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To ensure efficient performance of irrigation canals, the losses from the canals need to be minimized. In this paper a modified formulation is presented to solve the optimization model for the design of different canal geometries for minimum seepage loss, in meta-heuristic environment. The complex non-linear and non-convex optimization model for canal design is solved using a probabilistic search algorithm namely Probabilistic Global Search Lausanne (PGSL). The solutions are found to be competitive to those reported in literature while applied for different example problems. To suit for real field applications, three site specific constraints are considered and the sensitivity of solutions for the most popular trapezoidal canals is investigated. The study shows the potential of the proposed approach to perform optimal design of irrigation canals for minimum seepage loss.

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The main contribution of the present paper is to train efficient neural networks for seismic design of double layer grids subject to multiple-earthquake loading. As the seismic analysis and design of such large scale structures require high computational efforts, employing neural network techniques substantially decreases the computational burden. Square-on-square double layer grids with the variable length of span and height are considered. Back-propagation (BP), radial basis function (RBF) and generalized regression (GR) neural networks are trained for efficiently prediction of the seismic design of the structures. The numerical results demonstrate the superiority of the GR over the BP and RBF neural networks.

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The Big Bang-Big Crunch (BB–BC) method is a relatively new meta-heuristic algorithm which inspired by one of the theories of the evolution of universe. In the BB–BC optimization algorithm, firstly random points are produced in the Big Bang phase then these points are shrunk to a single representative point via a center of mass or minimal cost approach in the Big Crunch phase. In this paper, the BB–BC algorithm is presented for optimal cost design of water distribution systems and employed to optimize different types of hydraulic networks with discrete variables. The results demonstrate the efficiency of the proposed method compared to other algorithms.

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The present study is an attempt to propose a mutation-based real-coded genetic algorithm (MBRCGA) for sizing and layout optimization of planar and spatial truss structures. The Gaussian mutation operator is used to create the reproduction operators. An adaptive tournament selection mechanism in combination with adaptive Gaussian mutation operators are proposed to achieve an effective search in the design space. The standard deviation of design variables is used as a key factor in the adaptation of mutation operators. The reliability of the proposed algorithm is investigated in typical sizing and layout optimization problems with both discrete and continuous design variables. The numerical results clearly indicated the competitiveness of MBRCGA in comparison with previously presented methods in the literature.

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In this paper the performance of Artificial Neural Networks (ANNs) and Adaptive Neuro- Fuzzy Inference Systems (ANFIS) in simulating the inverse dynamic behavior of Magneto- Rheological (MR) dampers is investigated. MR dampers are one of the most applicable methods in semi active control of seismic response of structures. Various mathematical models are introduced to simulate the dynamic behavior of MR dampers. The Modified Bouc-Wen model is an appropriate model that has an acceptable accuracy in calculating the generated force of dampers compared to others. In this model displacement and voltage of a MR damper are known while the force generated by MR damper is considered as the unknown. Because of highly nonlinear characteristics of modified bouc-wen model determination of inverse dynamic behavior of MR dampers are generally done using ANNs and ANFIS. Since the ANNs and ANFIS have different mechanisms for emulating desired functions, their responses may be different. In this research the performance of a Back Propagation Neural Network (BPNN), Radial Basis Functions Neural Network (RBFNN) and ANFIS in estimating the inverse dynamic behavior of MR dampers are compared. The results emphasize on the advancement of ANFIS to the other methods studied in estimation of inverse dynamic behavior of MR dampers.

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Meta-heuristics have already received considerable attention in various engineering optimization fields. As one of the most rewarding tasks, eigenvalue optimization of truss structures is concerned in this study. In the proposed problem formulation the fundamental eigenvalue is to be maximized for a constant structural weight. The optimum is searched using Particle Swarm Optimization, PSO and its variant PSOPC with Passive Congregation as a recent meta-heuristic. In order to make further improvement an additional hybrid PSO with genetic algorithm is also proposed as PSOGA with the idea of taking benefit of various movement types in the search space. A number of benchmark examples are then treated by the algorithms. Consequently, PSOGA stood superior to the others in effectiveness giving the best results while PSOPC had more efficiency and the least fit ones belonged to the Standard PSO.

