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RISS 인기검색어
- 검색키워드
- 저자 : A. Thirumalaiselvi (검색결과 5 건)
- 무료
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Electro-mechanical impedance based strength monitoring technique for hydrating blended cements
Thirumalaiselvi, A.,Saptarshi Sasmal 국제구조공학회 2020 Smart Structures and Systems, An International Jou Vol.25 No.6
Real-time monitoring of stiffness and strength in cement based system has received significant attention in past few decades owing to the development of advanced techniques. Also, use of environment friendly supplementary cementitious materials (SCM) in cement, though gaining huge interest, severely affect the strength gain especially in early ages. Continuous monitoring of strength- and stiffness- gain using an efficient technique will systematically facilitate to choose the suitable time of removal of formwork for structures made with SCM incorporated concrete. This paper presents a technique for monitoring the strength and stiffness evolution in hydrating fly ash blended cement systems using electro-mechanical impedance (EMI) based technique. It is important to observe that the slower pozzolanic reactivity of fly ash blended cement systems could be effectively tracked using the evolution of equivalent local stiffness of the hydrating medium. Strength prediction models are proposed for estimating the strength and stiffness of the fly ash cement system, where curing age (in terms of hours/days) and the percentage replacement of cement by fly ash are the parameters. Evaluation of strength as obtained from EMI characteristics is validated with the results from destructive compression test and also compared with the same obtained from commonly used ultrasonic wave velocity (UPV). Statistical error indices indicate that the EMI technique is capable of predicting the strength of fly ash blended cement system more accurate than that from UPV. Further, the correlations between stiffness- and strength- gain over the time of hydration are also established. From the study, it is found that EMI based method can be effectively used for monitoring of strength gain in the fly ash incorporated cement system during hardening.
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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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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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Applied linear and nonlinear statistical models for evaluating strength of Geopolymer concrete
Prabhat Ranjan Prem,A. Thirumalaiselvi,Mohit Verma 사단법인 한국계산역학회 2019 Computers and Concrete, An International Journal Vol.24 No.1
The complex phenomenon of the bond formation in geopolymer is not well understood and therefore, difficult to model. This paper present applied statistical models for evaluating the compressive strength of geopolymer. The applied statistical models studied are divided into three different categories - linear regression [least absolute shrinkage and selection operator (LASSO) and elastic net], tree regression [decision and bagging tree] and kernel methods (support vector regression (SVR), kernel ridge regression (KRR), Gaussian process regression (GPR), relevance vector machine (RVM)]. The performance of the methods is compared in terms of error indices, computational effort, convergence and residuals. Based on the present study, kernel based methods (GPR and KRR) are recommended for evaluating compressive strength of Geopolymer concrete.
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