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In this paper, optimal design of arch dams is performed under frequency limitations. Colliding Bodies Optimization (CBO), a recently developed meta-heuristic optimization method, which has been successfully applied to several structural problems, is revised and utilized for finding the best feasible shape of arch dams. The formulation of CBO is derived from one-dimensional collisions between bodies, where each agent solution is considered as the massed object or body. The design procedure aims to obtain minimum weight of arch dams subjected to natural frequencies, stability and geometrical limitations. Two arch dam examples from the literature are examined to verify the suitability of the design procedure and to demonstrate the effectiveness and robustness of the CBO in creating optimal design for arch dams. The results of the examples show that CBO is a powerful method for optimal design of arch dams.

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During the planning phase of modern, complex, block-structured, large-area located, but still landscape-harmonized health-care buildings, the key is the optimal positioning of the blocks and functions, simultaneously ensuring the most-effective backup-paths for any transportation route failure in the buildings in order to speed up system operation, reduce maintenance costs and especially to improve patient safety and satisfaction. The importance of improving reliability and boundary conditions of the modelling in modern complex health-care building-systems are emphasized. A cost efficient pre-phase solution of mathematical, graph modelling is presented, with introducing link doubling to linearize a two segment, non-linear capacity-cost function. The developed and detailed mathematical graph model can be used as part of the architectural planning workflow. This model allows distinguishing the sharable part from the free part of capacity on a link in case of simultaneously routing multiple protection paths. Link doubling allows finding optimal routing of shared protection paths for failure cases. Two algorithms are proposed for routing of the guaranteed bandwidth pipes with shared protection which provides reliable building structures through thrifty additional resources. It is assumed that a single working path can be protected by one or multiple protection paths, which are partially or fully disjoint from the working one. This approach allows better capacity sharing among protection paths. The main aim of the recommendations is to achieve a reliable, fully operational building even if a failure, a reconditioning or emergency situation happens.

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For most practical purposes, true topology optimization of a braced frame should be synchronized with its sizing. An integrated layout optimization is formulated here to simultaneously account for both member sizing and bracings’ topology in such a problem. Code-specific seismic design spectrum is applied to unify the earthquake excitation. The problem is solved for minimal structural weight under codified stress, deformation and also user-defined weak-storey and architectural constraints. Particle swarm optimization is hybridized with an extra memory consideration strategy to solve this problem. As another issue, Baldwin effect of memetic algorithm is utilized in the proposed method to enhance its search capability regarding the geometrical and topological constraints. Treating a number of planar braced frames revealed superior performance of the proposed hybrid method partiqularly in avoiding premature convergence over the common particle swarm optimiztion for such a discrete problem.

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This paper presents the application of metaheuristic methods to the minimum crossing number problem for the first time. These algorithms including particle swarm optimization, improved ray optimization, colliding bodies optimization and enhanced colliding bodies optimization. For each method, a pseudo code is provided. The crossing number problem is NP-hard and has important applications in engineering. The proposed algorithms are tested on six complete graphs and eight complete bipartite graphs and their results are compared with some existing methods.

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Practical design of tall frame-tube and diagrids are formulated as two discrete optimization problems searching for minimal weight undercodified constraints under gravitational and wind loading due to Iranian codes of practice for steel structures (Part 6 & Part 10). Particular encoding of design vector is proposed to efficiently handle both problems leading to minimal search space. Two types of modeling are employed for the sizing problem one by rigid floors without rotational degrees of freedom and the other with both translational and rotational degrees of freedom. The optimal layout of diagrids using rigid model is searched as the second problem. Then performance of Mine Blast Optimization as a recent meta-heuristic is evaluated in these problems treating a number of three-dimensional structural models via comparative study with the common Harmony Search and Particle Swarm Optimization. Considerable benefit in material cost minimization is obtained by these algorithms using tuned parameters. Consequently, effectiveness of HS is observed less than the other two while MBO has shown considerable convergence rate and particle swarm optimiztion is found more trustable in global search of the second problem.