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The main objective of this paper is to use ant optimized neural networks to generate artificial earthquake records. In this regard, training accelerograms selected according to the site geology of recorder station and Wavelet Packet Transform (WPT) used to decompose these records. Then Artificial Neural Networks (ANN) optimized with Ant Colony Optimization and resilient Backpropagation algorithm and learn to relate the dimension reduced response spectrum of records to their wavelet packet coefficients. Trained ANNs are capable to produce wavelet packet coefficients for a specified spectrum, so by using inverse WPT artificial accelerograms obtained. By using these tools, the learning time of ANNs reduced salient and generated accelerograms had more spectrum-compatibility and save their essence as earthquake accelerograms.

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In this research, optimal design and assessment of multiple tuned mass dampers (MTMDs) capability in mitigating the damage of nonlinear steel structures subjected to earthquake excitation has been studied. Optimal parameters of TMDs on nonlinear multi-degree-of-freedom (MDOF) structures have been determined based on minimizing the maximum relative displacement (drift) of structure where for solving the optimization problem the genetic algorithm (GA) has been used successfully. For numerical analysis, three and nine storey 2-D moment resisting nonlinear steel frames subjected to far-field and near-field earthquakes and optimal MTMDs has been designed for different values of mass ratio and TMDs number. According to the results of numerical simulations, it can be said that MTMDs mechanism could reduce the damage of nonlinear steel structures where the effectiveness increases by increasing TMDs mass ratio. Also the performance of MTMDs depends on earthquake characteristics, mass ratio and TMDs configuration where in this research the effective case has been locating TMDs on top floor in parallel configuration.

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Runway length is usually a critical point in an airport system so, a great interest has been created for optimal use of this runway length. The most important factors in modeling of aircraft landing problem are time and cost while, the costs imposed on the system because of moving away from target times have different performances in terms of impact. In this paper, firstly, aircraft landing problem (ALP) and the works conducted in subject literature are briefly reviewed and presented. Then, this problem is formulated and proposed as a three-objective mathematical modeling which leads to more applicable formulation. Following this, the model introduced to solve this problem is solved for two groups including 20 and 50 aircrafts using the second version of NSGA and the results and recommendations will be provided.

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The interbasin water transfer is a remedy to mitigate the negative issues of water shortage in arid and semi-arid regions. In  a water transfer project  the receiving basin always  benefits while, the sending basin may suffer. In this study, the project of interbasin water transfer from Dez water resources system in south-west of Iran to the central part of the contrary is 
investigated during a drought period. To this end, a multi-objective optimization model is developed  based  on  the  Non  Dominated  Sorting  Genetic  Algorithm  (NSGA-II).  The optimum trade-off between the water supply benefits into and out of the Dez River basin as well  as  energy  production  is  derived.  Formulating  the  problem  as  a  multi-objective 
optimization provides a better insight into the gains and losses of a water transfer project. Analyzing the case study, revealed that to reach an acceptable level of reliability for meeting the water demands it is no longer possible to generate hydropower energy with high levels of reliability. 

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In this study, optimum combinations of available rainfall gauging stations are selected by a model which is consist of geo statistics model as an estimator  and an optimized model. At the  first,  watershed  is  approximated  to  several  regular  geometric  shapes.  Then  kriging calculates  the  variance  of  the  estimation  error  of  different  combinations  from  available rainfall gauging stations using inside and outside stations of watershed. In each combination, n is number of considered stations and N is number of available stations (N>n). At the end, the best combination is selected by genetic algorithm (the  error variance of this combination is minimum). For optimal set with one sample point (station) estimator model and optimize model select station that locates near to center of watershed. While for two stations case, these models select two stations that l ocate in boundaries face to face. Also for combination n  stations  of  N  stations,  selected  stations  have good  and  proportional  distribution  in watershed. These results show correctness of research methodology.
In this study, effects of variations of paramet ers of theoretical variogram and number of blocks  in  block  estimation  of  kriging  method  are  evaluated  too.  The  variance  of  the estimation error from block estimation with 8*8 blocks has showed the acceptable results.
This research shows a linear relation between variations of error variance and scale of variogram. Optimum combination does not vary with variations of scale of variogram but it varies with variations of range of variogram. Increasing of nugget effect of variogram would raise the variance but does not vary optimum combinations.

