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Abstract
Shear walls are a typical member under a complex stress state and have complicated mechanical properties and failure modes. The separated-elements model Genetic Evolutionary Structural Optimization (GESO), which is a combination of an elastic-plastic stress method and an optimization method, has been introduced in the literature for designing such members. Although the separated-elements model GESO method is well recognized due to its stability, feasibility, and economy, its adequacy has not been experimentally verified. This paper seeks to validate the adequacy of the separated-elements model GESO method against experimental data and demonstrate its feasibility and advantages over the traditional elastic stress method. Two types of reinforced concrete shear wall specimens, which had the location of an opening in the middle bottom and the center region, respectively, were utilized for this study. For each type, two specimens were designed using the separated-elements model GESO method and elastic stress method, respectively. All specimens were subjected to a constant vertical load and an incremental lateral load until failure. Test results indicated that the ultimate bearing capacity, failure modes, and main crack types of the shear walls designed using the two methods were similar, but the ductility indexes including the stiffness degradation, deformability, reinforcement yielding, and crack development of the specimens designed using the separated-elements model GESO method were superior to those using the elastic stress method. Additionally, the shear walls designed using the separated-elements model GESO method, had a reinforcement layout which could closely resist the actual critical stress, and thus a reduced amount of steel bars were required for such shear walls.

Key Words
shear wall; openings; genetic evolutionary algorithm; separated-elements model GESO; elastic stress method; structural optimization design

Address
Hu Z. Zhang: 1) Hunan Provincial Key Laboratory of Structures for Wind Resistance and Vibration Control, Hunan University of Science and Technology, Xiangtan, China 2) Hunan Provincial Key Lab on Damage Diagnosis for Engineering Structures, Hunan University, Changsha, China Xia Liu, Wei J. Yi and Yao H. Deng: Hunan Provincial Key Lab on Damage Diagnosis for Engineering Structures, Hunan University, Changsha, China

Abstract
In this article, a new method is introduced to improve the local search capability of meta-heuristic algorithms using the projection of the path on the border of constraints. In a mathematical point of view, the Gradient Projection Method is applied through a new approach, while the imposed limitations are removed. Accordingly, the gradient vector is replaced with a new meta-heuristic based vector. Besides, the active constraint identification algorithm, and the projection method are changed into less complex approaches. As a result, if a constraint is violated by an agent, a new path will be suggested to correct the direction of the agent\'s movement. The presented procedure includes three main steps: (1) the identification of the active constraint, (2) the neighboring point determination, and (3) the new direction and step length. Moreover, this method can be applied to some meta-heuristic algorithms. It increases the chance of convergence in the final phase of the search process, especially when the number of the violations of the constraints increases. The method is applied jointly with the authors\' newly developed meta-heuristic algorithm, entitled Star Graph. The capability of the resulted hybrid method is examined using the optimal design of truss and frame structures. Eventually, the comparison of the results with other meta-heuristics of the literature shows that the hybrid method is successful in the global as well as local search.

Key Words
hybrid optimization; global search; local search; meta-heuristic methods; classic methods

Address
Saeed Asil Gharebaghi and Mohammad Ardalan Asl: Department of Civil Engineering, K. N. Toosi University of Technology, Tehran, Iran

Abstract
In this work, an optimization method of Friction Tuned Mass Damper (FTMD) parameters is presented. Friction tuned mass dampers (FTMD) are attached to mechanical structures to reduce their vibrations with dissipating the vibratory energy through friction between both bodies. In order to exploit the performances of FTMD, the determination of the optimum parameters is recommended. However, the presence of Coulomb\'s friction force requires the resolution of a non-linear stick-slip problem. First, this work aims at determining the responses of the vibratory system. The responses of the main mass and of the FTMD are determined analytically in the sticking and sliding phase using the equivalent damping method. Second, this work aims to optimize the FTMD parameters; the friction coefficient and the tuned frequency. The optimization formulation based on the Ricciardelli and Vickery method at the resonance frequencies, this method is reformulated for a system with a viscous damping. The inverse problem of finding the FTMD parameters given the magnitude of the force and the maximum acceptable displacement of the primary system is also considered; the optimization of parameters leads to conclude on the favorable FTMD giving significant vibration decrease, and to advance design recommendations.

