Conference Papers
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Item HDMR-Based Model Update in Structural Damage Identification(World Scientific Publishing Co. Pte Ltd wspc@wspc.com.sg, 2019) Naveen, B.O.; Balu, A.S.This paper presents a practical approach of model updating based on high-dimensional model representation (HDMR). The proposed methodology involves integrated finite element modeling, obtaining explicit relationships between the structural responses and parameters using HDMR and minimization of objective function developed using structural responses obtained from HDMR approximation functions using genetic algorithm. First, the efficiency of the proposed method is demonstrated by considering a simply supported beam example. Later model updating of an existing bridge is considered to check the adequacy of the proposed method. © 2019 World Scientific Publishing Company.Item 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.Item Failure Mode Recognition of Columns Using Artificial Neural Network(IOP Publishing Ltd custserv@iop.org, 2020) Edward, C.; Balu, A.S.Columns are one of the most vital segments in bridgessince its post-seismic behaviour is of much importance. The retrofitting methods and rehabilitation strategies of bridges mainly rely on the identification of the failure mode of columns. It has been witnessed in various studies on columns that the mode of failure highly depends on section and material properties and there is no specific boundary between the modes, which makes their identification more sophisticated. This paper uses an artificial neural network to predict the modes of failure by analysing the effects of such soft computing methods. In this study, machine- learning models were generated from the experimental data of 253 columns of rectangular cross-section and its accuracy of failure mode prediction was evaluated by considering failure modes mainly flexure, flexure-shear, and shear. The optimal input parameters have also been evaluated for the machine-learning algorithm that enhances the efficiency of failure mode prediction. © Published under licence by IOP Publishing Ltd.Item Optimisation of Trapezoidal Corrugated Plate Girder(IOP Publishing Ltd custserv@iop.org, 2020) Dhakate, S.; Balu, A.S.Trapezoidal Corrugated web girders is a newly developed structural design. The major advantage of such design of web is that the corrugated webs enhance the stability of beam against buckling, which results in a very economical design by the reduction in the use of web stiffeners. The flanges are made up of flat plates and welded to the trapezoidal web sheet with the modern manufacturing process and modern advance welding technology. The flanges are mainly used to provide flexural strength to the beam and web are used to increase the shear capacity of the beam. Main reason for the failure of the web is the steel yielding or web buckling. Other possible failure reasons are lateral torsional buckling of the girder and local flange buckling, separately or in combination. This paper presents a new technological solution of such a system, composed by web made of trapezoidal shape. The buckling strength of the girder is studied under different geometrical modifications performing a nonlinear finite element analysis in ANSYS. © Published under licence by IOP Publishing Ltd.Item 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.Item Universal Grey Number Systems for Uncertainty Quantification(Springer Science and Business Media Deutschland GmbH, 2023) Kumar, A.; Balu, A.S.In the recent past, modelling and analysis of structures with uncertain parameters have evoked significant interest.Physical imperfections, model flaws and system complexities can all be sources of uncertainty.In addition, the action loads (live, wind and earthquake) applied to a structure during its lifetime are not deterministic, hence for the proper performance assessment of the structural system, it is essential to properly account for these uncertainties.Uncertainties are usually described by probabilistic and non-probabilistic approaches.The growing interest in the non-probabilistic approach developed due to the incredibility of the probabilistic approach when data is insufficient.For estimating the ranges of the structural system’s response, the interval finite element approach looks to be acceptable, whose input parameters are defined in the ranges.However, the range of values predicted by the interval analysis suffers dependency problem.This can cause the computed findings to be overestimated.Although, the use of numerical truncation technique, parameterization of intervals and subinterval technique suggested by several researchers to avoid the dependency problem caused by general interval arithmetic.The physical rules (distributive law) are not violated by a universal grey numbers are a form of grey number and predict accurate results when compared with the interval approach.The universal grey number system is one such approach where computational efficiency and accuracy can be achieved when the input parameters are available in the ranges/interval. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Uncertainty Quantification of Structures Using Belief Theory(Springer Science and Business Media Deutschland GmbH, 2023) Metagudda, S.H.; Balu, A.S.Uncertainties due to loading, material properties, lack of knowledge, and manufacturing defaults are typical among practical engineering problems.They affect the performance and safety aspects of the structures.The fundamental difficulty in reliability evaluation is quantifying the uncertainty of structural systems.There are two forms of uncertainties such as aleatory uncertainty and epistemic uncertainty.Both aleatory uncertainty and epistemic uncertainty affect structures in the real world: aleatory uncertainty (induced by innate randomness) and epistemic uncertainty (induced by deficiency of information).Aleatory uncertainties can be handled effectively using probability measures, but epistemic uncertainties are not justified precisely using probability-based theories.Many non-probabilistic reliability estimates have been proposed to account for the influence of epistemic uncertainty, including evidence theory, interval analysis, fuzzy analysis, and the posbist reliability approach.A new reliability metric, belief reliability, is created to solve the disadvantages of non-probability approaches.Belief reliability is defined as the degree of confidence in the belief dependability of a system that meets four key axioms: normalcy, duality, subadditivity, and the product axiom.The belief’s reliability combines the contribution of design margin, aleatory uncertainty, and epistemic uncertainty.Belief reliability is an effective tool to evaluate uncertainties caused due to inherent randomness of the system (irreducible uncertainties) and uncertainties caused due to lack of information (reducible uncertainties).This paper reviews the belief reliability approach adopted for uncertainty quantification in civil engineering structures. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.