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Abstract
In this paper, we introduce a new framework for running the finite element (FE) packages inside an online Loop together with MATLAB. Contrary to the Hardware-in-the-Loop techniques (HiL), in the proposed Software-in-the-Loop framework (SiL), the FE package represents a simulation platform replicating the real system which can be out of access due to several strategic reasons, e.g., costs and accessibility. Practically, SiL for sophisticated structural design and multi-physical simulations provides a platform for preliminary tests before prototyping and mass production. This feature may reduce the new product\'s costs significantly and may add several flexibilities in implementing different instruments with the goal of shortlisting the most cost-effective ones before moving to real-time experiments for the civil and mechanical systems. The proposed SiL interconnection is not limited to ABAQUS as long as the host FE package is capable of executing user-defined commands in FORTRAN language. The focal point of this research is on using the compiled FORTRAN subroutine as a messenger between ABAQUS/CAE kernel and MATLAB Engine. In order to show the generality of the proposed scheme, the limitations of the available SiL schemes in the literature are addressed in this paper. Additionally, all technical details for establishing the connection between FEM and MATLAB are provided for the interested reader. Finally, two numerical sub-problems are defined for offline and online post-processing, i.e., offline optimization and closed-loop system performance analysis in control theory.

Key Words
software-in-the-loop; finite element; optimal placement; structural optimization; vibration control

Address
Atta Oveisi, T. Arriessa Sukhairi and Tamara Nestorovic: Mechanics of Adaptive Systems, Institute of Computational Engineering, Ruhr-Universitat Bochum, Universitätsstr. 150, 44801 Bochum, Germany

Abstract
Temperature-induced responses, such as strains and displacements, are related to the boundary conditions. Therefore, it is required to determine the boundary conditions to establish a reliable bridge model for temperature-induced responses analysis. Particularly, bridge bearings usually present nonlinear behavior with an increase in load, and the nonlinear boundary conditions cause significant effect on temperature-induced responses. In this paper, the bridge nonlinear boundary conditions were simulated as bilinear translational or rotational springs, and the boundary parameters of the bilinear springs were identified based on the measured temperature-induced responses. First of all, the temperature-induced responses of a simply support beam with nonlinear translational and rotational springs subjected to various temperature loads were analyzed. The simulated temperature-induced strains and displacements were assumed as measured data. To identify the nonlinear translational and rotational boundary parameters of the bridge, the objective function based on the temperature-induced responses is then created, and the nonlinear boundary parameters were further identified by using the nonlinear least squares optimization algorithm. Then, a beam structure with nonlinear translational and rotational springs was simulated as a numerical example, and the nonlinear boundary parameters were identified based on the proposed method. The numerical results show that the proposed method can effectively identify the parameters of the nonlinear boundary conditions. Finally, the boundary parameters of a real arch bridge were identified based on the measured strain data and the proposed method. Since the bearings of the real bridge do not perform nonlinear behavior, only the linear boundary parameters of the bridge model were identified. Based on the bridge model and the identified boundary conditions, the temperature-induced strains were recalculated to compare with the measured strain data. The recalculated temperature-induced strains are in a good agreement with the real measured data.

Key Words
temperature-induced response; boundary condition; parameter identification; model updating; nonlinear least squares optimization

Address
Zuo-Cai Wang and Wei-Xin Ren: 1) Department of Civil Engineering, Hefei University of Technology, Hefei, 230009, Anhui, China 2) New Theory and Technology Research Platform of Bridge Structure Safety Monitoring, Hefei University of Technology, Hefei, 230009, China Guo-Peng Zha and Hao Yang: Department of Civil Engineering, Hefei University of Technology, Hefei, 230009, Anhui, China Ke Hu: Anhui Transportation Holding Group Co., Ltd., Hefei, 230088, China

Abstract
One of the important problems in the vehicle design is vehicle handling and stability. Effective parameters which should be considered in the vehicle handling and stability are roll angle, camber angle and scrub radius. In this paper, a planar vehicle model is considered that two right and left suspensions are double wishbone suspension system. For a better analysis of the suspension geometry, a kinestatic model of vehicle is considered which instantaneous kinematic and statics relations are analyzed simultaneously. In this model, suspension geometry is considered completely. In order to optimum design of double wishbones suspension system, a multi-objective genetic algorithm is applied. Three important parameters of suspension including roll angle, camber angle and scrub radius are taken into account as objective functions. Coordinates of suspension hard points are design variables of optimization which optimum values of them, corresponding to each optimum point, are obtained in the optimization process. Pareto solutions for three objective functions are derived. There are important optimum points in these Pareto solutions which each point represents an optimum status in the model. In other words, corresponding to any optimal point, a specific geometric position is determined for the suspension hard points. Each of the obtained points in the Pareto optimization can be selected for a special design purpose by designer to create an optimum condition in the vehicle handling and stability.

