Faculty Publications
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Item Efficient assessment of structural reliability in presence of random and fuzzy uncertainties(American Society of Mechanical Engineers (ASME), 2014) Balu, A.S.; Rao, B.N.This paper presents an efficient uncertainty analysis for estimating the possibility distribution of structural reliability in presence of mixed uncertain variables. The proposed method involves high dimensional model representation for the limit state function approximation, transformation technique to obtain the contribution of the fuzzy variables to the convolution integral and fast Fourier transform for solving the convolution integral. In this methodology, efforts are required in evaluating conditional responses at a selected input determined by sample points, as compared to full scale simulation methods, thus the computational efficiency is accomplished. The proposed method is applicable for structural reliability estimation involving any number of fuzzy and random variables with any kind of distribution. Copyright © 2014 by ASME.Item Assessment of Cohesive Parameters Using High Dimensional Model Representation for Mixed Mode Cohesive Zone Model(Elsevier Ltd, 2019) Rao, B.; Balu, A.S.Simulation of the mechanical behavior of bonded joints using a cohesive zone model (CZM) is the most common technique to characterize the delamination process. It is usually dependent on the calculation of cohesive parameters of the traction-separation law, and the parameters are iteratively obtained with the help of simulation and experimental results. The non-availability of standard methods to obtain the parameters necessitates the iterative adjustments of simulation results to the experimental results. However, the calculations based on all individuals for the simulation are not effective as it demands high computational effort. To overcome this issue, this paper proposes a computationally efficient method using high dimensional model representation (HDMR). The cohesive parameters are determined by adopting an efficient sampling scheme within the limits of the parameters. Single leg bending (SLB) joint is tested under the influence of dominant conditions such as mode-I and mode-II to determine the equivalent parameters. The errors resulted from the comparison between the simulation, and experimental values are minimized in order to determine the optimal values. The mixed mode (MM) CZM is then established by pure mode cohesive parameters, and the same is implemented to the SLB joint under various mode mixities for analyzing the fracture process. Comparison between the numerical analysis and the experimental study proves that the proposed HDMR based approach estimates the failure mechanism exactly. © 2019 Institution of Structural EngineersItem Modeling of delamination in fiber-reinforced composite using high-dimensional model representation-based cohesive zone model(Springer Verlag service@springer.de, 2019) Rao, B.; Balu, A.S.Prediction of delamination failure is challenging when the researchers try to achieve the task without overburdening the available computational resources. One of the most powerful computational models to predict the crack initiation and propagation is cohesive zone model (CZM), which has become prominent in the crack propagation studies. This paper proposes a novel CZM using high-dimensional model representation (HDMR) to capture the steady-state energy release rate (ERR) of a double-cantilever beam (DCB) under mode I loading. The finite element models are created using HDMR-based load and crack length response functions. Initially, the model is developed for 51-mm crack size DCB specimens, and the developed HDMR-based CZM is then used to predict the ERR variations of 76.2-mm crack size DCB model. Comparisons have been made between the available unidirectional composite (IM7/977-3) experimental data and the numerical results obtained from the 51-mm and 76.2-mm initial crack size DCB specimens. In order to demonstrate the efficiency of the proposed model, the results of the second-order nonlinear regression model using RSM are used for the comparison study. The results show that the proposed method is computationally efficient in capturing the delamination strength. © 2019, The Brazilian Society of Mechanical Sciences and Engineering.Item Structural damage identification of bridge using high dimensional model representation(Bellwether Publishing, Ltd., 2021) Naveen, B.O.; Balu, A.S.Any engineering structure under the action of various internal and external factors like changes in the material properties, inadequate design, faulty construction, deterioration due to malfunctioning are susceptible to damages. In the past, many methods have attempted to identify damage by solving an inverse problem, which inevitably needs an analytical model. However, often the construction of these analytical model requires considerable effort in building a mathematical framework with acceptable level of accuracy and reliability which makes these approaches less attractive. To circumvent this complexity, this work presents a computationally efficient approach in structural damage identification using high dimensional model representation. © 2020 Taylor & Francis Group, LLC.Item Analysis of structural systems with imprecise uncertainties using high dimensional model representation(World Scientific, 2021) Spoorthi, S.K.; Balu, A.S.Uncertainties present in any structural system inherently affect the performance and design of the system. The sources of uncertainties serve the basis for delineating the types as aleatory or epistemic. The probabilistic models can be considered as the most valuable strategies to deal with aleatory uncertainties, while convex models, possibility theory, evidence theory and Bayesian probability theory can be used to deal with epistemic uncertainty. However, when only scarce datasets are available and knowledge is incomplete, a more general framework, such as probability-box, is more appropriate to describe the uncertainty. Furthermore, analysis of complex and multi-dimensional structures is expensive and time consuming when numerical techniques are used. Therefore, simulation of such structures for many realisation of uncertain input becomes a challenging task in the uncertainty analysis. In this paper, complex structural systems with imprecise uncertain input are studied and evaluated efficiently by High Dimensional Model Representation based uncertainty analysis. © 2021 World Scientific Publishing Company.