• Journal of the Korean Society of Industry Convergence
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• v.23 no.5
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• pp.799-807
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• 2020

This paper deals with identification of probabilistic design using reliability based robust optimization in structure design of automatic salt collector. The thickness sizing variables of main structure member in the automatic salt collector were considered the random design variables including the uncertainty of corrosion that would be an inevitable hazardousness in the saltern work environment. The probabilistic constraint functions were selected from the strength performances of the automatic salt collector. The reliability based robust optimum design problem was formulated such that the random design variables were determined by minimizing the weight of the automatic salt collector subject to the probabilistic strength performance constraints evaluating from reliability analysis. Mean value reliability method and adaptive importance sampling method were applied to the reliability evaluation in the reliability based robust optimization. The three sigma level quality was considered robustness in side constraints. The probabilistic optimum design results according to the reliability analysis methods were compared to deterministic optimum design results. The reliability based robust optimization using the mean value reliability method showed the most rational results for the probabilistic optimum structure design of the automatic salt collector.

• Structural Engineering and Mechanics
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• v.45 no.4
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• pp.471-494
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• 2013

In this paper, identification of isotropic and orthotropic linear elastic material constitutive parameters has been demonstrated by a FEA-free energy-based inverse analysis method. An important feature of the proposed method is that it requires no finite element (FE) simulation of the tested material. Full-field displacements calculated using digital image correlation (DIC) are used to compute DIC stress fields enforcing the equilibrium condition and DIC strain fields using interpolation functions. Boundary tractions and displacements are implicitly recast into an objective function that measures the energy residual of external work and internal elastic strain energy. The energy conservation principle states that the residual should be zero, and so minimizing this objective function inversely identifies the constitutive parameters. Synthetic data from simulated testing of isotropic materials and orthotropic composite materials under 2D plane stress conditions are used for verification of the proposed method. When identifying the constitutive parameters, it is beneficial to apply loadings in multiple directions, and in ways that create non-uniform stress distributions. The sensitivity of the parameter identification method to noise in both the measured full-field DIC displacements and loadings has been investigated.

• Steel and Composite Structures
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• v.32 no.6
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• pp.731-741
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• 2019

Shear deformation effects are neglected in most structural system identification methods. This assumption might lead to important errors in some structures like built up steel or composite deep beams. Recently, the observability techniques were presented as one of the first methods for the inverse analysis of structures including the shear effects. In this way, the mechanical properties of the structures could be obtained from the nodal movements measured on static tests. One of the main controversial features of this procedure is the fact that the measurement set must include rotations. This characteristic might be especially problematic in those structures where rotations cannot be measured. To solve this problem and to increase its applicability, this paper proposes an update of the observability method to enable the structural identification including shear effects by measuring only vertical deflections. This modification is based on the introduction of a numerical optimization method. With this aim, the inverse analysis of several examples of growing complexity are presented to illustrate the validity and potential of the updated method.

• Smart Structures and Systems
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• v.30 no.4
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• pp.339-351
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• 2022

Evaluating the current condition of existing structures is of primary importance for economic and safety reasons. This can be addressed by Structural System Identification (SSI). A reliable static SSI depends on well-designed sensor configuration and loading cases, as well as efficient parameter estimation algorithms. Static SSI by the Measurement Error-Minimizing Observability Method (MEMOM) is a model-based deterministic static SSI method that could estimate structural parameters from static responses. In the current state of the art, this method is only applicable when structures are subjected to one loading case. This might lead to lack of information in some local regions of the structure (such as the null curvatures zones). To address this issue, the SSI by MEMOM using multiple loading cases is proposed in this work. Observability equations obtained from different loading cases are concatenated simultaneously and an optimization procedure is introduced to obtain the estimations by minimizing the discrepancy between the predicted response and the measured one. In addition, a Genetic-Algorithm (GA)-based Optimal Sensor Placement (OSP) method is proposed to tackle the OSP problem under multiple static loading cases for the very first time. In this approach, the Fisher Information Matrix (FIM)'s determinant is used as the metric of the goodness of sensor configurations. The numerical examples of a 3-span continuous bridge and a 13-story frame, are analyzed to validate the applicability of the extended SSI by MEMOM and the GA-based OSP method.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.5
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• pp.555-565
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• 1999

The optimal identification algorithm of fuzzy systems is presented for rule-based fuzzy modeling of nonlinear complex systems. Nonlinear systems are expressed using the identification of structure such as input variables and fuzzy input subspaces, and parameters of a fuzzy model. In this paper, the rule-based fuzzy modeling implements system structure and parameter identification using the fuzzy inference methods and hybrid structure combined with two types of optimization theories for nonlinear systems. Two types of inference methods of a fuzzy model are the simplified inference and linear inference. The proposed hybrid optimal identification algorithm is carried out using both a genetic algorithm and the improved complex method. Here, a genetic algorithm is utilized for determining initial parameters of membership function of premise fuzzy rules, and the improved complex method which is a powerful auto-tuning algorithm is carried out to obtain fine parameters of membership function. Accordingly, in order to optimize fuzzy model, we use the optimal algorithm with a hybrid type for the identification of premise parameters and standard least square method for the identification of consequence parameters of a fuzzy model. Also, an aggregate performance index with weighting factor is proposed to achieve a balance between performance results of fuzzy model produced for the training and testing data. Two numerical examples are used to evaluate the performance of the proposed model.