Key Words
tuned mass damper; coulomb friction; optimization parameters; vibration reduction

Address
Aymen Nasr, Charfeddine Mrad and Rachid Nasri: Laboratory of Applied Mechanics and Engineering (LMAI), National School of Engineers of Tunis (ENIT), University of Tunis El Manar (UTM), BP 37, Le Belvedere, 1002, Tunis, Tunisia

Abstract
This paper proposes a developed optimization model for steel frames with semi-rigid beam-to-column connections and fixed bases using teaching-learning-based optimization (TLBO) and genetic algorithm (GA) techniques. This method uses rotational deformations of frame members ends as an optimization variable to simultaneously obtain the optimum cross-sections and the most suitable beam-to-column connection type. The total cost of members plus connections cost of the frame are minimized. Frye and Morris (1975) polynomial model is used for modeling nonlinearity of semi-rigid connections, and the P-∆ effect and geometric nonlinearity are considered through a stepped analysis process. The stress and displacement constraints of AISC-LRFD (2016) specifications, along with size fitting constraints, are considered in the design procedure. The developed model is applied to three benchmark steel frames, and the results are compared with previous literature results. The comparisons show that developed model using both LTBO and GA achieves better results than previous approaches in the literature.

Key Words
teaching-learning-based optimization; genetic algorithm; steel frame optimization; semi-rigid connections; geometrically nonlinear; the P-∆ effect; rotational deformations variable

Address
Osman Shallan, Hassan M. Maaly and Osman Hamdy: Department of Structural Engineering, University of Zagazig, Zagazig, Egypt

Abstract
In this paper, a new meta-heuristic optimization method is presented. This new method is named \"Numbers Cup Optimization\" (NCO). The NCO algorithm is inspired by the sport competitions. In this method, the objective function and the design variables are defined as the team and the team members, respectively. Similar to all cups, teams are arranged in groups and the competitions are performed in each group, separately. The best team in each group is determined by the minimum or maximum value of the objective function. The best teams would be allowed to the next round of the cup, by accomplishing minor changes. These teams get grouped again. This process continues until two teams arrive the final and the champion of the Numbers Cup would be identified. In this algorithm, the next cups (same iterations) will be repeated by the improvement of players\' performance. To illustrate the capabilities of the proposed method, some standard functions were selected to optimize. Also, size optimization of three benchmark trusses is performed to test the efficiency of the NCO approach. The results obtained from this study, well illustrate the ability of the NCO in solving the optimization problems.

Key Words
optimization; meta-heuristic; standard function; truss structure; size optimization

Address
Mojtaba Riyahi Vezvari, Ali Ghoddosian and Amin Nikoobin: Faculty of Mechanical Engineering, Semnan University, Semnan, Iran

Abstract
Topology optimization of steel and concrete composite based on truss-like material model is studied in this paper. First, the initial design domain is filled with concrete, and the steel is distributed in it. The problem of topology optimization is to minimize the volume of steel material and solved by full stress method. Then the optimized steel and concrete composite truss-like continuum is obtained. Finally, the distribution of steel material is determined based on the optimized truss-like continuum. Several numerical results indicate the numerical instability and rough boundary are settled. And more details of manufacture and construction can be presented based on the truss-like material model. Hence, the truss-like material model of steel and concrete is efficient to establish the distribution of steel material in concrete.

Key Words
strut-and-tie; topology optimization; truss-like continuum; steel; concrete

Address
Yang Zhiyi and Zhou Kemin: College of Civil Engineering, Huaqiao University, Jimei, Xiamen, 361021, China Qiao Shengfang: School of Civil Engineering and Transportation, South China University of Technology, Tianhe, Guangzhou, 510641, China

Abstract
In this paper, a Cloud Model based Fruit Fly Optimization Algorithm (CMFOA) is presented for structural damage identification, which is a global optimization algorithm inspired by the foraging behavior of fruit fly swarm. It is assumed that damage only leads to the decrease in elementary stiffness. The differences on time-domain structural acceleration data are used to construct the objective function, which transforms the damaged identification problem of a structure into an optimization problem. The effectiveness, efficiency and accuracy of the CMFOA are demonstrated by two different numerical simulation structures, including a simply supported beam and a cantilevered plate. Numerical results show that the CMFOA has a better capacity for structural damage identification than the basic Fruit Fly Optimization Algorithm (FOA) and the CMFOA is not sensitive to measurement noise.

