backcalculation of layer parameters from LWD deflections is formulated as an inverse problem where predicted vertical deflections are matched to observed vertical deflections using a gradient search algorithm. In this paper, we present an LWD backcalculation scheme to recover layer parameters, including top-layer thickness, of a two-layer earthwork system. Our approach resolves the problem using a dynamic finite-element (FE) model for the forward calculation of LWD deflection data and implements a genetic algorithm (GA) as the inverse solver. The objective function we minimize is formulated as a measure of the data misfit between predicted and observed data, normalized by the peak deflections, and it includes 180 data points from the dynamic deflection time history. The objective function contains multiple local minima that can potentially trap gradient search algorithms, thus validating application of GA as a global search technique for this problem. The GA is applied to both synthetic and experimental data, and we demonstrate that the recovered top-layer thickness, top-layer modulus, and underlying modulus for the experimental data compare favorably with expected values. [ABSTRACT FROM AUTHOR]

Streams and their associated biological communities are among our most valuable natural resources. Humans rely on the environmental services provided by streams in a myriad of ways. However, in some areas, excessive groundwater pumping exacerbates the already critical pressure on streamflow and must be managed through effective planning. Based on economic and hydrogeological concepts, this study estimates the quantity of streamflow depletion that is attributable to groundwater pumping and the negative impact on the socioeconomic system if groundwater pumping must be constrained to restore streamflow. The primary objective of this paper is to develop a multiobjective nonlinear optimization model to simulate the tradeoffs between streamflow restoration and economic welfare loss in a Chicago suburban county, McHenry County. The multiobjective optimization was conducted at both county and municipality levels. An evolutionary algorithm, the nondominated sorting genetic algorithm, was used to solve the optimization model and to identify the tradeoff curve (Pareto frontier). Comparing municipal Pareto frontiers shows spatially heterogeneous costs of preserving streamflow through various shadow prices and also the different capacities of restoring streamflow. The results include discussion of the shapes of the Pareto frontier, the sensitivity of the pumping boundary constraints, and return flow coefficients. It is concluded that the multiobjective optimization model provides a useful framework to consider conflicting objectives in a typical environmental management and planning process, and that the findings can help decision-makers and planners to formulate effective groundwater pumping strategies. [ABSTRACT FROM AUTHOR]

The ability to monitor the flow and how water transforms throughout water networks would greatly improve the management of those distribution systems. The sensor placement problem attempts to find the locations of monitoring devices that would optimally observe water quality and protect consumers from accidents and intrusions of contaminants. In some related critical scenarios, the absence of information about possible contamination events, including knowledge of the injection sources, contaminant types or mass, time of pollution, and the variability of water network input parameters, such as the nodal demands and the pipe roughness coefficients, raises several challenges that must be addressed by the sensor network design process. In this paper, the authors describe a stochastic parameterization and analysis of uncertainty for the design of sensor networks aimed at maximizing the probability of detection of accidents and intrusions in water distribution networks. A challenge with such an approach, in particular, when applied to large urban water networks is the size of the ensuing computational model and the associated numerical optimization problem. This problem is compounded in the presence of uncertainty, where the need to deal with a large number of statistically plausible scenarios underscores the need for efficient yet credible algorithms for addressing the sensor placement issue. In addition to modeling aspects, the authors also address a challenge associated with the computational feasibility and performance of relevant numerical algorithms. Specifically, and through the use of submodular cost functions, the authors are able to solve the optimization problem with a greedy algorithm, yielding sufficient computational performance that allows for the description of stochastic water demands in the context of Monte Carlo simulations. Imperfect sensors are further accounted for while maintaining submodularity. The results show that the optimal sensor network layout is highly dependent on whether uncertainty in the stochastic parameters has been introduced and whether individual sensors can fail or not. [ABSTRACT FROM AUTHOR]

This work presents a postoptimization methodology for refining the solutions found by adaptive search algorithms used in the design of large water distribution networks. The approach uses two heuristics to search for an optimal combination of pipes that, after a reduction of their diameters, will maximize cost savings while continuing to meet design constraints. Adaptive search methods are often used to design urban water distribution networks when the number of pipes in the network is insignificant. For complex, real-world networks, however, such methods are computationally demanding, and they have difficulty finding near-global optima. To identify a solution as close to the global optimum (and in which no pipe can be reduced without violating pressure constraint), requires a high-speed computer potentially running for a long time and also probably some good fortune. The postoptimization approach presented in this paper is shown to be an efficient complement to heuristic search algorithms used in the design of real-world networks. In a network created with the aid of a genetic algorithm, the proposed heuristics found that 4.37% of the pipes with a diameter greater than the minimum could be further reduced without causing hydraulic failure. [ABSTRACT FROM AUTHOR]