• Structural Engineering and Mechanics
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• v.86 no.1
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• pp.93-107
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• 2023

In this paper, a density based topology optimization is proposed for generating of supports required in additive manufacturing to maintain the overhanging regions of main structures during layer by layer fabrication process. For this purpose, isogeometric analysis method is employed to model geometry and structural analysis of main and support structures. In order to model the problem two cases are investigated. In the first case, design domain of supports can easily be separated from the main structure by using distinct isogeometric patches. The second case happens when the main structure itself is optimized by using topology optimization and the supports should be designed in the voids of optimum layout. In this case, in order to avoid boundary identification and re-meshing process for separating design domain of supports from main structure, a parameterization technique is proposed to identify the design domain of supports. To achieve this, two density functions are defined over the entire domain to describe the main structure and supporting areas. On the other hand, since supports are under gravity loads while main structure and its stiffness is not completed during manufacturing process, in the proposed method, stiffness of the main structure is considered to be trivial and the gravity loads are also naturally applied to design support structures. By doing so, the results show reasonable supports are created to protect, continuously, overhanging surfaces of the main structure. Several examples are presented to demonstrate the efficiency of the proposed method and compare the results with literature.

• Structural Engineering and Mechanics
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• v.52 no.1
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• pp.149-172
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• 2014

Four algorithms for damage detection of trusses are presented in this paper. These approaches can detect damage by using both complete and incomplete measurements. The suggested methods are based on the minimization of the difference between the measured and analytical static responses of structures. A non-linear constrained optimization problem is established to estimate the severity and location of damage. To reach the responses, the successive quadratic method is used. Based on the objective function, the stiffness matrix of the truss should be estimated and inverted in the optimization procedure. The differences of the proposed techniques are rooted in the strategy utilized for inverting the stiffness matrix of the damaged structure. Additionally, for separating the probable damaged members, a new formulation is proposed. This scheme is employed prior to the outset of the optimization process. Furthermore, a new tactic is presented to select the appropriate load pattern. To investigate the robustness and efficiency of the authors' method, several numerical tests are performed. Moreover, Monte Carlo simulation is carried out to assess the effect of noisy measurements on the estimated parameters.

• Smart Structures and Systems
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• v.24 no.3
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• pp.303-317
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• 2019

Magneto-rheological fluid (MRF) rotatory dampers are normally used for controlling the constant rotation of machines and engines. In this research, such a device is proposed to act as variable stiffness device to alleviate the rotational oscillation existing in the many engineering applications, such as motor. Under such thought, the main purpose of this work is to characterize the nonlinear torque-angular displacement/angular velocity responses of an MRF based variable stiffness device in oscillatory motion. A rotational hysteresis model, consisting of a rotatory spring, a rotatory viscous damping element and an error function-based hysteresis element, is proposed, which is capable of describing the unique dynamical characteristics of this smart device. To estimate the optimal model parameters, a modified whale optimization algorithm (MWOA) is employed on the captured experimental data of torque, angular displacement and angular velocity under various excitation conditions. In MWOA, a nonlinear algorithm parameter updating mechanism is adopted to replace the traditional linear one, enhancing the global search ability initially and the local search ability at the later stage of the algorithm evolution. Additionally, the immune operation is introduced in the whale individual selection, improving the identification accuracy of solution. Finally, the dynamic testing results are used to validate the performance of the proposed model and the effectiveness of the proposed optimization algorithm.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.25 no.12
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• pp.830-842
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• 2015

This paper aims to develop a SI(structural identification) technique using the kinetic energy optimization technique(KEOT) and the direct matrix updating method(DMUM) to decide on optimal location of sensors and to update FE model respectively, which ultimately contributes to a composition of more effective SHM. Owing to the characteristic structural flexing behavior of cable bridges, which makes them vulnerable to any vibration, systematic and continuous structural health monitoring (SHM) is pivotal for them. Since it is necessary to select optimal measurement locations with the fewest possible measurements and also to accurately assess the structural state of a bridge for the development of an effective SHM, a SI technique is as much important to accurately determine the modal parameters of the current structure based on the data optimally obtained. In this study, the KEOT was utilized to determine the optimal measurement locations, while the DMUM was utilized for FE model updating. As a result of experiment, the required number of measurement locations derived from KEOT based on the target mode was reduced by approximately 80 % compared to the initial number of measurement locations. Moreover, compared to the eigenvalue of the modal experiment, an improved FE model with a margin of error of less than 1 % was derived from DMUM. Finally, the SI technique for long-span bridges proposed in this study, which utilizes both KEOT and DMUM, is proven effective in minimizing the number of sensors while accurately determining the structural dynamic characteristics.

• Journal of Mechanical Science and Technology
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• v.17 no.5
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• pp.654-667
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• 2003

This paper presents a consequence of the systematic approach to identify the aerodynamic parameters of an unmanned aerial vehicle (UAV) equipped with the automatic flight control system. A 3-2-1-1 excitation is applied for the longitudinal mode while a multi-step input is applied for lateral/directional excitation. Optimal time step for excitation is sought to provide the broad input bandwidth. A fully automated programmed flight test method provides high-quality flight data for system identification using the flight control computer with longitudinal and lateral/directional autopilots, which enable the separation of each motion during the flight test. The accuracy of the longitudinal system identification is improved by an additional use of the closed-loop flight test data. A constrained optimization scheme is applied to estimate the aerodynamic coefficients that best describe the time response of the vehicle. An appropriate weighting function is introduced to balance the flight modes. As a result, concurrent system models are obtained for a wide envelope of both longitudinal and lateral/directional flight maneuvers while maintaining the physical meanings of each parameter.