• Proceedings of the Computational Structural Engineering Institute Conference
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• 2006.04a
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• pp.531-534
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• 2006

A multi-scale (macro-micro) finite element framework for analysis of polycrystalline solids is suggested. The proposed frame work is strongly-coupled in a sense that the two scale calculation is performed at the same time. The issue of averaging micro-scale material stress and stiffness is addressed and a strategy is proposed. The proposed framework is implemented and applied to two examples having different geometries and loading modes. It is concluded that the proposed multi-scale framework can be used for more detailed and accurate analysis compared with the single-scale finite element analysis.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.29 no.5
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• pp.421-428
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• 2016

The present work extends the recent variational formulation to more general time-dependent problems. Thus, based upon recent works of variational formulation in dynamics and pure heat diffusion in the context of the extended framework of Hamilton's principle, formulation for fully coupled thermoelasticity is developed first, then, with thermoelasticity-poroelasticity analogy, poroelasticity formulation is provided. For each case, energy conservation and energy dissipation properties are discussed in Fourier transform domain.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2007.04a
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• pp.707-712
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• 2007

Recently, the rapid progress of Internet and Network affects engineering design environment as well as business fields to utilize Web technologies to enhance it's competitiveness in the world. This situation has also caused many companies to seek out the possibility to increase the collaboration between different organizations within their Product design process. Thus. this research shows the web based distributed design system framework which involves Workflow-based framework that enables to automate, integrate and manage the design process, such as CAD, CAE, Optimization tools.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2003.10a
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• pp.169-176
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• 2003

In the present study, a shape design optimization scheme in shell structures is implemented based on the integrated framework of geometric modeling and analysis. The common representation of B-spline surface patch is used for geometric modeling. A geometrically-exact shell finite element is implemented. Control points or the surface are employed as design variables. In the computation of shape sensitivity, semi-analytical method is employed. Sequential linear programming is applied to the shape optimization of surfaces. The developed integrated framework should serve as a powerful tool to design and analysis of surfaces.

• Structural Engineering and Mechanics
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• v.88 no.6
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• pp.535-549
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• 2023

Deep extreme learning machine (DELM) and multi-verse optimization algorithms (MVO) are hybridized for designing an optimal and adaptive control framework for uncertain buildings. In this approach, first, a robust model predictive control (RMPC) scheme is developed to handle the problem uncertainty. The optimality and adaptivity of the proposed controller are provided by the optimal determination of the tunning weights of the linear programming (LP) cost function for clustered external loads using the MVO. The final control policy is achieved by collecting the clustered data and training them by DELM. The efficiency of the introduced control scheme is demonstrated by the numerical simulation of a ten-story benchmark building subjected to earthquake excitations. The results represent the capability of the proposed framework compared to robust MPC (RMPC), conventional MPC (CMPC), and conventional DELM algorithms in structural motion control.

• Structural Engineering and Mechanics
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• v.23 no.5
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• pp.455-468
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• 2006

Smart structural systems are defined as ones that demonstrate the ability to modify their characteristics and/or properties in order to respond favorably to unexpected severe loading conditions. The performance of such a task requires a set of additional components to be integrated within such systems. These components belong to three major categories, sensors, processors and actuators. It is wellknown that all structural systems entail some level of uncertainty, because of their extremely complex nature, lack of complete information, simplifications and modeling. Similarly, sensors, processors and actuators are expected to reflect a similar uncertain behavior. As it is imperative to be able to evaluate the impact of such components on the behavior of the system, it is as important to ensure, or at least evaluate, the reliability of such components. In this paper, a system model for reliability assessment of smart structural systems is outlined. The presented model is considered a necessary first step in the development of a reliability assessment algorithm for smart structural systems. The system model outlines the basic components of the system, in addition to, performance functions and inter-relations among individual components. A fault tree model is developed in order to aggregate the individual underlying component reliabilities into an overall system reliability measure. Identification of appropriate limit states for all underlying components are beyond the scope of this paper. However, it is the objective of this paper to set up the necessary framework for identifying such limit states. A sample model for a three-story single bay smart rigid frame, is developed in order to demonstrate the proposed framework.