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A Reliability-Based Design Optimization (RBDO) framework is presented that accounts for stochastic variations in structural parameters and operating conditions. The reliability index calculation is itself an iterative process, potentially employing an optimization technique to find the shortest distance from the origin to the limit-state boundary in a standard normal space. Monte Carlo simulation (MCs) is embedded into a design optimization procedure by a modular double loop approach, which the self-adaptive version of particle swarm optimization method is introduced as an optimization technique. Double loop method has the advantage of being simple in concepts and easy to implement. First, we study the efficiency of self-adaptive PSO algorithm inorder to solve the optimization problem in reliability analysis and then compare the results with the Monte Carlo simulation. While computationally significantly more expensive than deterministic design optimization, the examples illustrate the importance of accounting for uncertainties and the need for regarding reliability-based optimization methods and also, should encourage the use of PSO as the best of evolutionary optimization methods to more such reliability-based optimization problems.

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This paper presents a Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) for the optimal operation of a complex multipurpose and multi-reservoir system. Firstly, MOEA/D decomposes a multi-objective optimization problem into a number of scalar optimization sub-problems and optimizes them simultaneously. It uses information of its several neighboring sub-problems for optimizing each sub-problem. This simple procedure makes MOEA/D have lower computational complexity compared with non-dominated sorting genetic algorithm II (NSGA-II). The algorithm (MOEA/D) is compared with the Genetic Algorithm (NSGA-II) using a set of common test problems and the real-world Zohre reservoir system in southern Iran. The objectives of the case study include water supply of minimum flow and agriculture demands over a long-term simulation period. Experimental results have demonstrated that MOEA/D can improve system performance to reduce the effect of drought compared with NSGA-II superiority. Therefore, MOEA/D is highly competitive and recommended to solve multi-objective optimization problems for water resources planning and management.

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A new meta-heuristic algorithm is proposed for optimal design of reinforced concrete (RC) frame structures subject to combinations of gravity and lateral static loads based on ACI 318-08 design code. In the present work, artificial bee colony algorithm (ABCA) is focused and an improved ABCA (IABCA) is proposed to achieve the optimization task. The total cost of the RC frames is minimized during the optimization process subject to constraints on demand capacity ratios (DCRs) of structural members. Three benchmark design examples are tested using ABCA and IABCA and the results are compared with those of presented in the literature. The numerical results indicate that the proposed IABCA is an efficient computational tool for discrete optimization of RC frames.

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The shape optimization of gravity dam is posed as an optimization problem with goals of minimum value of concrete, stresses and maximum safety against overturning and sliding need to be achieved. Optimally designed structure generally saves large investments especially for a large structure. The size of hydraulic structures is usually huge and thus requires a huge investment. If the optimization techniques are employed in the design stage, the project investment can be effectively minimized. There are many optimization techniques were used to optimize the gravity dam. In the present work, optimization of gravity dam is carried out using the differential evolution technique. Differential evolution is an evolutionary algorithm which process iteratively to locate best solution in the large search space. Searching of optimal solution to a problem is carried out by the process of mutation, cross over and reproduction from the initial developed candidate solutions. After undergoing a number of iterations, it is possible to get the minimum cross sectional area of dam which can satisfy various constraints and thus the reduction in volume of concrete can be achieved. From the results obtained, it is found that differential evolution is one of the efficient techniques for solving such a problem over continuous space. The success of differential evolution in solving a specific problem critically depends on appropriately choosing trial vector generation strategies and their associated control parameter value. The optimum solution obtained is compared with analytical method and it is found that there is 20.44 % of reduction in the requirement of concrete is envisaged.

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Castellated beams are generally provided with hexagonal and circular openings in the web portion. However, in view of structural applications, appropriate size and shape of openings in web are always a major issue of concern. Research work carried out in optimizing sizes of castellated beam with hexagonal openings have reported that castellated beams fail mainly by local failure modes and stress concentrations at opening edges. Castellated beams with sinusoidal openings offer better performance due to its increased area for stress distribution in addition to curved edges that causes smooth stress distribution. Few researchers have studied flexural behaviour of castellated beams with sinusoidal openings however, optimization for size of such openings has not been reported so far. The paper focuses on parametric study of castellated beam with sinusoidal openings for optimization of opening size. Finite element analysis (FEA) is carried out by Abaqus software and also by Eurocode for different opening sizes and results obtained is experimentally validated. Results show that, castellated beam with sinusoidal opening of size 0.55times the overall depth of beam gives better strength.