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The paper is concerned with a methodology of optimal design of shells of minimum weight with strength, stability and strain constraints. Stress and strain state of the shell is determined by Galerkin method in the mixed finite element formulation within the geometrically nonlinear theory. The analysis of the effectiveness of different optimization algorithms to solve the set problem is given. The results of solving test problems are presented.

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In the present study, two new hybrid approaches are proposed for predicting peak ground acceleration (PGA) parameter. The proposed approaches are based on the combinations of Adaptive Neuro-Fuzzy System (ANFIS) with Genetic Algorithm (GA), and with Particle Swarm Optimization (PSO). In these approaches, the PSO and GA algorithms are employed to enhance the accuracy of ANFIS model. To develop hybrid models, a comprehensive database from Pacific Earthquake Engineering Research Center (PEER) are used to train and test the proposed models. Earthquake magnitude, earthquake source to site distance, average shear-wave velocity, and faulting mechanisms are used as predictive parameters. The performances of developed hybrid models (PSO-ANFIS-PSO and GA-ANFIS-GA) are compared with the ANFIS model and also the most common soft computing approaches available in the literature. According to the obtained results, three developed models can be effectively used to predict the PGA parameter, but the comparison of models shows that the PSO-ANFIS–PSO model provides better results.

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The objective of this work is to predict the temperature of the different types of walls which are Ferro cement wall, reinforced cement concrete (RCC) wall and two types of cavity walls (combined RCC with Ferrocement and combined two Ferro cement walls) with the help of mathematical modeling. The property of low thermal transmission of small air gap between the constituents of combine materials has been utilized to obtain energy efficient wall section. Ferro cement is a highly versatile form of reinforced concrete made up of wire mesh, sand, water, and cement, which possesses unique qualities of strength and serviceability. The significant intention of the proposed technique is to frame a mathematical modeling with the aid of optimization techniques. Mathematical modeling is done by minimizing the cost and time consumed in the case of extension of the existing work. Mathematical modeling is utilized to predict the temperature of the different wall such as RCC wall, Ferro cement, combined RCC with Ferro cement and combined Ferro cement wall. The different optimization algorithms such as Social Spider Optimization (SSO), Genetic Algorithm (GA) and Group Search Optimization (GSO) are utilized to find the optimal weights α and β of the mathematical modeling. All optimum results demonstrate that the attained error values between the output of the experimental values and the predicted values are closely equal to zero with the SSO model. The results of the proposed work are compared with the existing methods and the minimum errors with SSO algorithm for the case of two combined RCC wall was found to be less than 2%.

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In this article, by Partitioning of designing space, optimization speed is tried to be increased by GA. To this end, designing space search is done in two steps which are global search and local search. To achieve this goal, according to meshing in FEM, firstly, the list of sections is divided to specific subsets. Then, intermediate member of each subset, as representative of subset, is defined in a new list. Optimization process is started based on the new list of sections which includes subset’s representatives (global search). After some specific generations, range of optimum design is indicated for each designing variable. Afterwards, the list of sections is redefined relative to previous step’s result and based on subset of relevant variable. Finally, optimization will be continued based on the new list of sections for each designing variable to complete the generations (local search). In this regard, effect of dimension and number of subset’s members of global and local searches in proposal are investigated by optimization examples of skeletal structures. Results imply on optimization speed enhancement based on proposal in different cases proportional to simple and advanced cases of GA.

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Risks are natural and inherent characteristics of major projects. Risks are usually considered independently in analysis of risk responses. However, most risks are dependent on each other and independent risks are rare in the real world. This paper proposes a model for proper risk response selection from the responses portfolio with the purpose of optimization of defined criteria for projects. This research has taken into account the relationships between risk responses; especially the relationships between risks, which have been rarely considered in previous works. It must be pointed out that not considering or superficial evaluation of the interactions between risks and risk responses reduces the expected desirability and increases project execution costs. This model is capable of optimization of different criteria in the objective function based on the proposed projects. Multi-objective Harmony Search (MOHS) and Non-dominated Sorting Genetic Algorithm II (NSGA-II) are used to solve this model and the numerical results obtained are analyzed. Finally, it was observed that ranges of objective functions in MOHS are better than those in NSGA-II.

