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The reliable operation of spillways, in emergency as well as normal conditions, is one of the vital components in dam safety. Free or uncontrolled overflow spillways are the most reliable choice however. They usually impose higher construction cost and /or results in wasting a considerable amount of water or live capacity of the reservoirs. Employing fuse gates might be a way of reconciling dam safety with maximized storage capacity. The operation of the system can be controlled to within a few centimeters, and the entire installation is not lost for floods less than the maximum design flood. The installation offers more or less the same level of safety as ungated spillways, but avoids their inherent storage capacity loss. Optimum design of fuse gates in particular installation calls for a mathematical model. The model developed in this work includes structural, hydraulics and operational constraints while maximizing the expected cost over the useful life of the project. Accounting for the lost benefit (i.e., water lost as a result of gate tilting) has an influenced effect on the optimum design. To test the performance of the model, data from Zarineh Rud dam in Iran has been used and its result is compared with a direct search technique. The model is capable of helping the design engineer to select the best alternative considering different types of constraints.

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In this paper the analysis of the pipe networks is formulated as a nonlinear unconstrained optimization problem and solved by a general purpose optimization tool. The formulation is based on the minimization of the total potential energy of the network with respect to the nodal heads. An analogy with the analysis of the skeletal structures is used to derive tire formulation. The proposed formulation owes its significance for use in pipe network optimization algorithms. The ability and versatility of the method to simulate different pipe networks are numerically tested and the accuracy of the results is compared with direct network algorithms.

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In this paper, optimal bridge management system models have been presented. These optimization models are capable of allocating limited resources to the bridge preservation schemes in order to establish the optimal time of completing the activities. Bridge-based activities are divided into two main groups: repair projects, and maintenance activities and both models are presented in this paper. Particular attention has been made to optimize the management of the two system activities. The dynamic programming approach was utilized to formulate and analyze the two models. The developed models are found to be more accurate and faster than the previous ones.

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In this paper a structural damage detection algorithm using static test data is presented. Damage is considered as a reduction in the structural stiffness (Axial and/or Flexural) parameters. Change in the static displacement of a structure is characterized as a set of non-linear undetermined simultaneous equations that relates the changes in static response of the structure to the location and severity of damage. An optimality criterion is introduced to solve these equations by minimizing the difference between the load vector of damaged and undamaged structures. The overall formulation leads to a non-linear optimization problem with non-linear equality and linear inequality constraints. A method based on stored strain energy in elements is presented to select the loading location. Measurement locations are selected based on Fisher Information Matrix. Numerical and experimental results of a 2D frame represent good ability of this method in detecting damages in a given structure with presence of noise in measurements.

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One main factor in design of panels subjected to axial loading is their buckling behaviour. The design of stiffeners in a metal or composite plated structure is the key factor for safety and weight reduction. This work presents a parametric study on the optimal types and geometrical properties of stiffeners in plates under in-plane axial loads. The results show that flanged type (such as T or L) longitudinal stiffeners increase the normal critical stresses by at least 28% compared to non-flanged stiffener. It is also shown that the optimum geometric properties of stiffeners correspond to the point when the buckling shape of a plate changes from the overall to local mode. Also it is illustrated that for these optimal instances, there always is a linear relationship between the cross-sectional area ratio and the rigidity ratio of the stiffeners to the plates. Finally, Sample relationships for plates having different number of stiffeners are presented.

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Genetic Algorithm is known as a generalized method of stochastic search and has been successfully applied to various types of optimization problems. By GA s it is expected to improve the solution at the expense of additional computational effort. One of the key points which controls the accuracy and convergence rate of such a process is the selected method of coding/decoding of the original problem variables and the discrete feasibility space to be searched by GAS. In this paper, a direct index coding (DIC) is developed and utilized for the discrete sizing optimization of structures. The GA operators are specialized and adopted for this kind of encoded chromosomes and are compared to those of traditional GA S. The well-known lO-bar truss example from literature is treated here as a comparison benchmark, and the outstanding computational efficiency and stability of the proposed method is illustrated. The application of the proposed encoding method is not limited to truss structures and can also be directly applied to frame sizing problems.

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Water conveyance systems (WCSs) are costly infrastructures in terms of materials, construction, maintenance and energy requirements. Much attention has been given to the application of optimization methods to minimize the costs associated with such infrastructures. Historically, traditional optimization techniques have been used, such as linear and non-linear programming. In this paper, application of ant colony optimization (ACO) algorithm in the design of a water supply pipeline system is presented. Ant colony optimization algorithms, which are based on foraging behavior of ants, is successfully applied to optimize this problem. A computer model is developed that can receive pumping stations at any possible or predefined locations and optimize their specifications. As any direct search method, the mothel is highly sensitive to setup parameters, hence fine tuning of the parameters is recommended.

