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Damage of both building and non-building structures (including the space structures) against earthquake is of great importance for civil engineers, because collapse of such large structures may have significant casualties and economic losses. Space structures are classified as large-scale structures and can cover a large space without columns. Seismic behavior of these structures is different from building structures. There are different types of space structures considering the geometrical aspects, which are effective in the damages causing economic and life losses. A space structure has three-dimensional behavior, and their higher-mode effects are remarkable. On the other hand, the weight of these structures is relatively low that may cause an unrealistic reduction in the calculation of seismic force in a static analysis. Therefore, the static analysis cannot capture their structural response effectively. Traditionally, the dynamic analysis is utilized for seismic design of space structures due to their complex structural behavior. Thus, in this paper, seismic design of two single-layer domes is performed using two dynamic analyses: time history analysis and response spectrum analysis. Although there are some studies on seismic design of domes, further investigations are required due to the structural diversity of different domes and the difference in their seismic behavior. Here, the ribbed and Schwedler domes under gravity and seismic loads are analyzed dynamically. The parameters of the design response spectral acceleration are based on ASCE7-16, and the site class (based on the soil type) is selected as “D”. Both horizontal and vertical components of seismic excitations are utilized in the dynamic analyses, because all these components are effective in design of a dome structure. The damping ratio is assumed to be 2% in the dynamic analyses based on the relevant literature.  In the response spectrum analysis, the vertical seismic load is expressed in terms of dead loads in the response spectrum analysis. In the time history analysis, seven ground motion records are selected based on the seismic zone. These ground motion records are scaled using both amplitude scaling and spectral matching approaches. The vertical components are scaled to the specific vertical design spectrum obtained from ASCE7-16. In this study, seismic design of the ribbed and Schwedler domes with a span of 36 meters and a height of 6 meters are carried out with some limitations on the member stress ratios and top nodal displacements. The structural designs based on the time history analysis and the response spectrum analysis are compared. The same cross-sectional areas are used in designs of the dome structures to compare the effects of these dynamic analysis methods better. In general, the top node displacement and stress ratios of the dome obtained using time history analysis is larger than that obtained using spectral dynamic analysis. Accordingly, the results indicate that the structure designed with the time history analysis is heavier than the structure designed with the response spectrum analysis. Obviously, although the time history analysis provides a better understanding of the dynamic behavior of the structure, it requires much higher computational cost than the response spectrum analysis.

 

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Technology has become a quintessential component of educational practice over the past years. Research in this area has shown that the integration of various technologies positively contributed to language education and facilitated learning different language skills. Despite the extensive application of computer assisted language learning for adults, little research has examined Young Language Learners’ (YLL) language development through technology. In this regard, the current study investigated the impact of using a mobile technology on YLLs’ (age range: 6 to 8) vocabulary development. Seventy-one learners participated in the study who were divided into a control (N = 32) and an experimental (N = 39) groups. Data were collected using a vocabulary test in three rounds of pre-test, post-test, and delayed post-test. The collected data in terms of vocabulary test scores were analyzed using mixed between-within subjects analysis of variance. The results revealed that the experimental group who used mobile devices for vocabulary learning outperformed the control group in the posttest and gained significant improvements in the delayed posttest. The study provides implications for various educational stakeholders including teachers, learners, and material developers to exploit the affordances of technology in effectively contributing to YLLs’ vocabulary development.