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Design of blast resistant structures is an important subject in structural engineering, attracting the attention of governments, researchers, and engineers. Thus, given the benefits of optimization in engineering, development and assessment of optimization methods for optimum design of structures against blast is of great importance. In this research, multi-objective optimization of steel moment frames subjected to blast is investigated. The considered objectives are minimization of the structural weight and minimization of the maximum inter-story drifts. The minimization of weight is related to obtain low cost designs and the minimization of inter-story drifts is related to obtain higher performance designs. By proposing a design methodology, a framework is developed for solving numerical problems. The developed framework is constructed by combining explicit finite element analysis of the structure and the NSGA-II optimization algorithm. The applicability and efficiency of the proposed method is shown through two numerical examples.

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Flow number of asphalt–aggregate mixtures as an explanatory factor has been proposed in order to assess the rutting potential of asphalt mixtures. This study proposes a multiple–kernel based support vector machine (MK–SVM) approach for modeling of flow number of asphalt mixtures. The MK–SVM approach consists of weighted least squares–support vector machine (WLS–SVM) integrating two kernel functions in order to improve the learning and generalization ability of WLS–SVM. In the proposed method, a linear convex combination of the radial basis function (RBF) and Morlet wavelet kernel functions is adopted, which are considered as the most popular kernel functions. To validate the efficiency of the proposed method, experiments are conducted on a database including 118 uniaxial dynamic creep test results. The results of the statistical criteria show a good agreement between the predicted and measured flow number values. Further, the simulation results demonstrate that the proposed MK–SVM approach has more superior performance than the single kernel based WLS–SVM and other methods found in the literature.

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This paper aims to obtain the optimal distance between the adjacent structures using Particle Swarm Optimization (PSO) algorithm considering structure-soil-structure systems; The optimization algorithm has been prepared in MATLAB software and connected into OpenSees software (where the structure-soil-structure system has been analyzed by the direct approach). To this end, a series of adjacent structures with various slenderness have been modeled on the three soil types according to Iranian seismic code (Standard No. 2800) using the direct method. Then they have been analyzed under six earthquake excitations with different risk levels (low, moderate, and high).
The results are compared with the proposed values of separation gap between adjacent structures in the Iranian seismic code (Standard No. 2800). Results show that since structures with the same height constructed on a stiff soil will move in the same phase, there is no need to put distance between them. Although, the structures with the height more than 6-story frames where are located on a soft soil are needed to be separated. Additionally, the results show more separation gap between two adjacent structures when the risk level of earthquake is high. In general, the values which are presented in Standard No. 2800 are not suitable for low /moderate-rise structures specially when they are subjected to a high-risk level earthquake and are located on a soft soil and this separation gap should be increased about 10 to 90 percentage depend on the conditions but these values are appropriate for the adjacent structures with same height where are subjected to a low-risk level earthquakes built on soft soil.

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This paper presents a parametric study of steel I- beam with stepped flanges by using finite element analysis. Stepped flange beam is used in structures to decrease the negative bending moments near interior supports that causes failure due to buckling. Steps in the cross section can be achieved by adding cover plates to the beam flanges, changing the size of the hot rolled section, or changing the flange thickness and/or width for built-up section. The stress concentration with variation in stepped beam configuration such as doubly and singly stepped I-beams has been examined thoroughly. The loadings are limited to those having an inflection point of zero under point load at mid span. Beams with degree of symmetry, ρ of 0.2 are investigated for the present study. Unbraced length to height ratio of the beam to be analyzed is considered as 15. In addition, to check the effect of steps, stepped parameters α, β and γ are varied. The results shows that, a change of flange thickness is more significant than a change of flange width on the lateral torsional buckling capacity of a singly stepped beam.