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In this paper a general procedure for automated minimum weight design of twodimensional steel frames under seismic loading is proposed. The proposal comprises two parts: a) Formulation of automated design of frames under seismic loading and b) introduction of an optimization engine and the improvement made on it for the solution of optimal design. Seismic loading, that depends on dynamic characteristics of structure, is determined using "Equivalent static loading" scheme. The design automation is sought via formulation of the design problem in the form of a standard optimization problem in which the design requirements is treated as optimization constraints. The Optimality Criteria (OC) method has been modified/improved and used for solution of the optimization problem. The improvement in (OC) algorithm relates to simultaneous identification of active set of constraints and calculation of corresponding Lagrange multipliers. The modification has resulted in rapid convergence of the algorithm, which is promising for highly nonlinear optimal design problems. Two examples have been provided to show the procedure of automated design and optimization of seismic-resistant frames and the performance and capability of the proposed algorithm.

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In this paper, a new Continuous Ant Colony Optimization (CACO) algorithm is proposed for optimal reservoir operation. The paper presents a new method of determining and setting a complete set of control parameters for any given problem, saving the user from a tedious trial and error based approach to determine them. The paper also proposes an elitist strategy for CACO algorithm where best solution of each iteration is directly copied to the next iteration to improve performance of the method. The performance of the CACO algorithm is demonstrated against some benchmark test functions and compared with some other popular heuristic algorithms. The results indicated good performance of the proposed method for global minimization of continuous test functions. The method was also used to find the optimal operation of the Dez reservoir in southern Iran, a problem in the reservoir operation discipline. A normalized squared deviation of the releases from the required demands is considered as the fitness function and the results are presented and compared with the solution obtained by Non Linear Programming (NLP) and Discrete Ant Colony Optimization (DACO) models. It is observed that the results obtained from CACO algorithm are superior to those obtained from NLP and DACO models.

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A new mathematical model for identifying pollution sources in aquifers is presented. The model utilizes Lagrange Constrained Optimization Method (LCOM) and is capable to inversely solve unsteady fluid flow in saturated, heterogeneous, anisotropic confined and/or unconfined aquifers. Throughout the presented model, complete advection-dispersion equation, including the adsorption as well as retardation of contaminant, is considered. The well-known finite element method is used to discretize and solve the governing equations. The model verification is implemented using a hypothetical example. Also, the applicability of the developed code is illustrated by the real field problem of Ramhormoz aquifer in southwestern Iran.

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Through a collection of cooperative agents called ants, the near optimal solution to the multi-reservoir operation problem may be effectively achieved employing Ant Colony Optimization Algorithms (ACOAs). The problem is approached by considering a finite operating horizon, classifying the possible releases from the reservoir(s) into pre-determined intervals, and projecting the problem on a graph. By defining an optimality criterion, the combination of desirable releases from the reservoirs or operating policy is determined. To minimize the possibility of premature convergence to a local optimum, a combination of Pheromone Re-Initiation (PRI) and Partial Path Replacement (PPR) mechanisms are presented and their effects have been tested in a benchmark, nonlinear, and multimodal mathematical function. The finalized model is then applied to develop an optimum operating policy for a single reservoir and a benchmark four-reservoir operation problem. Integration of these mechanisms improves the final result, as well as initial and final rate of convergence. In the benchmark Ackley function minimization problem, after 410 iterations, PRI mechanism improved the final solution by 97 percent and the combination of PRI and PPR mechanisms reduced final result to global optimum. As expected in the single-reservoir problem, with a continuous search space, a nonlinear programming (NLP) approach performed better than ACOAs employing a discretized search space on the decision variable (reservoir release). As the complexity of the system increases, the definition of an appropriate heuristic function becomes more and more difficult this may provide wrong initial sight or vision to the ants. By assigning a minimum weight to the exploitation term in a transition rule, the best result is obtained. In a benchmark 4-reservoir problem, a very low standard deviation is achieved for 10 different runs and it is considered as an indication of low diversity of the results. In 2 out of 10 runs, the global optimal solution is obtained, where in the other 8 runs results are as close as 99.8 percent of the global solution. Results and execution time compare well with those of well developed genetic algorithms (GAs).

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This paper presents a relatively new management model for the optimal design and operation of irrigation water pumping systems. The model makes use of the newly introduced particle swarm optimization algorithm. A two step optimization model is developed and solved with the particle swarm optimization method. The model first carries out an exhaustive enumeration search for all feasible sets of pump combinations able to cope with a given demand curve over the required period. The particle swarm optimization algorithm is then called in to search for optimal operation of each set. Having solved the operation problem of all feasible sets, one can calculate the total cost of operation and depreciation of initial investment for all the sets and the optimal set and the corresponding operating policy is determined. The proposed model is applied to the design and operation of a real-world irrigation pumping system and the results are presented and compared with those of a genetic algorithm. The results indicate that the proposed mode in conjunction with the particle swarm optimization algorithm is a versatile management model for the design and operation of real-world irrigation pumping systems.