 

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Uncertainty inherently exists in quantity of a system’s parameters (e.g., loading or elastic modulus of a structure), and thus its effects have always been considered as an important issue for engineers. Meanwhile, numerical methods play significant role in stochastic computational mechanics, particularly for the problems without analytical solutions. In this article, spectral finite element method is utilized for stochastic spectral finite element analysis of 2D continua considering material uncertainties. Here, Lobatto family of higher order spectral elements is extended, and then influence of mesh configuration and order of interpolation functions are evaluated. Furthermore, Fredholm integral equation due to Karhunen Loève expansion is numerically solved through spectral finite element method such that different meshes and interpolation functions’ orders are also chosen for comparison and assessment of numerical solutions solved for this equation. This method needs fewer elements compared to the classic finite element method, and it is specifically useful in dynamic analysis as supplies desirable accuracy with having diagonal mass matrix. Also, these spectral elements accelerate the computation process along with Karhunen Loève and polynomial chaos expansions involving numerical solution of Fredholm integral equation. This research examines elastostatic and elastodynamic benchmark problems to demonstrate the effects of the undertaken parameters on accuracy of the stochastic analysis. Moreover, results demonstrate the effects of higher-order spectral elements on speed, accuracy and efficiency of static and dynamic analysis of continua.

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Organizational behavior is a complex of voluntary and intentional behaviors that are committed by the staffs and personnel of an organization even though they are not part of their official behaviors. The consequence of such behaviors is an enhancement of the functions and jobs of the organization. Organizational justice refers to ethical and fair behaviors of the staffs and personnel of the organization and is identified with 'equity', 'impartiality' and 'indiscrimination'. It is the organizational health that provides an opportunity for the staffs or their respective organization to perform far beyond the held expectations or their rivals. Considering these factors, the present study aimed at investigating the effect of the organizational health on organizational citizenship behaviors. It also aimed to explore the role of 'organizational healthy personality'   as a moderate factor.  

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Surface deformation of the earth due to earthquake fault dislocation is very important for predicting ground motions. There are many studies on kinematic modeling of earthquake faults in both analytical and numerical methods. However, suitable investigations on improving usefulness and efficiency of those numerical methods are still necessary for relevant researchers. In this paper, displacement fields for free surface of the earth due to fault dislocation in homogeneous elastic half-space have been investigated by finite element method. Boundary conditions have significant effects on the results of finite element method, especially when the domain of the problem has infinite boundaries (half-space). Therefore, appropriate cares should be taken to increase the efficiency and accuracy of this method. In order to achieve a comprehensive study on this topic, boundaries have been modeled by two approaches here. The first approach uses common elements for infinite boundaries, while the second one uses infinite elements for those boundaries. To verify the results, each problem has been examined by several meshes and numerical solutions have been compared to Okada’s analytical solutions. In addition to the effects of the boundary modeling, the discretization effects have been investigated in order to find a suitable approach to reduce computational efforts and to increase the accuracy and efficiency of finite element method. In the modeling process, contact elements have been employed to impose fault dislocation. Three numerical examples have been provided for these finite element analyses. Each example includes four analyses without infinite elements and three analyses with infinite elements which are also compared together. The results show that not only infinite elements are necessary for quasi-static fault dislocation problems, but also they improve the performance of finite element method, so that with finer meshes and smaller dimensions of a domain, analytical solutions can be captured by numerical solutions with suitable accuracy.

