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RISS 인기검색어
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- 저자 : N.Anandavalli (검색결과 4 건)
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Mechanics based analytical approaches to predict nonlinear behaviour of LSCC beams
A. Thirumalaiselvi,N. Anandavalli,J. Rajasankar 국제구조공학회 2017 Structural Engineering and Mechanics, An Int'l Jou Vol.64 No.3
This paper presents the details of analytical studies carried out towards the prediction of flexural capacity and loaddeflection behaviour of Laced Steel-Concrete Composite (LSCC) beams. Analytical expressions for flexural capacity of the beams are derived in accordance with the basic principles of conventional Reinforced Concrete (RC) beams, but incorporated with relevant modifications to account for the composite nature of the cross-section. The ultimate flexural capacity of the two LSCC beams predicted using the derived expressions is found to be approximately 20% lower than those obtained due to measurement from experiments. Further to these, two simple methods are also proposed on the basis of unit load method and equivalent steel beam method to determine the non-linear load-deflection response of the LSCC beams for monotonic loading. Upon validation of the proposed methods by comparing the predicted responses with those of experiments and finite element analysis, it is found that the methods are useful to find nonlinear response of such composite beams.
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Amar Prakash,N.Anandavalli,C.K.Madheswaran,N.Lakshmanan 국제구조공학회 2012 Steel and Composite Structures, An International J Vol.13 No.3
In this paper, experimental investigations on high strength steel (HSS) stud connected steelconcrete composite (SCC) girders to understand the effect of shear connector density on their flexural behaviour is presented. SCC girder specimens were designed for three different shear capacities (100%, 85%,and 70%), by varying the number of stud connectors in the shear span. Three SCC girder specimens were tested under monotonic/quasi-static loading, while three similar girder specimens were subjected to nonreversal cyclic loading under simply supported end conditions. Details of casting the specimens, experimental set-up, and method of testing, instrumentation for the measurement of deflection, interface-slip and strain are discussed. It is found that SCC girder specimen designed for full shear capacity exhibits interface slip for loads beyond 25% of the ultimate load capacity. Specimens with lesser degree of shear connection show lower values of load at initiation of slip. Very good ductility is exhibited by all the HSS stud connected SCC girder specimens. It is observed that the ultimate moment of resistance as well as ductility gets reduced for HSS stud connected SCC girder with reduction in stud shear connector density. Efficiency factor indicating the effectiveness of high strength stud connectors in resisting interface forces is estimated to be 0.8 from the analysis. Failure mode is primarily flexure with fracturing of stud connectors and characterised by flexural cracking and crushing of concrete at top in the pure bending region. Local buckling in the top flange of steel beam was also observed at the loads near to failure, which is influenced by spacing of studs and top flange thickness of rolled steel section. One of the recommendations is that the ultimate load capacity can be limited to 1.5 times the plastic moment capacity of the section such that the post peak load reduction is kept within limits. Load-deflection behaviour for monotonic tests compared well with the envelope of load-deflection curves for cyclic tests. It is concluded from the experimental investigations that use of HSS studs will reduce their numbers for given loading, which is advantageous in case of long spans. Buckling of top flange of rolled section is observed at failure stage. Provision of lips in the top flange is suggested to avoid this buckling. This is possible in case of longer spans, where normally built-up sections are used.
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A. Thirumalaiselvi,N. Anandavalli,J. Rajasankar,Nagesh R. Iyer 국제구조공학회 2016 Steel and Composite Structures, An International J Vol.20 No.1
This paper presents the details of Finite Element (FE) analysis carried out to determine the limiting deformation capacity and failure mode of Laced Steel-Concrete Composite (LSCC) beam, which was proposed and experimentally studied by the authors earlier (Anandavalli <i>et al</i>. 2012). The present study attains significance due to the fact that LSCC beam is found to possess very high deformation capacity at which range, the conventional laboratory experiments are not capable to perform. FE model combining solid, shell and link elements is adopted for modeling the beam geometry and compatible nonlinear material models are employed in the analysis. Besides these, an interface model is also included to appropriately account for the interaction between concrete and steel elements. As the study aims to quantify the limiting deformation capacity and failure mode of the beam, a suitable damage model is made use of in the analysis. The FE model and results of nonlinear static analysis are validated by comparing with the load-deformation response available from experiment. After validation, the analysis is continued to establish the limiting deformation capacity of the beam, which is assumed to synchronise with tensile strain in bottom cover plate reaching the corresponding ultimate value. The results so found indicate about 20° support rotation for LSCC beam with 45° lacing. Results of parametric study indicate that the limiting capacity of the LSCC beam is more influenced by the lacing angle and thickness of the cover plate.
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Response prediction of laced steel-concrete composite beams using machine learning algorithms
Thirumalaiselvi, A.,Mohit Verma,Anandavalli, N.,Rajasankar, J. 국제구조공학회 2018 Structural Engineering and Mechanics, An Int'l Jou Vol.66 No.3
This paper demonstrates the potential application of machine learning algorithms for approximate prediction of the load and deflection capacities of the novel type of Laced Steel Concrete-Composite1 (LSCC) beams proposed by Anandavalli et al. (Engineering Structures 2012). Initially, global and local responses measured on LSCC beam specimen in an experiment are used to validate nonlinear FE model of the LSCC beams. The data for the machine learning algorithms is then generated using validated FE model for a range of values of the identified sensitive parameters. The performance of four well-known machine learning algorithms, viz., Support Vector Regression (SVR), Minimax Probability Machine Regression (MPMR), Relevance Vector Machine (RVM) and Multigene Genetic Programing (MGGP) for the approximate estimation of the load and deflection capacities are compared in terms of well-defined error indices. Through relative comparison of the estimated values, it is demonstrated that the algorithms explored in the present study provide a good alternative to expensive experimental testing and sophisticated numerical simulation of the response of LSCC beams. The load carrying and displacement capacity of the LSCC was predicted well by MGGP and MPMR, respectively.
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