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This paper presents the application of an iterative penalty method for the design of water distribution pipe networks. The optimal design of pipe networks is first recasted into an unconstrained minimization problem via the use of the penalty method, which is then solved by a global mathematical optimization tool. The difficulty of using a trial and error procedure to select the proper value of the penalty parameter is overcome by an iterative use of the penalty parameter. The proposed method reduces the original problem with a priori unknown penalty parameter to a series of similar optimization problems with known and increasing value of the penalty parameters. An iterative use of the penalty parameter is then implemented and its effect on the final solution is investigated. Two different methods of fitting, namely least squares and cubic splines, are used to continuously approximate the discrete pipe cost function and are tested by numerical examples. The method is applied to some benchmark examples and the results are compared with other global optimization approaches. The proposed method is shown to be comparable to existing global optimization methods.

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In this paper, the problem of layout optimization for X-bracing of steel frames is studied using the ant system (AS). A new design method is employed to share the gravity and the lateral loads between the main frame and the bracings according to the requirements of the IBC2006 code. An algorithm is developed which is called optimum steel designer (OSD). An optimization method based on an approximate analysis is also developed for layout optimization of braced frames. This method is called the approximate optimum steel designer (AOSD) and uses a simple deterministic optimization algorithm leading to the optimum patterns and it is much faster than the OSD. Several numerical examples are treated by the proposed methods. Efficiency and accuracy of the methods are then discussed. A comparison is also made with Genetic algorithm for one of the frames.

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The congestion pricing has been discussed as a practical tool for traffic management on urban transport networks. The traffic congestion is defined as an external diseconomy on the network in transport economics. It has been proposed that the congestion pricing would be used to reduce the traffic on the network. This paper investigates the cordon-based second-best congestion-pricing problems on road networks, including optimal selection of both toll levels and toll locations. A road network is viewed as a directed graph and the cutest concept in graph theory is used to describe the mathematical properties of a toll cordon by examining the incidence matrix of the network. Maximization of social welfare is sought subject to the elastic-demand traffic equilibrium constraint. A mathematical programming model with mixed (integer and continuous) variables is formulated and solved by use of two genetic algorithms for simultaneous determination of the toll levels and cordon location on the networks. The model and algorithm are demonstrated in the road network of Mashhad CBD.

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Cost optimization of the reinforced concrete cantilever soil retaining wall of a given height satisfying some structural and geotechnical design constraints is performed utilizing harmony search and improved harmony search algorithms. The objective function considered is the cost of the structure, and design is based on ACI 318-05. This function is minimized subjected to design constraints. A numerical example of the cost optimization of a reinforced concrete cantilever retaining wall is presented to illustrate the performance of the presented algorithms and the necessary sensitivity analysis is performed.

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An evolutionary structural optimization (ESO) method is used for plastic design of frames. Based on safe theorems some criteria are derived and made an effort to satisfy them during the optimization process. In this regard, equilibrium is checked and yield condition is gradually satisfied during the optimization process. In this method, the amount of used material and the stiffness for each element are improved, simultaneously, to impose upper bound of moment in the element. Frame analysis and optimization algorithm are implemented as PLADOF (PLAstic Design of Frames) computer code. Four examples are presented to illustrate the performance of the algorithm

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This article presents the application of two algorithms: heuristic big bang-big crunch (HBB-BC) and a heuristic particle swarm

ant colony optimization (HPSACO) to discrete optimization of reinforced concrete planar frames subject to combinations of

gravity and lateral loads based on ACI 318-08 code. The objective function is the total cost of the frame which includes the cost

of concrete, formwork and reinforcing steel for all members of the frame. The heuristic big bang-big crunch (HBB-BC) is based

on BB-BC and a harmony search (HS) scheme to deal with the variable constraints. The HPSACO algorithm is a combination of

particle swarm with passive congregation (PSOPC), ant colony optimization (ACO), and harmony search scheme (HS)

algorithms. In this paper, by using the capacity of BB-BC in ACO stage of HPSACO, its performance is improved. Some design

examples are tested using these methods and the results are compared.

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This paper presents a strategy for using Harmony Search algorithm in facility layout optimization problems. In this paper an

adapted harmony search algorithm is developed for solving facility layout optimization problems. This method finds an optimal

facility arrangement in an existing layout. Two real-world case studies are employed to demonstrate the efficiency of this model.

A comparison is also made to illustrate the efficiency of these strategies in facility layout optimization

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Compared 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 tradeoff

between maximum safe drinking water with allowable chlorine residual levels and minimum adverse DBP levels.