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This article introduces two swarm intelligent algorithms, a group search optimizer (GSO) and an artificial fish swarm algorithm (AFSA). A single intelligent algorithm always has both merits in its specific formulation and deficiencies due to its inherent limitations. Therefore, we propose a mixture of these algorithms to create a new hybrid optimization algorithm known as the group search-artificial fish swarm algorithm (GS-AFSA). This algorithm has been applied to three different discrete truss optimization problems. The optimization results are compared with those obtained using the standard GSO, the AFSA and the quick group search optimizer (QGSO). The proposed GS-AFSA eliminated the shortcomings of GSO regarding falling into the local optimum by taking advantage of AFSA’s stable convergence characteristics and achieving a better convergence rate and convergence accuracy than the GSO and the AFSA. Furthermore, the GS-AFSA has a superior convergence accuracy compared to the QGSO, all while solving a complicated structural optimization problem containing numerous design variables.

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A multi-objective heuristic particle swarm optimiser (MOHPSO) based on Pareto multi-objective theory is proposed to solve multi-objective optimality problems. The optimality objectives are the roof displacement and structure weight. Two types of structure are analysed in this paper, a truss structure and a framework structure. Performance-based seismic analysis, such as classical and modal pushover analysis, is carried out for the structures. Four optimality algorithms, namely, NSGA-II, MOPSO, MGSO, and MOHPSO, were used for structural optimisation to compare the effectiveness of the algorithms. The calculation results indicate that MOHPSO outperformed the other algorithms in terms of solution stability, universality, and consistency of the distribution of the Pareto front and the ability to consider constraints. The population can converge to the true Pareto front in the latter generations, which indicates that MOHPSO is effective for engineering multi-objective optimality problems.

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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 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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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  paper,  a  method  is  presented  for  damage  detection  of  bridges  using  the  Enhanced Colliding Bodies Optimization (ECBO)  utilizing time-domain responses. The finite element modeling of the structure is based on  the equation of motion under the moving load, and the flexural stiffness of the structure is determined by the acceleration responses obtained via sensors placed in different places. Damage detection problem presented in this research is an inverse  problem,  which  is  optimized  by  the  ECBO  algorithm,  and  the  damages  in  the structures  are  fully  detected.  Furthermore,  for  simulating  the  real  situation,  the  effect  of measured noises is considered on the structure, to obtain more accurate results.

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The Isogeometric Analysis (IA) is utilized for structural topology optimization  considering minimization of weight and local stress constraints. For this purpose, material density of the structure  is  assumed  as  a  continuous  function  throughout  the  design  domain  and approximated using the Non-Uniform Rational B-Spline (NURBS) basis functions. Control points of the density surface are considered as design variables of the optimization problem that can change the topology during the optimization process. For initial design, weight and stresses of the structure are obtained based on full material density over the design domain. The  Method  of  Moving  Asymptotes  (MMA)  is  employed  for  optimization  algorithm. Derivatives of the objective function and constraints with respect to the design variables are determined  through  a  direct  sensitivity  analysis.  In  order  to  avoid  singularity  a  relaxation technique  is  used  for  calculating  stress  constraints.  A  few  examples  are  presented  to demonstrate the performance of the method. It is shown that using the IA method and an appropriate stress relaxation technique can lead to reasonable optimum layouts.

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In this study an efficient meta-heuristic is proposed for layout optimization of truss structures by combining cellular automata (CA) and firefly algorithm (FA). In the proposed meta-heuristic, called here as cellular automata firefly algorithm (CAFA), a new equation is presented for position updating of fireflies based on the concept of CA. Two benchmark examples of truss structures are presented to illustrate the efficiency of the proposed algorithm. Numerical results reveal that the proposed algorithm is a powerful optimization technique with improved convergence rate in comparison with other existing algorithms.

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The travel times among demand points are strongly influenced by traffic in a supply chain. Due to this fact, the service times for customers are variable. For this reason, service time is often changes over a time interval in a real environment. In this paper, a time-dependent periodic green vehicle routing problem (VRP) considering the time windows for serving the customers and multiple trip is developed with this assumption that urban traffic would disrupt timely services. The objective function of proposed problem is to minimize the total amount of carbon dioxide emissions produced by the vehicle, earliness and lateness penalties costs and costs of used vehicles. At first, a novel linear integer mathematical model is formulated and then the model is validated via solving some test problems by CPLEX solver. Finally, the sensitivity analysis is carried out to study the role of two critical parameters in the optimal solution.

