Browsing by Author "Nagesh, M."

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    ANN Based Design Parameter Estimation for Structural Systems
    (IOP Publishing Ltd custserv@iop.org, 2020) Nagesh, M.; Balu, A.S.
    Estimation of the probability of failure of multi-dimensional structural systems is expensive from the computation perspective. To decrease the burden of computation, one can use simple approximation methods like Surrogate models, Kriging model, Support vector machine, Artificial neural network, and more based on the suitability for the problems. In Surrogate or Response surface modeling, the limit state function of any system is suitably approximated by making use of known mathematical models like polynomials, exponentials, etc. During the construction of surrogates, variables in the model should be well known prior to the approximation. In practical consideration, the design parameters are the unknowns that need to be evaluated before reliability-based design. Inverse Response surface procedure is proposed in the paper to address the above-mentioned issue. The procedure developed is the combination of adaptive Response surface method with appropriate experimental design i.e. Halton low discrepancy sequence sampling technique for evaluating the probability of failure or reliability index and an Artificial neural network is utilised as an inverse reliability procedure for design optimisation. The method gives an accurate result and the efficiency is increased for the same number of iterations in comparison to the work of David Lehky and Martina Somodikova [1] with Latin hypercube sampling as experimental design. © Published under licence by IOP Publishing Ltd.
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    Inverse response surface method for structural reliability analysis
    (Springer Science and Business Media Deutschland GmbH, 2020) Nagesh, M.; Balu, A.S.
    Reliability-based design of complex structural systems is a computationally tedious task. In order to reduce the computational effort, approximation methods, such as classical response surface method, Kriging model and artificial neural network, can be adopted. Response surface model is a conventional method, where the limit state function is approximated using a suitable surrogate model. For the construction of response surface, variables of stochastic model should be known well in advance. However, the design parameters are unknown during initial stages of reliability-based design optimization (RBDO). For such structural design cases using RBDO, an adaptive inverse response surface procedure is proposed in this paper. The procedure is developed by coupling the adaptive response surface method with suitable experimental design (Halton low-discrepancy sequence sampling) for estimating reliability indicators and artificial neural network-based inverse reliability method for design optimization. The validity and accuracy of the proposed method are tested on example with explicit nonlinear limit state function. © Springer Nature Singapore Pte Ltd 2020.