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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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A least squares finite element method for the .solution of steady incompressible Navier Stokes equations is presented. The Navier-.Stocks equation is first recast into a system of first order partial differential equations with the velocitv. pressure and the vorticity as the main variables. Finite element discretization of the domain introduces a residual in the governing equation which is subsequently minimized in a least squares sense. The method so developed clearly. falls into the minimization category card hence circumventing the L.B.B. condition. Furthermore. the method produces symmetric positive definite matrices which makes the way for using more efficient iterative sobers. A Conjugate Gradient algorithm is, therefore, used for the solution of the resulting .system of linear algebraic equations. To improve the efficiency , of this iterative solver an incomplete Cholesky factorization of the stiffness matrix is used as ct pre-conditioner. Since the storage requirement of the Cholesky factor depends on the bandwidth of matrix. an effective algorithm for the reduction of this bandwidth has also been employed. The application of the method to solve cavity problem and .step flow with different Remolds number is presented to show the applicability of the method to solve practical flows of incompressible fluid The use of both linear and quadratic elements with selective reduced integration is also investigated and the results are presented.

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It is indispensable to explore simulation techniques that not only represent complexdynamic systems in a realistic way but also allow the involvement of end users in modeldevelopment to increase their confidence in the modeling process. System dynamics as a feedbackbasedand object-oriented simulation approach is presented for reservoir operation modeling. Thequick modeling process, the trust developed in the model due to user contribution, group modelsdevelopment possibility and the effective relations of model results are the most significant strongpoints of this approach. The simple modification of model in response to changes in system andcapability to accomplish sensitivity analysis make this approach more attractive and useful ratherthan traditional reservoir operation models. In this paper system dynamics is applied to simulateoperation of a free reservoir with an Ogee spillway, a reservoir with a gated spillway and finally amulti-reservoir system with simple and gated spillways. The multi-reservoir system on Karun riverin south of Iran is modeled under flood condition as a case study in order to demonstrate thecapabilities of the developed model.

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An efficient selection operator for use in genetic search of pipe networks optimal design is introduced in this paper. The proposed selection scheme is the superior member of a family of improved selection operators developed in an attempt to more closely simulate the main features of the natural mating process which is not reflected in existing selection schemes. The mating process occurring in the nature exhibits two distinct features. First, there is a competition between phenotypes looking for the fittest possible mate which usually ends up with choosing a mate with more or less the same fitness. Second, and more importantly, the search for a mate is often confined to a community of phenotypes rather than the whole population. Four different selection operators simulating these features in a random and pre-determined manner are developed and tested. All the selection schemes exhibit good convergence characteristics, in particular the one in which both the size of the sub-community and the pair of the mates in the sub-community are determined randomly. The efficiency of the proposed selection operator is shown by applying the method for the optimal design of three well-known benchmark networks, namely two-loop, Hanoi and New-York networks. The proposed scheme produces results comparable to the best results presented in the literature with much less computational effort

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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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The use of Value Engineering (VE) methodology in construction industry has grown significantly, mainly in view of its extensive benefits. The main task in evaluation phase of VE workshop is to assess alternative ideas, proposed for each function. This phase of VE, hence, could be deemed as a Multi Criteria Decision Making (MCDM) problem. This paper presents a fuzzy decision support system (DSS) to be employed in evaluation phase of VE. The proposed multi alternative decision model may be recommended where alternatives’ preferences ratios are different, and scores assigned to each alternative idea are uncertain. As use of VE has greater payoffs at the earlier stages of the construction projects, in which most of the criteria are still vague and not precisely defined, exploiting this DSS may result in more tangible model of decision making process and satisfactory outlook of VE studies in construction projects. A ranking methodology in a spreadsheet template is also provided to facilitate the ranking process. Performance of the proposed methodology is tested using a case example in the tunneling industry.

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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 meshless method namely, discrete least square method (DLSM), is presented in the paper for the solution of free surface seepage problem. In this method computational domain is discredited by some nodes and then the set of simultaneous equations are built using moving least square (MLS) shape functions and least square technique. The proposed method does not need any background mesh therefore it is a truly meshless method. Several numerical two dimensional examples of Poisson partial differential equations (PDEs) are presented to illustrate the performance of the present DLSM. And finally a free surface seepage problem in a porous media is solved and results are presented.