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The objective of the present paper is to propose a sequential enhanced colliding bodies optimization (SECBO) algorithm for implementation of seismic optimization of steel braced frames in the framework of performance-based design (PBD). In order to achieve this purpose, the ECBO is sequentially employed in a multi-stage scheme where in each stage an initial population is generated based on the information derived from the results of previous stages. The required structural seismic responses, at performance levels, are evaluated by performing nonlinear pushover analysis. Two numerical examples are presented to illustrate the efficiency of the proposed SECBO for tackling the seismic performance-based optimization problem. The numerical results demonstrate the computational advantages of the SECBO algorithm.

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Calculation of lateral earth pressure on retaining walls is one of the main issues in geotechnics. The upper and lower bound theorems of plasticity are used to analyze the stability of geotechnical structures include bearing capacity of foundations, lateral earth pressure on retaining walls and factor of safety of slopes. In this paper formulation of finite element limit analysis is introduced to determine plastic limit load in the perfect plastic materials. Elements with linear strain rates, which are used in the formulation, cause to eliminate the necessity of velocity discontinuities between the elements. Using non-linear programming based on second order cone programming (SOCP), which has good conformity with cone yield functions such as Mohr-Coulomb and Drucker-Prager, is another important advantage that remove the problem of using ordinary linear programming algorithms for yield functions such as divergent in the apexes. Finally, the optimization problem will be solved by mathematical method. The proposed method is used for calculating active earth pressure on retaining walls in cohesive-frictional soils. According to results of analysis, active earth force on retaining wall is decreased by increasing soil cohesion, wall inclination friction angle between backfill and wall and friction angle of soil wherein the force is increased by increasing surcharge on the backfill and the backfill slope. Mathematical method is used for obtaining accurate results in this research, however, heuristic methods are used when approximate solutions are sufficient.

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In this paper, a systematic approach is presented for optimal design of tunnel support lining using two-dimensional finite element analysis models of soil-structure interaction developed in ABAQUS software and the Modified Colliding Bodies Optimization (MCBO) algorithm implemented in MATLAB environment. This approach is then employed to study the influence of variable geometrical and geo-mechanical parameters on the optimal design of a class of practical access tunnels.

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Optimization techniques can be efficiently utilized to achieve an optimal shape for arch dams. This optimal design can consider the conditions of the economy and safety simultaneously. The main aim is to present an applicable and practical model and suggest an algorithm for optimization of concrete arch dams to enhance their seismic performance. To achieve this purpose, a preliminary optimization is accomplished using PSO procedure in the first stage. Capabilities of Ansys Parametric Design Language (APDL) are applied for modeling the Dam-Foundation-Reservoir system. In the second stage with training the neural network, Group Method of Data Handling (GMDH) and replacement of Ansys analyst, optimal results have been achieved with the lowest error and less number of iteration respectively. Then a real world double-arch dam is presented to demonstrate the effectiveness and practicality of the PSO-GMDH. The numerical results reveal that the proposed method called PSO-GMDH provides faster rate and high searching accuracy to achieve the optimal shape of arch concrete dams and the modification and optimization of shape have a quite important role in increasing the safety against dynamic design loads.

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Growing tendency for Urbanization and rapid development of the cities has resulted in urban neighborhoods obstructing the access of each other to the natural sources e.g. solar energy, natural ventilation. Sunlight as the main part of input energy in urban energy balance equation and natural lighting is of vital importance. This paper attempts to achieve an optimum morphology for residential blocks in urban area with the highest exposure to the sunlight. To reach this goal a pilot area in Tabriz’s downtown was selected and regarding solar angle, local street regulations and the width of surrounding streets 3 different scenarios for the buildings blocks were defined. Using a three-dimensional microclimate model, ENVI-met, solar access of defined scenarios was calculated for the longest and the shortest day of the year. Results showed that Type C2 (highest, more open spaces) is a more efficient style for winter times as it receives more of the sun’s energy and also the amount of sun it gets during a day and type B2 (medium open space and height) is the better for summer as it gets less energy from the sun and it is exposed to sunlight less than other types in a hot summer day.

