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Modeling the Behavior of Axially and Laterally Loaded Pile with a Contact Model
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Modeling the Behavior of Axially and Laterally Loaded Pile with a Contact Model Zakia Khelifi Department of Civil Engineering, University AbouBakr Belkaid, Algeria e-mail: zakia_khelifi@yahoo.fr Abdelmadjid Berga Department of Civil Engineering, University of Bechar, Algeria e-mail: bergaabdelmadjid@yahoo.fr Nazihe Terfaya Department of Mechanical Engineering, University of Bechar, Algeria e-mail: t_nazihe@yahoo.fr Abstract This work treats the behavior of a single pile subjected to two types of loading: axial and lateral, with taking into account the interaction between the soil and the pile. The zone of soil-pile interface is modeled by a simple model of interaction; based on the Coulomb friction contact law. The numerical analysis is carried out in 2D using a finite element program. Comparisons are presented between modeled pile behavior and that predicted from experimental, analytical and analysis results, a reasonable good agreement is obtained between them. منبع 10.1.1.477.248.pdf-
- soil-structure interaction
- piles
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(و 3 مورد دیگر)
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Numerical evaluation of the hysteretic behavior of concentrically braced frames and buckling restrained brace frame systems ارزیابی عددی رفتار هیسترتیک سیستم های قاب های مهاربندی همگرا و قاب مهاربندی کمانش تاب Mohammad Naghavia,⁎, Rohola Rahnavardb,⁎, Robert J. Thomasb,⁎, Mohsen Malekinejadc a Department of Civil Engineering, Javid Institute for Higher Education of Jiroft, Iran b Department of Civil and Environmental Engineering, Clarkson University, Potsdam, NY, USA c Department of Civil Engineering, Islamic Azad University, Sirjan branch, Iran This paper presents a numerical investigation of buckling restrained brace (BRB) frames (BRBF) and a comparison with concentrically braced frames (CBF). A simplified core-spring finite element model was used to model BRB elements. Four types of CBF (X, V, inverted V, and two-story X) and four types of BRBF (diagonal, V, inverted V, and two-story X) were modeled in ABAQUS. A static nonlinear pushover analysis considering two cyclic loading protocols showed that the buckling restraint allows the BRB elements to undergo significant plasticity without forming plastic hinges, thereby dissipating a proportionally larger amount of energy and delaying yielding in the frame elements. Results indicated dramatic improvements in energy dissipation and ductility in BRBF models as compared to equivalent CBF models. Seismic response modification factors for BRBF models also showed greater values over CBF models. Time-history analysis evaluated the performance of CBF and BRBF under historical earthquake loadings and supported the same conclusions. The best performance was observed for V, inverted V, and two-story X BRBF configurations; the diagonal BRBF configuration performed better than equivalent CBF models, but did not perform nearly as well as the other BRBF models. منبع article_Numerical_evaluation_of.pdf
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- finite elements
- buckling restrained brace
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(و 2 مورد دیگر)
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Comparison Study on Neural Networks in Damage Detection of Steel Truss Bridge
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Comparison Study on Neural Networks in Damage Detection of Steel Truss Bridge Hassan Aghabarati 1 ; Mohsen Tabrizizadeh2 This paper presents the application of three main Artificial Neural Networks (ANNs) in damage detection of steel bridges. This method has the ability to indicate damage in structural elements due to a localized change of stiffness called damage zone. The changes in structural response is used to identify the states of structural damage. To circumvent the difficulty arising from the non-linear nature of the inverse problem, three neural networks, Multi-Layer Perceptron Neural Network (MLPNN), Radial Basis Function Neural Network (RBFNN) and General Regression Neural Network (GRNN) are employed to simulate damage states of steel bridges. It was observed that the performance of all three networks is well and they have good agreement with actual results performed with Finite Element analysis. The efficiency of GRNN in structural identification is so good, although RBFNN has results close to GRNN and MLPNN results are satisfactory. All networks have good results while there is a little damage in structural members. Generally, results would have more error when damages in structural members extend. The engineering importance of the whole exercise can be appreciated once we realize that the measured input at only a few locations in the structure is needed in the identification process using neural networks. منبع دانلود JSEG61313868600.pdf-
- Steel Bridges
- Finite Elements
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(و 3 مورد دیگر)
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The Effect of a Sluice Gate Edge on Water Surface Profile
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The Effect of a Sluice Gate Edge on Water Surface Profile Original Article, B42 Mohammed A.Y, and Khaleel M.S. Journal. Civil Eng. Urban. 2(6): 239-243. 2012. ABSTRACT:In this paper the effect of the lower gate edge on the water surface profile (W.S.P) have been studied in a rectangular flume with four gate openings (2, 3, 4 and 4.5) cm, five upstream of water heads (18.9,21,24,26 and 28.9) cm and five gate cases (vertical and inclined vertically by angle (45)o with and opposite to flow direction )with horizontal and sharp lower edge ( inclined by angle (45)o ) Were adopted in the experiments. The results show that the convergence length for inclined gate with flow direction and horizontal edge increases by (28.7)"%" with respect to vertical, while decreases by (21.4)"%" when gate inclined opposite to flow direction and horizontal edge. The convergence angle decreases by (38.7)"%" when gate inclined with flow direction and horizontal edge, while increase by (22.4)"%" when gate inclined opposite flow direction and horizontal edge. The horizontal lip for the inclined gate with flow direction reduced the (W.S.P) convergence angle and then reduced the scour downstream sluice gate. Keywords:sluice gate, water surface profile, tail water, finite elements منبع: [Hidden Content] دانلود: [Hidden Content],%20B%2042%20239-243.pdf JCEU, B 42 239-243.pdf-
- sluice gate
- water surface profile
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(و 2 مورد دیگر)
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