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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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In a large scale cyclic storage system ,as the number of rule parameters and/or number of operating period increase, general purpose gradient-based NLP solvers and/or genetic algorithms may loose their merits in finding optimally feasible solution to the problem. In these cases hybrid GA which decomposes the main problem into two manageable sub-problems with an iterative scheme between GA and LP solvers may be considered as a sound alternative This research develops a hybrid GA-LP algorithm to optimally design and operate a nonlinear, non-convex, and large scale lumped cyclic storage system. For optimal operation of the system a set of operating rules are derived for joint utilization of surface and groundwater storage capacities to meet a predefined demand with minimal construction and operation cost over a 20 seasonal planning period. Performance of the proposed model is compared with a non-cyclic storage system. The management model minimizes the present value of the design and operation cost of the cyclic and non-cyclic systems under specified and governing constraints, employing the developed GA-LP hybrid model. Results show that cyclic storage dominates non-cyclic storage system both in cost and operation flexibility.

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Presence of risks and uncertainties inherent in project development and implementation plays

significant role in poor project performance. Thus, there is a considerable need to have an effective risk

analysis approach in order to assess the impact of different risks on the project objectives. A powerful risk

analysis approach may consider dynamic nature of risks throughout the life cycle of the project, as well as

accounting for feedback loops affecting the overall risk impacts. This paper presents a new approach to

construction risk analysis in which these major influences are considered and quantified explicitly. The

proposed methodology is a system dynamics based approach in which different risks may efficiently be

modeled, simulated and quantified in terms of time, cost and quality by the use of the implemented object

oriented simulation methodology. To evaluate the performance of the proposed methodology it has been

employed in a bridge construction project. Due to the space limitations, the modeling and quantification

process for one of the identified risks namely “pressure to crash project duration” is explained in detail.

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A meshless approach, collocation discrete least square (CDLS) method, is extended in this paper, for solving

elasticity problems. In the present CDLS method, the problem domain is discretized by distributed field nodes. The field

nodes are used to construct the trial functions. The moving least-squares interpolant is employed to construct the trial

functions. Some collocation points that are independent of the field nodes are used to form the total residuals of the

problem. The least-squares technique is used to obtain the solution of the problem by minimizing the summation of the

residuals for the collocation points. The final stiffness matrix is symmetric and therefore can be solved via efficient

solvers. The boundary conditions are easily enforced by the penalty method. The present method does not require any

mesh so it is a truly meshless method. Numerical examples are studied in detail, which show that the present method

is stable and possesses good accuracy, high convergence rate and high efficiency.

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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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This research presents a dynamic mathematical system for modeling and simulating the quality management process in construction projects. Through sets of cause and effect feedback loops, all factors that internally and externally affect the quality management process are addressed. The proposed system integrates fuzzy logic with system dynamics simulation scheme to consider the uncertainties associated with the model parameters and estimation of the extra cost and time due to quality defects. Quantification of the consequences of the quality failures is performed based on the α-cut representation of fuzzy numbers and interval analysis. The proposed approach is efficient in modeling and analyzing a quality management process which is complex and dynamic in nature and involves various uncertainties. The proposed approach is implemented in a real submarine water supply pipe line project in order to evaluate its applicability and performance. The negative impacts resulting from quality failures are simulated. These negative impacts are mitigated by the implementation of alternative solutions.

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The Stone-column is a useful method for increasing the bearing capacity and reducing settlement of foundation soil. The prediction of accurate ultimate bearing capacity of stone columns is very important in soil improvement techniques. Bulging failure mechanism usually controls the failure mechanism. In this paper, an imaginary retaining wall is used such that it stretches vertically from the stone column edge. A simple analytical method is introduced for estimation of the ultimate bearing capacity of the stone column using Coulomb lateral earth pressure theory. Presented method needs conventional Mohr-coloumb shear strength parameters of the stone column material and the native soil for estimation the ultimate bearing capacity of stone column. The validity of the developed method has been verified using finite element method and test data. Parametric studies have been carried out and effects of contributing parameters such as stone column diameter, column spacing, and the internal friction angle of the stone column material on the ultimate bearing capacity have been investigated.