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Reinforced concrete (RC) slabs exhibit complexities in their structural behavior under load due to the composite nature of the material and the multitude and variety of factors that affect such behavior. Current methods for determining the load-deflection behavior of reinforced concrete slabs are limited in scope and are mostly dependable on the results of experimental tests. In this study, an alternative approach using Artificial Neural Network (ANN) model is produced to predict the load-deflection behavior of a two-way RC slab. In the study, 30 sets of RC slab specimens of sizes 700mm x 600mm x 75mm were cast, cured for 28days using the sprinkling method of curing and tested for deflection experimentally by applying loads ranging from 10kN to 155kN at intervals of 5kN. ANN model was then developed using the neural network toolbox of ANN in MATLAB version R2015a using back propagation algorithm. About 54% of the RC specimens were used for the training of the network while 23% of the sets were used for validation leaving the remaining 23 % for testing the network. The experimental test results show that the higher the applied load on the slab, the higher the deflection. The result of the ANN model shows a good correlation between the experimental test and the predicted results with training, validation and test correlation coefficients of 0.99692, 0.98921 and 0.99611 respectively. It was also found that ANN model is quite efficient in determining the deflection of 2-way RC slab. The predicted accuracy of performance value for the load-deflection set falls at 96.67% of the experimental load-deflection with a 0.31% minimum error using the Microsoft spreadsheet model. As such the comprehensive spreadsheet tool created to incorporate the optimum neural network. The spreadsheet model uses the Microsoft version 2013 excel tool software and can be used by structural engineers for instantaneous access to the prediction if any aspect of a concrete slab behavior given minimal data to describe the slab and the loading condition.

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Leakage detection in water distribution systems play an important role in storage and management of water resources. Therefore, to reduce water loss in these systems, a method should be introduced that reacts rapidly to such events and determines their occurrence time and location with the least possible error. In this study, in order to determine position and amount of leakage in distribution system, a detection method based on hydraulic model was evaluated using Extended Kalman Filter (EKF), which is a non-linear Kalman Filter. The results indicated that the method was well able to predict leakage position and its amount. Using a numerical model, a leakage was placed in 25.4 m distance of its upstream, amounting to 1.33 lit/sec which was equal to 10 percent of overall flow. The calculated mean position and leakage value by EKF were 27.17 m and 1.11 lit/sec, respectively.

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Damage assessment is one of the crucial topics in the operation of structures. Multiplicities of structural elements and joints are the main challenges about damage assessment of space structure. Vibration-based damage evaluation seems to be effective and useful for application in industrial conditions and the low-cost. A method is presented to detect and assess structural damages from changes in mode shapes. First, the mechanism of using two-dimensional continuous wavelet transform is applied for damage localization. Second, finite element model updating technique is utilized as an inverse optimization problem by applying the charged system search algorithm to assess the damage in each element sited in the first stage. The study indicates the potentiality of the developed code to assess the damages of space structures without concerning about the size and shape of structure. A series of numerical examples with different damage scenarios have been carried out in the double layer space structures and the results confirm the reliability and applicability of introduced method.

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In this paper an optimization framework is presented for automated performance-based seismic design of bridges consisting of multi-column RC pier substructures. The beneficial effects of fusing components on seismic performance of the quasi-isolated system is duly addressed in analysis and design. The proposed method is based on a two-step structural analysis consisting of a linear modal dynamic demand analysis and a nonlinear static capacity evaluation of the entire bridge structure. Results indicate that the proposed optimization method is capable of producing cost-effective design solutions combining the fusing behavior of bearings and yielding mechanism of piers. The optimal designs obtained from models addressing the performance of fusing components are far more efficient than those that do not take care of quasi-isolation behavior. 

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Vulnerability assessment of structures encounter many uncertainties like seismic excitations intensity and response of structures. The most common approach adopted to deal with these uncertainties is vulnerability assessment through fragility functions. Fragility functions exhibit the probability of exceeding a state namely performance-level as a function of seismic intensity. A common approach is finding some response points of the fragility function and then fitting a typical probability distribution like lognormal through curve fitting estimation techniques. Maximum-likelihood approach is a fitting method to find the probability distribution parameters. Performing this approach for distributions like lognormal which is defined by just two parameters are straight forward while for more complicated distribution which are based on additional characterizing parameters is not feasible, since this approach is based on minimizing an error function through classic mathematical approaches like calculating partial derivations. An applicable modification is to add an efficient optimization approach to determine maximum-likelihood function. In this article, an optimization algorithm is proposed with maximum-likelihood-estimation and the results indicate the efficiency and feasibility of future developments in finding the most appropriate fragility function.