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Location and types of sensors may be integrated for simultaneous achievement of water security goals and other water utility objectives, such as regulatory monitoring requirements. Complying with the recent recommendations on dual benefits of sensors, this study addresses the optimal location of these types of sensors in a multipurpose approach. The study presents two mathematical models for optimum location of sensors as static double use benefit model (SDUBM) and dynamic double use benefit model (DDUBM) which provides tradeoffs between maximum monitored volume of water known as “demand coverage” and minimum consumption of contaminated water. In the proposed modeling scheme, sensors are located to maximize dual use benefits of achieving water security goals and accomplishing regulatory monitoring requirements. The validity of the model is tested using two extensively tested example problems with multi-objective ant colony optimization (ACO) algorithm. The Pareto front for different number of sensors are presented and discussed.

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In this paper, for topology optimization of double layer grids, an efficient optimization method is presented by combination of Imperialist Competitive Algorithm (ICA) and Gravitational Search Algorithm (GSA) which is called ICA-GSA method. The present hybrid method is based on ICA but the moving of countries toward their relevant imperialist is done using the law of gravity of GSA. In topology optimization process, the weight of the structure is minimized subjected to displacements of joints, internal stress and slenderness ratio of members constraints. Through numerical example, topology optimization of a typical large-scale double layer grid is obtained by ICA, GSA and ICA-GSA methods. The numerical results indicate that the proposed algorithm, ICA-GSA, executes better than ICA, GSA and the other methods presented in the literatures for topology optimization of largescale skeletal structures.

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In this paper the piecewise level set method is combined with phase field method to solve the shape and topology optimization problem. First, the optimization problem is formed based on piecewise constant level set method then is updated using the energy term of phase field equations. The resulting diffusion equation which updates the level set function and optimization problem is solved through finite element method. The proposed method enhances the convergence rate and solution efficiency. Various two-dimensional examples are solved to verify the performance of proposed method.

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This study has been inspired by the paper "An efficient 3D topology optimization code written in MATLAB” written by Liu and Tovar (2014) demonstrating that SIMP-based three-dimensional (3D) topology optimization of continuum structures can be implemented in 169 lines of MATLAB code. Based on the above paper, we show here that, by simple and easy-to-understand modifications we get a few lines longer code, which is able to solve robust topology optimization problems with uncertain load directions. In the presented worst load direction oriented approach, the varying load directions are handled by quadratic constrains, which describe spherical regions about the nominal loads. The result of the optimization is a robust compliance-minimal volume constrained design, which is invariant to the investigated directional uncertainty. The key element of the robustification is a worstload-direction searching process, which is formulated as a small quadratic programming problem with quadratic constraints. The presented approach is a 3D extension of the robust approach originally developed by Csébfalvi (2014) for 2D continuum structures. In order to demonstrate the viability and efficiency of the extension, we present the model and algorithm with detailed benchmark results for robust topology optimization of 3D continuum structures. It will be demonstrated that the computational cost of the robustification is comparable with its deterministic equivalent because its central element is a standard 3D deterministic multi-load structure optimization problem and the worst-loaddirection searching process is formulated as a significantly smaller quadratically constrained quadratic programming problem, which can be solved efficiently by several different ways.

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This study presents shape optimization of a gravity dam imposing stability and principal stress constraints. A gravity dam is a large scale hydraulic structure consisting of huge amount of concrete material. Hence, an optimum design gives a cost-benefit structure due to the fact that small changes in shape of dam cross-section leads to large saving of concrete volume. Three recently developed meta-heuristics are utilized for optimizing the structure. These algorithms are charged system search (CSS), colliding bodies optimization (CBO) and its enhanced edition (ECBO). This article also provides useful formulations for stability analysis of gravity dams which can be extended to further researches.