High-speed rail (HSR) networks require large investments, and the performance of the infrastructure is affected by varying local environments, while subject to tight layout restrictions. This paper presents a fully integrated three-dimensional model to optimize the HSR alignment at a planning scale, which sets boundaries for the final project design. The model considers mandatory and desirable specifics for the locations to link and the geometry in both the plan view and the longitudinal profile. It also allows one to define prohibited and restricted land-use areas. A computational tool has been developed that takes into account the problem specifics using a simulated annealing algorithm to optimize the problem solution. The capabilities of the model and the tool are demonstrated with the application to an intentionally simple and synthetic case study, considering construction costs and problem constraints, for which sound results are obtained. Both the model and the tool can be expanded to incorporate additional complexity, establishing the basis for real applications and for further integration of geotechnical and hydrological risk factors that affect the HSR performance. [ABSTRACT FROM AUTHOR]

Allocating tracks with operation restrictions for trains in a conflict-free way is known as the basic scheduling problem, and it has attracted much attention, especially at the station bottlenecks. First, this paper presents a mean-variance optimization model for solving the track allocation problem that minimizes the occupation time costs in groups of turnouts at station bottlenecks. Then the simulated annealing algorithm is provided to solve this programming. In addition, a case of a China railway station focused on track allocation is performed that illustrates the application of the proposed model. Also, considering the quality of track allocation program, an evaluation function that tests the results in a balanced allocation is built to further strengthen the allocating results. Finally, to verify the efficiency of the simulated annealing algorithm, the effects of algorithm parameters on an optimizing function have been tested. It illustrates that the algorithm parameters have great effect on the global optimal results. The more cycles the program runs, the better optimization results will be obtained. [ABSTRACT FROM AUTHOR]

This paper presents an application of the Multiobjective Differential Evolution (MoDE) algorithm for the optimal operation of a complex multipurpose reservoir system. The developed algorithm (MoDE) is compared with the Genetic Algorithm (NSGA-II) using a set of common test problems and the case study. The case study includes part of a complex water supply system located in southwest Brazil that provides water for approximately 20 million people in the Sao Paulo metropolitan area (SPMA). The objectives of the case study include minimization of demand shortage (the difference between demand for water and available water supply), maximization of water quality (or minimization of the deviation from the water quality standards), and minimization of pumping cost. MoDE is applied to the case study using two inflow scenarios: (1) a drought period with inflows below historical average; and (2) a wet period with inflows above historical average. Multiobjective analysis is done by comparing two pairs of objective functions: minimization of demand shortage versus minimization of pumping cost and minimization of demand shortage versus minimization of the deviation from water quality standards. The constraints for the analysis are reservoir capacity, tunnels and channel limitations, and minimum downstream flow for all reservoirs. The proposed MoDE algorithm is outperforming NSGA-II as it converges closer to and provides better spread coverage of the true Pareto front. [ABSTRACT FROM AUTHOR]

This paper presents a multiobjective model for optimization of the activated sludge process (ASP) in a wastewater-treatment plant (WWTP). To minimize the energy consumption of the activated sludge process and maximize the quality of the effluent, three different objective functions are modeled [i.e., the airflow rate, the carbonaceous biochemical oxygen demand (CBOD) of the effluent, and the total suspended solids (TSS) of the effluent]. These models are developed using a multilayer perceptron (MLP) neural network based on industrial data. Dissolved oxygen (DO) is the controlled variable in these objectives. A multiobjective model that included these objectives is solved with a multiobjective particle swarm optimization (MOPSO) algorithm. Computation results are reported for three trade-offs between energy savings and the quality of the effluent. A 15% reduction in airflow can be achieved by optimal settings of dissolved oxygen, provided that energy savings take precedence over the quality of the effluent. [ABSTRACT FROM AUTHOR]

Optimal design of tunnels and surge tanks is a major concern in the implementation and operation of large hydropower plants, particularly those in which tunnels are long and water hammer is likely. In this paper, flow simulation and an optimization process have been linked in an iterative manner to develop a general model for the optimal design of hydropower tunnels and surge tanks for the emergency condition of operation (maximum surge pressure). The governing equations for the analysis of transient flow in conduits are solved using a finite difference scheme. Furthermore, a genetic algorithm (GA) optimization technique is utilized to select the optimal diameter for the headrace tunnel, penstocks, and surge tanks. The benefit-cost ratio is considered as the objective function in the optimization analysis. The proposed method was used to design diameters for Marun hydropower tunnels (an in-operation large hydropower dam located in southwest Iran). The results were compared with results from a nonlinear optimization technique. They revealed considerable savings in construction cost, attributable to the application of GA. The calculated pressures across the tunnels with and without surge tank agree with valve closure theory and previous studies. The model is able to optimize diameters for a branching system of tunnels and penstocks with surge tanks. [ABSTRACT FROM AUTHOR]