Key Words
multi-objective optimization; double wishbone suspension; kinestatic analysis; vehicle handling and stability

Address
Mohammad Reza Bagheri, Masoud Mosayebi and Asghar Mahdian: Department of Mechanical Engineering, Malek-Ashtar University of Technology, Isfahan, Iran Ahmad Keshavarzi: Department of Mechanical Engineering, Khomeinishahr Branch, Islamic Azad University, Isfahan, Iran

Abstract
Optimizing composite structures to exploit their maximum potential is a realistic application with promising returns. In this research, simultaneous maximization of the fundamental frequency and frequency separation between the first two modes by optimizing the fiber angles is considered. A high-fidelity design optimization methodology is developed by combining the high-accuracy of finite element method with iterative improvement capability of metaheuristic algorithms. Three powerful nature-inspired optimization algorithms viz. a genetic algorithm (GA), a particle swarm optimization (PSO) variant and a cuckoo search (CS) variant are used. Advanced memetic features are incorporated in the PSO and CS to form their respective variants-RPSOLC (repulsive particle swarm optimization with local search and chaotic perturbation) and CHP (co-evolutionary host-parasite). A comprehensive set of benchmark solutions on several new problems are reported. Statistical tests and comprehensive assessment of the predicted results show CHP comprehensively outperforms RPSOLC and GA, while RPSOLC has a little superiority over GA. Extensive simulations show that the on repeated trials of the same experiment, CHP has very low variability. About 50% fewer variations are seen in RPSOLC as compared to GA on repeated trials.

Key Words
cuckoo search; finite element method; genetic algorithm; multiobjective optimization; particle swarm optimization

Address
Kanak Kalita: Department of Aerospace Engineering and Applied Mechanics, Indian Institute of Engineering, Science and Technology, Howrah, West Bengal 711103, India Uvaraja Ragavendran: Department of Electronics and Telecommunication Engineering, MPSTME SVKM'S Narsee Monjee Institute of Management Studies, Shirpur, Maharashtra 425405, India Manickam Ramachandran: Department of Mechanical Engineering, MPSTME SVKM'S Narsee Monjee Institute of Management Studies, Shirpur, Maharashtra 425405, India Akash Kumar Bhoi: Department of Electrical & Electronics Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Majhitar, Sikkim 737136, India

Abstract
In this paper, a hybrid uncertain model is applied to system reliability based design optimization (RBDO) of trusses. All random variables are described by random distributions but some key distribution parameters of them which lack information are defined by variation intervals. For system RBDO of trusses, the first order reliability method, as well as monotonicity analysis and the branch and bound method, are utilized to determine the system failure probability; and Improved (Mu+Lambda) constrained differential evolution (ICDE) is employed for the optimization process. System reliability assessment of several numerical examples and system RBDO of different truss structures are proposed to verify our results. Moreover, the effect of different classes of interval distribution parameters on the optimum weight of the structure and the reliability index are also investigated. The results indicate that the weight of the structure is increased by increasing the uncertainty level. Moreover, it is shown that for a certain random variable, the optimum weight is more increased by the translation interval parameters than the rotation ones.

Key Words
reliability based design optimization; interval distribution parameters; improved (Mu+Lambda) constrained differential evolution (ICDE); structural optimization

Address
Mohammad Zaeimi and Ali Ghoddosian: Faculty of Mechanical Engineering, Semnan University, Semnan, Iran

Abstract
This paper performs for the first time a simultaneous optimization for members sections along with semi-rigid beam-to-column connections for space steel frames with fixed, semi-rigid, and hinged bases using a biogeography-based optimization algorithm (BBO) and a genetic algorithm (GA). Furthermore, a member\'s sections optimization for a fully fixed space frame is carried out. A real and accurate simulation of semi-rigid connection behavior is considered in this study, where the semi-rigid base connections are simulated using Kanvinde and Grilli (2012) nonlinear model, which considers deformations in different base connection components under the applied loads, while beam-to-column connections are modeled using the familiar Frye and Morris (1975) nonlinear polynomial model. Moreover, the P-∆ effect and geometric nonlinearity are considered. AISC-LRFD (2016) specification constraints of the stress and displacement are considered as well as section size fitting constraints. The optimization is applied to two benchmark space frame examples to inspect the effect of semi-rigidity on frame weight and drift using BBO and GA algorithms.

Key Words
genetic algorithm; biogeography-based optimization; semi-rigid space steel frame; optimization; semi-rigid base

Address
Osman Shallan, Hassan M. Maaly and Osman Hamdy: Department of Structural Engineering, University of Zagazig, Zagazig, Egypt Merve Sagiroglu: Department of Civil Engineering, Technical University of Erzurum, Erzurum, Turkey

Abstract
This research focuses on finite element model updating and damage assessment of structures at element level based on global nondestructive test results. For this purpose, an optimization system is generated to minimize the structural dynamic parameters discrepancies between numerical and experimental models. Objective functions are selected based on the square of Euclidean norm error of vibration frequencies and modal assurance criterion of mode shapes. In order to update the finite element model and detect local damages within the structural members, modern optimization techniques is implemented according to the evolutionary algorithms to meet the global optimized solution. Using a simulated numerical example, application of genetic algorithm (GA), particle swarm (PSO) and artificial bee colony (ABC) algorithms are investigated in FE model updating and damage detection problems to consider their accuracy and convergence characteristics. Then, a hybrid multi stage optimization method is presented merging advantages of PSO and ABC methods in finding damage location and extent. The efficiency of the methods have been examined using two simulated numerical examples, a laboratory dynamic test and a high-rise building field ambient vibration test results. The implemented evolutionary updating methods show successful results in accuracy and speed considering the incomplete and noisy experimental measured data.