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This paper presents a new model updating approach for structural damage localization and quantification. Based on the Modal Assurance Criterion (MAC), a new damage-sensitive cost function is introduced by employing the main diagonal and anti-diagonal members of the calculated Generalized Flexibility Matrix (GFM) for the monitored structure and its analytical model. Then, the cost function is solved by Democratic Particle Swarm Optimization (DPSO) algorithm to achieve the optimal solution of the problem lead to damage identification. DPSO is a modified version of standard PSO algorithm which is developed for presenting a fast speed evolutionary optimization strategy. The applicability of the method is demonstrated by studying three numerical examples which consists of a ten-story shear frame, a plane steel truss and a plane steel frame. Several challenges such as the efficiency of the DPSO algorithm in comparison with other evolutionary optimization approaches for solving the inverse problem, impacts of random noise in input data on the reliability of the presented method, and effects of the number of available modal data for damage identification, are studied. The obtained results reveal good, robust and stable performance of the presented method for structural damage identification using only the first several modes’ data.

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In rigid plastic analysis one of the most widely applicable methods that is based on the minimum principle, is the combination of elementary mechanisms which uses the upper bound theorem. In this method a mechanism is searched which corresponds to the smallest load factor. Mathematical programming can be used to optimize this search process for simple frames, and meta-heuristic algorithms are the best choice for larger frame structures. In this paper, the Colliding Bodies Optimization (CBO) and its enhanced variant (ECBO) are employed to optimize the process of finding an upper bound for the collapse load factor of the planar frames. The efficiency of these algorithms is compared to that of the Particle Swarm Optimization (PSO) algorithm through four numerical examples form multi-bay multi-story frames and pitched roof frames.

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The present paper tackles the optimization problem of double layer grids considering nonlinear behaviour. In this paper, an efficient optimization algorithm is proposed to achieve the optimization task based on the newly developed grey wolf algorithm (GWA) termed as sequential GWA (SGWA). In the framework of SGWA, a sequence of optimization processes is implemented in which the initial population of each process is selected from the neighboring region of the best design found in the previous optimization process. This procedure is repeated until a termination criterion is met. Two illustrative examples are presented and optimization is performed by GWA and SGWA and two other meta-heuristics. The numerical results indicate that the proposed SGWA utperforms the other algorithms in finding optimal design of nonlinear double layer grids.

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Inverse Transient Analysis (ITA) is a powerful approach for leak detection of pipelines. When the pipe transient flow is analyzed in frequency domain the ITA is called Inverse Frequency Response Analysis (IFRA). To implement an IFRA for leak detection, a transient state is initiated in the pipe by fast closure of the downstream end valve. Then, the pressure time history at the valve location is measured. Using the Fast Fourier Transform (FFT) the measured signal is transferred into the frequency domain. Besides, using the transfer matrix method, a frequency response analysis model for the pipeline is developed as a function of the leak parameters including the number, location and size of leaks. This model predicts the frequency responses of the pipe in return for any random set of leak parameters. Then, a nonlinear inverse problem is defined to minimize the discrepancies between the observed and predicted responses at the valve location. To find the pipeline leaks the method of Particle Swarm Optimization (PSO) is coupled to the transient analysis model while, the leak parameters are the optimization decision variables. The model is successfully applied against an example pipeline and in both terms of efficiency and reliability the results are satisfactory.

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Slope stability is one of the most complex and essential issues for civil and geotechnical engineers, mainly due to life and high economical losses resulting from these failures. In this paper, a new approach is presented for estimating the Safety Factor (SF) for circular failure slope using hybrid support vector regression (SVR) and Ant Colony Optimization (ACO). The ACO is combined with the SVR for determining the optimal value of its user-defined parameters. The optimization implementation by the ACO significantly improves the generalization ability of the SVR. In this research, the input data for the SF estimation consists of the values of geometrical and geotechnical input parameters. As an output, the model estimates the SF that can be modeled as a function approximation problem. A data set that includes 46 data points is applied in current study, while 32 data points are used for constructing the model, and the remainder data points (14 data points) are used for assessment of the degree of accuracy and robustness. The results obtained show that the hybrid SVR with ACO model can be used successfully for estimation of the SF.