The railroad blocking problem (RBP) is an important decision for freight railroad companies. The objective of this problem is to minimize the costs of delivering all commodities by deciding which interyard blocks to build and specifying the assignment of commodities to these blocks. In this paper, a mathematical model is presented for the RBP on Iran Railways. Its decision variables identify blocking scheme and demand assignment to these blocks. The RBP in medium and large sizes is not solvable with any commercial software available in the market. Therefore, a solution method based on tabu search algorithm is proposed for the suggested model. For evaluating the proposed algorithm, several simulated test problems are randomly generated and solved. The obtained results on the test problems are compared with those of solutions generated by CPLEX software. The comparison shows high efficiency and effectiveness of the proposed algorithm. The proposed model and solution method are applied to build a blocking plan for the Iranian railway. [ABSTRACT FROM AUTHOR]

The fast-track method is one of the most recognized methodologies for reducing construction project schedules. However, due to the lack of definitive research to date pertaining to the effects of fast-track application in terms of time and cost, it has been difficult for project owners to determine its correct application. This paper presents the time and cost optimized decision support (TACTICS) model, and it was developed based on the fast-track methodology and genetic algorithms (GAs). TACTICS was applied to two case studies, and the results indicated that the fast-track method could be expected to deliver more efficient projects compared with using the traditional method. In particular, (1) the average reduction in project duration by applying the fast-track method was 40.48% (Case I) and 18.59% (Case II) compared with using the traditional method, and (2) the average project costs were reduced by as much as 0.39% (Case I) and 4.48% (Case II). Consequently, TACTICS could be expected to help in making a decision regarding the fast-track application and further contribute to the project scheduling expertise in the construction engineering and management body of knowledge. [ABSTRACT FROM AUTHOR]

The power generated by a hydroelectric plant depends on the penstock head loss, the turbine efficiency, and the generator efficiency among other factors. Initially, the functions related to these variables are provided by the equipment manufacturer; however, over time they change as the plant ages. This paper presents a methodology to adjust an efficiency function for each generating unit based on measured data. It is applied using two optimization methods: the generalized reduced gradient and the evolutionary algorithm. A case study with data from a large Brazilian hydroelectric plant demonstrates how the methodology can be employed and compares the performance of the optimization tools. A comparison metric is used to show that the optimal unit efficiency function significantly improves the performance of simulation models to reproduce observed data and better describe the actual operation of the hydroelectric plant. [ABSTRACT FROM AUTHOR]

Diminishing marginal utility is an important characteristic of water resources systems. With the assumption of diminishing marginal utility (i.e., concavity) of reservoir utility functions, this paper derives a monotonic relationship between reservoir storage and optimal release decision under both deterministic and stochastic conditions, and proposes an algorithm to improve the computational efficiency of both deterministic dynamic programming (DP) and stochastic dynamic programming (SDP) for reservoir operation with concave objective functions. The results from a real-world case study show that the improved DP and SDP exhibit higher computational efficiency than conventional DP and SDP. The computation complexity of the improved DP and SDP is (order of , the number of state discretization) compared to with conventional DP and SDP. [ABSTRACT FROM AUTHOR]

Designing baseline or vertical alignment in railways greatly depends on the designer's experience. An inaccurate design imposes unreasonable costs during construction and operations. This paper presents a program to find the best vertical alignment for a track with a given horizontal layout. First, the vertical alignment is formulated using mathematical equations, and then an algorithm is developed to produce its geometric configuration. Construction and operating costs, which are sensitive to gradient changes, are expressed as vertical alignment variables and included in the model. A direct search method is employed to solve the objective function, and a genetic algorithm encodes solution algorithms in a program, whereas certain heuristic operators are utilized to cover operational situations. The program also provides railway engineers and planners with practical information on the resulting alignment such as time and speed graph, total traction force, and block capacity. Moreover, the model is evaluated using a real example to verify the algorithms' ability to find the optimal alignment. [ABSTRACT FROM AUTHOR]

The paper presents a technique to invert two-dimensional (2D) full wavefields using simulated annealing and a finite-difference solution of the 2D elastic wave equation in the time-distance domain. The algorithm generates all possible wave types (body waves, surface waves, etc.) to simulate complex seismic wavefields and for comparison with observed data. Model runs with both synthetic and actual experimental data sets illustrate the capability of the inversion technique. The results from synthetic data demonstrate the potential of characterizing both low- and high-velocity layers in laterally inhomogeneous profiles, and the inversion results from actual data are consistent with the crosshole, standard penetration test N-value, and material log results. Based on the cases presented, the coupling of global optimization with full waveforms is computationally practical; the results presented herein required less than 1 day of computer time on a standard laptop computer. [ABSTRACT FROM AUTHOR]