Key Words
damage identification; swarm intelligence; cloud model; fruit fly optimization algorithm; time domain data

Address
Tongyi Zheng, Jike Liu and Zhongrong Lu: Department of Applied Mechanics, Sun Yat-Sen University, Guangzhou, P.R. China Weili Luo: School of Civil Engineering, Guangzhou University, Guangzhou, P.R. China

Abstract
In this paper, a multi-objective multiparameter optimization procedure is developed by combining rigorously developed metamodels with an evolutionary search algorithm—Genetic Algorithm (GA). Response surface methodology (RSM) is used for developing the metamodels to replace the tedious finite element analyses. A nine-node isoparametric plate bending element is used for conducting the finite element simulations. Highly accurate numerical data from an author compiled FORTRAN finite element program is first used by the RSM to develop second-order mathematical relations. Four material parameters- E_1/E_2 , G_12/E_2 , G_23/E_2 and v_12 are considered as the independent variables while simultaneously maximizing fundamental frequency, lambda_1 and frequency separation between the 1st two natural modes, lambda_21. The optimal material combination for maximizing lambda_1 and lambda_21 is predicted by using a multi-objective GA. A general sensitivity analysis is conducted to understand the effect of each parameter on the desired response parameters.

Key Words
FE-surrogate; finite element; multi-objective; optimization; robust model

Address
Kanak Kalita, Pratik Nasre and Salil Haldar: Department of Aerospace Engineering and Applied Mechanics, Indian Institute of Engineering Science and Technology, Shibpur, Howrah 711 103, India Partha Dey: Department of Mechanical Engineering, Academy of Technology, Adisaptagram, Hooghly 712 121, India

Abstract
This paper presents a method for detecting damage in irregular 2D and 3D continuum structures based on combination of wavelet transform (WT) with fuzzy inference system (FIS) and particle swarm optimization (PSO). Many damage detection methods study regular structures. This method studies irregular structures and doesn\'t need response of healthy structures. First the damaged structure is analyzed with finite element methods, and damage response is obtained at the finite element points that have irregular distance, secondly the FIS, which is optimized by PSO is used to obtain responses at points, having equal distance by response at those points that previously obtained by the finite element methods. Then a 2D (for 2D continuum structures) or a 3D (for 3D continuum structures) matrix is performed by equal distance point response. Thirdly, by applying 2D or 3D wavelet transform on 2D or 3D matrix that previously obtained by FIS detail matrix coefficient of WT is obtained. It is shown that detail matrix coefficient can determine the damage zone of the structure by perturbation in the damaged area. In order to illustrate the capability of proposed method some examples are considered.

Key Words
discrete wavelet transform; damage detection; fuzzy inference system; particle swarm optimization

Address
Davood Hamidian, Eysa Salajegheh and Javad Salajegheh: Department of Civil Engineering, Shahid Bahonar University of Kerman, Kerman, 7618868366, Iran

Abstract
This paper deals with the maximization of the critical buckling load of simply supported antisymmetric angle-ply plates resting on Pasternak foundation subjected to compressive loads using teaching learning based optimization method (TLBO). The first order shear deformation theory is used to obtain governing equations of the laminated plate. In the present optimization problem, the objective function is to maximize the buckling load factor and the design variables are the fibre orientation angles in the layers. Computer programming is developed in the MATLAB environment to estimate optimum stacking sequences of laminated plates. A comparison also has been performed between the TLBO, genetic algorithm (GA) and differential evolution algorithm (DE). Some examples are solved to show the applicability and usefulness of the TLBO for maximizing the buckling load of the plate via finding optimum stacking sequences of the plate. Additionally, the influences of different number of layers, plate aspect ratios, foundation parameters and load ratios on the optimal solutions are investigated.

Key Words
laminated composite plates; Pasternak foundation; optimization; TLBO; buckling

Address
Umut Topal: Department of Civil Engineering, Faculty of Technology, Karadeniz Technical University, Trabzon, Turkey Trung Vo-Duy: 1) Division of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam 2) Faculty of Civil Engineering Ton Duc Thang University, Ho Chi Minh City, Vietnam Tayfun Dede: Department of Civil Engineering, Karadeniz Technical University, Trabzon, Turkey Ebrahim Nazarimofrad: Department of Civil Engineering, Bu Ali Sina University, Hamedan, Iran

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