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There are many factors causing damages to a structure, including earthquakes, winds, environmental effects, etc. In order to repair a damaged structure, first its damage locations should be identified. Therefore, the damage identification of structures is considered as an important issue in civil engineering as well as mechanical engineering. Many methodologies have been devised for damage identification of structures, which are generally categorized to destructive and non-destructive cases. As a non-destructive damage identification approach, solving inverse problems for identifying the properties of a damaged structure is one of the popular methods which utilizes an optimization algorithm to minimize an error function in terms of measured strains or displacements. Since an iterative procedure with significant number of structural analyses should be carried out for the optimization process, an efficient numerical method should be employed to reduce the total computational cost. In this paper, the identification of hole in two-dimensional continuum structures is investigated with finite cell method and particle swarm optimization algorithm. The finite cell method is an efficient numerical method for solving the governing equations of continuum structures having geometrical complexity and/or discontinuities, which uses the concept of virtual domain method. The use of this concept makes the mesh generation easier such that the simple structured meshes can be utilized even for the curved boundaries of a structure, and hence mesh refinement is not necessary for the problems like damage detection. The finite cell method uses adaptive numerical integration for the cells including non-uniform material distribution. Accordingly, quadtree integration is utilized for the structural analysis using the finite cell method. Consequently, the computational time is significantly reduced. On the other hand, particle swarm optimization is a well-known meta-heuristic algorithm, and hence it does not require the gradient information of the problem. This population-based algorithm has been inspired by the social behaviour of animals such as fish schooling and birds flocking. The basis of this algorithm relies on the social influence and learning which enable individuals to preserve cognitive consistency. Thus, the exchange of ideas and interactions between individuals can lead them to solve optimization problems like damage detection. This study proposes the finite cell method and particle swarm optimization algorithm for damage detection of plate structures with single hole or multiple holes. As a non-gradient-based method, particle swarm optimization explores the search space to find the coordinates of the existing damage by minimizing an error function. This error function is evaluated by the strains or displacements calculated by the structural analysis utilizing the finite cell method. In order to evaluate the proposed methodology, numerical examples are provided to demonstrate the capability of finite cell method and particle swarm optimization algorithm in damage detection of two-dimensional structures. The first example considers the damage detection of a plate with a single hole, and it also considers the effects of mesh density. The second example employs a plate structure with three holes. The results demonstrate that the proposed methodology, with suitable computational efforts, can successfully be applied to damage detection of these structures.

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Nowadays, advances in numerical methods have led to model real-life physical problems effectively. One of the difficulties in modelling the real-life physical problems is the geometric creation, because the mesh definition for a complex geometry is hard. In order to overcome this issue, one can use the spectral cell method due to employing a Cartesian mesh even for a complex geometry, such that constant Jacobian is considered for cells in the mesh. Spectral cell method is a combination of the spectral element method and the fictious domain concept, which uses an adaptive integration employing the quadtree or octree partitioning for the cells intersecting arbitrary boundaries as well as the cells including nonuniform material distribution. The interpolation functions of Lobatto family of spectral elements are utilized in spectral cell method. The spectral cell method is an efficient numerical method to solve the governing equations of continuum structures with complicated geometries. On the other hand, uncertainty naturally exists in the parameters of an engineering system (e.g., elastic modulus) and the input of that system (e.g., loading). Thus, the effects of those uncertainties are important in the response calculation of the engineering system. There are two types of uncertainty: aleatoric and epistemic. Aleatoric uncertainty is defined as an intrinsic variability of certain quantities, while epistemic uncertainty is defined as a lack of knowledge about certain quantities. An alternative to a deterministic modelling is a stochastic modelling, but analysing such a stochastic model is harder than a deterministic model having deterministic material properties and configuration. This is because the behaviour of the stochastic model is inevitably stochastic. Traditionally, Monte-Carlo simulation analyses a stochastic model by generating numerous realizations of the stochastic problem, and then solves each one like a deterministic problem. Nevertheless, Monte-Carlo simulation needs very high computational cost, particularly for large-scale problems. A systematic technique for uncertainty quantification is the stochastic finite element method providing a variety of statistical information. However, the method is computationally expensive with respect to the finite element method, and thus there are many developments for stochastic methods. Consequently, this paper presents stochastic form of spectral cell method to solve elastostatic problems considering material uncertainties. Therefore, uncertainty quantification of an elastostatic problem with geometrically complex domain can be modelled more efficiently than the traditional stochastic finite element method. In the proposed method, Fredholm integral equation is discretised using spectral cell method to solve Karhunen-Loève expansion used for the random field decomposition. Also, this method uses fewer cells than the stochastic finite cell method, and does not require formation of the eigenfunctions. In addition, Karhunen-Loève and polynomial chaos expansions are used to decompose the random field and to consider the response variability, respectively. Simple mesh generation, desirable accuracy and computational cost are the main features of the present method. In this study, two benchmark numerical examples are provided to demonstrate the efficiency and capabilities of the proposed method in the solution of elastostatic problems. The results are compared to those of stochastic finite element method and stochastic spectral element method.