The fast-track method is one of the most recognized methodologies for reducing construction project schedules. However, due to the lack of definitive research to date pertaining to the effects of fast-track application in terms of time and cost, it has been difficult for project owners to determine its correct application. This paper presents the time and cost optimized decision support (TACTICS) model, and it was developed based on the fast-track methodology and genetic algorithms (GAs). TACTICS was applied to two case studies, and the results indicated that the fast-track method could be expected to deliver more efficient projects compared with using the traditional method. In particular, (1) the average reduction in project duration by applying the fast-track method was 40.48% (Case I) and 18.59% (Case II) compared with using the traditional method, and (2) the average project costs were reduced by as much as 0.39% (Case I) and 4.48% (Case II). Consequently, TACTICS could be expected to help in making a decision regarding the fast-track application and further contribute to the project scheduling expertise in the construction engineering and management body of knowledge. [ABSTRACT FROM AUTHOR]

Computational cost is a critical issue for large-scale water-resource optimization under uncertainty, since time-intensive Monte Carlo simulations are often required to evaluate over multiple parameter realizations. This paper presents an efficient approach for replacing most Monte Carlo simulations with surrogate models within a noisy genetic algorithm (GA). The surrogates are trained to predict the posterior expectations online on the basis of stochastic decision theory, using Monte Carlo simulation results created during the GA run. The surrogates, which in this application are neural networks, are adaptively updated to improve their prediction performance as the search progresses. A Latin hypercube sampling method is used to efficiently sample parameters for the Monte Carlo simulation, and the sampling results are archived so that the estimate of posterior expectation can be iteratively improved in an efficient manner. In addition, the GA is modified to incorporate hypothesis tests in its selection operator to account for sampling noise. The method is applied to a field-scale groundwater remediation design case study, whereas the primary source of uncertainty stems from hydraulic conductivity values in the aquifers. The results show that the method identified more reliable and cost-effective solutions with 86-90% less computational effort than the purely physically based noisy GA approach. [ABSTRACT FROM AUTHOR]

In this paper, an optimization model for force finding of tensegrity systems with considerations of the geometrical stability condition, the unilateral property of members, and the evenness requirement of internal forces was developed. The simulated annealing algorithm was used to solve this optimization problem. Force finding of several typical tensegrity systems was carried out with the proposed method. It was found that the optimized solutions were very close to the analytical solutions and able to reflect the geometrical symmetry of the structures. It also verified that the proposed method is applicable to tensegrity systems under consideration including simple two-dimensional structures and complex three-dimensional multimodular structures. [ABSTRACT FROM AUTHOR]

A pontoon-type, very large floating structure (VLFS) undergoes uneven deformation when loaded unevenly. The resulting differential deflection may lead to a cessation of equipment operation, and even compromising the structural integrity of the VLFS. One cost effective solution for reducing the differential deflection is by introducing the innovative gill cells at appropriate locations in the VLFS. Gill cells are compartments in VLFS with holes or slits at their bottom surfaces to allow water to enter or leave freely. At these gill cell locations, the buoyancy forces are eliminated and this allows uneven buoyancy forces acting at the bottom hull of the VLFS to somewhat counterbalance the applied nonuniform loading. In this paper, we investigate the effectiveness of gill cells in pontoon-type VLFS in reducing the differential deflection and von Mises stresses as well as the optimal layout (i.e., the number and locations) of gill cells that minimizes the differential deflection subject to a draft constraint. As the decision variables, objective functions and the constraints are not continuous and differentiable, genetic algorithms are adopted as an optimization tool. The optimal layouts for gill cells are determined for various VLFS shapes such as square, rectangular and I-shape and loading configurations. [ABSTRACT FROM AUTHOR]

Reservoir flood control operation (RFCO) is a large scale multiobjective problem with complex constraints that require powerful algorithms to solve it. As a new metaheuristic evolutionary algorithm, shuffled frog leaping algorithm (SFLA) has the potential ability to solve multiobjective optimization problems because of its group evolution characteristic. In this paper, we present a novel multiobjective shuffled frog leaping algorithm (MOSFLA), which incorporates an archiving strategy based on self-adaptive niche method to maintain the nondominated solutions, and improves the memetic evolution process of SFLA to adapt to the multiobjective optimization problem. The numerical experiments of five Zitzler-Deb-Thiele functions indicate that MOSFLA yields better-spread solutions and converges closer to the true Pareto frontier than non-denominated sorting genetic algorithm (NGSA)-II and SPEA2. Furthermore, MOSFLA is applied to solve RFCO of the Three Gorges Project, and the results demonstrate that this algorithm can generate a solution set with uniform spread and good convergence for the problems with two conflicting objectives, including minimizing the highest reservoir water level and minimizing the peak flood discharge. Additionally, if compared with dynamic programming and NGSA-II, MOSFLA is verified to be more efficient and competitive, and thus can be provided as a new effective alternative for solving the complex reservoir operation problems. [ABSTRACT FROM AUTHOR]