• Smart Structures and Systems
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• v.32 no.1
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• pp.61-81
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• 2023

This study presents an ensemble learning based Bayesian model updating approach for structural damage diagnosis. In the developed framework, the structure is initially decomposed into a set of substructures. The autoregressive moving average (ARMAX) model is established first for structural damage localization based structural motion equation. The wavelet packet decomposition is utilized to extract the damage-sensitive node energy in different frequency bands for constructing structural surrogate models. Four methods, including Kriging predictor (KRG), radial basis function neural network (RBFNN), support vector regression (SVR), and multivariate adaptive regression splines (MARS), are selected as candidate structural surrogate models. These models are then resampled by bootstrapping and combined to obtain an ensemble model by probabilistic ensemble. Meanwhile, the maximum entropy principal is adopted to search for new design points for sample space updating, yielding a more robust ensemble model. Through the iterations, a framework of surrogate ensemble learning based model updating with high model construction efficiency and accuracy is proposed. The specificities of the method are discussed and investigated in a case study.

• The Journal of Information Systems
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• v.25 no.4
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• pp.63-85
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• 2016

Purpose While the popularity of C2C auction platforms such as eBay is gradually decreased, this domain is still undermined to explain online bidding behaviors. Online bidders sometimes engage in impulsive bidding due to some of the online auction characteristics. Therefore, this study develops and tests a model of the impulsive bidding exhibited by online bidders in C2C auction platforms. Based on S-O-R framework, our model posits that both perceived time-pressure and competition intensity affect cognitive absorption which ultimately influences the impulsive bidding. Design/methodology/approach This study collected survey data from 214 C2C auction participants, who have prior experience on impulsive bidding and tested both measurement model and structural model by using CB-SEM (covariate-based structural equation modelling) technique. In this study, by using AMOS 20.0, we tested the measurement model for its overall fit, item reliability, and validity and further conducted the structural model to test our proposed hypotheses. Findings Based on our results, we found that perceived tim-pressure and competition intensity were positively related to cognitive absorption. We also found that the cognitive absorption was positively associated with impulsive bidding behavior. In this study, by developing our research model in S-O-R framework, we provide an alternative theoretical mechanism to describe online impulsive bidding behavior.

• Structural Engineering and Mechanics
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• v.35 no.6
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• pp.659-676
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• 2010

An energy-based fatigue life prediction framework was previously developed by the authors for prediction of axial and bending fatigue life at various stress ratios. The framework for the prediction of fatigue life via energy analysis was based on a new constitutive law, which states the following: the amount of energy required to fracture a material is constant. In this study, the energy expressions that construct the new constitutive law are integrated into minimum potential energy formulation to develop new finite elements for uniaxial and bending fatigue life prediction. The comparison of finite element method (FEM) results to existing experimental fatigue data, verifies the new finite elements for fatigue life prediction. The final output of this finite element analysis is in the form of number of cycles to failure for each element in ascending or descending order. Therefore, the new finite element framework can provide the number of cycles to failure for each element in structural components. The performance of the fatigue finite elements is demonstrated by the fatigue life predictions from Al6061-T6 aluminum and Ti-6Al-4V. Results are compared with experimental results and analytical predictions.

• Advances in Computational Design
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• v.6 no.1
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• pp.15-30
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• 2021

With improvement in innovative manufacturing technologies, it became possible to fabricate any complex shaped structural design for practical applications. This allows for the fabrication of curvilinearly stiffened pressure vessels and pipes. Compared to straight stiffeners, curvilinear stiffeners have shown to have better structural performance and weight savings under certain loading conditions. In this paper, an optimization framework for designing curvilinearly stiffened composite pressure vessels and pipes is presented. NURBS are utilized to define curvilinear stiffeners over the surface of the pipe. An integrated tool using Python, Rhinoceros 3D, MSC.PATRAN and MSC.NASTRAN is implemented for performing the optimization. Rhinoceros 3D is used for creating the geometry, which later is exported to MSC.PATRAN for finite element model generation. Finally, MSC.NASTRAN is used for structural analysis. A Bi-Level Programming (BLP) optimization technique, consisting of Particle Swarm Optimization (PSO) and Gradient-Based Optimization (GBO), is used to find optimal locations of stiffeners, geometric dimensions for stiffener cross-sections and layer thickness for the composite skin. A cylindrical pipe stiffened by orthogonal and curvilinear stiffeners under torsional and bending load cases is studied. It is seen that curvilinear stiffeners can lead to a potential 10.8% weight saving in the structure as compared to the case of using straight stiffeners.