Key Words
Finite Element Model updating; GA; PSO; ABC; hybrid optimization; damage detection, modal analysis

Address
School of Civil Engineering, College of Engineering, University of Tehran, 16 Azar St, Enghelab Ave., Tehran, Iran

Abstract
The paper concerns topology and geometry optimization of statically determinate beams with arbitrary number of supports. The optimization problem is treated as a bi-criteria one, with the objectives of minimizing the absolute maximum bending moment and the maximum deflection for a uniform gravity load. The problem is formulated and solved using the Pareto optimality concept and the lexicographic ordering of the objectives. The non-dominated sorting genetic algorithm NSGA-II and the local search method are used for the optimization in the Pareto sense, whereas the genetic algorithm and the exhaustive search method for the lexicographic optimization. Trade-offs between objectives are examined and sets of Pareto-optimal solutions are provided for different topologies. Lexicographically optimal beams are found assuming that the maximum moment is a more important criterion. Exact formulas for locations and values of the maximum deflection are given for all lexicographically optimal beams of any topology and any number of supports. Topologies with lexicographically optimal geometries are classified into equivalence classes, and specific features of these classes are discussed. A qualitative principle of the division of topologies equivalent in terms of the maximum moment into topologies better and worse in terms of the maximum deflection is found.

Key Words
Pareto optimality; lexicographic ordering; beams; topology and geometry optimization; bending moment; deflection

Address
Faculty of Architecture, Bialystok University of Technology, Oskara Sosnowskiego 11, 15-351 Bialystok, Poland

Abstract
In the present work, the effects of cutting parameters on surface roughness parameters (Ra), tool wear parameters (VBmax), tool vibration (Vy) and material removal rate (MRR) during hard turning of AISI 4140 steel using coated carbide tool have been evaluated. The relationships between machining parameters and output variables were modeled using response surface methodology (RSM). Analysis of variance (ANOVA) was performed to quantify the effect of cutting parameters on the studied machining parameters and to check the adequacy of the mathematical model. Additionally, Multi-objective optimization based desirability function was performed to find optimal cutting parameters to minimize surface roughness, and maximize productivity. The experiments were planned as Box Behnken Design (BBD). The results show that feed rate influenced the surface roughness; the cutting speed influenced the tool wear; the feed rate influenced the tool vibration predominantly. According to the microscopic imagery, it was observed that adhesion and abrasion as the major wear mechanism.

Key Words
cutting parameter; surface roughness; wear; vibration; hard turning; coated carbide; multi-objective optimization

Address
Ouahid Keblouti: Département Construction Mécanique et Productique, FGM&GP. Université des Sciences et de la Technologie Houari Boumediene. B.P. 32, El-Alia Bab-Ezzouar 16111, Alger, Algérie Ouahid Keblouti, Lakhdar Boulanouar, Mohamed W. Azizi and Abderrahim Bouziane: Advanced Technologies in Mechanical Production Research Laboratory (LRTAPM), Badji Mokhtar Annaba University BP12, Annaba, 23000, Algeria. Mohamed W. Azizi : Département science technique/Centre Universitaire Abdelhafid Boussouf- Mila, 43000, Algeria

Abstract
This study proposed an improved particle swarm optimization (IPSO) method ensemble with kriging model for model updating. By introducing genetic algorithm (GA) and grouping strategy together with elite selection into standard particle optimization (PSO), the IPSO is obtained. Kriging metamodel serves for predicting the structural responses to avoid complex computation via finite element model. The combination of IPSO and kriging model shall provide more accurate searching results and obtain global optimal solution for model updating compared with the PSO, Simulate Annealing PSO (SimuAPSO), BreedPSO and PSOGA. A plane truss structure and ASCE Benchmark frame structure are adopted to verify the proposed approach. The results indicated that the hybrid of kriging model and IPSO could serve for model updating effectively and efficiently. The updating results further illustrated that IPSO can provide superior convergent solutions compared with PSO, SimuAPSO, BreedPSO and PSOGA.

Key Words
model updating; kriging metamodel; improved particle swarm optimization; elite selection; global best solution

Address
Shiqiang Qin, Jia Hu, Yazhou Zhang and Juntao Kang: School of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan 430070, China Yun-Lai Zhou: Department of Civil and Environmental Engineering, National University of Singapore, 2 Engineering Drive 2, 117576, Singapore

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