Structural damage detection in framed structures using under foundation settlement/rotation of bases Sdhm Structural Durability and Health Monitoring, 2017
Assessment of residual strength and residual life of concrete beams International Journal of Applied Engineering Research, 2016
Propagation of uncertainties in randomly parametered reduced and coupled finite element dynamic models M. N. Hedge New Horizons and Better Practices, 2007 The present paper deals with the reduction of the large stochastic finite element model with the reduction in its mechanical degrees of freedom. The procedure is based on the available reduction/condensation procedures. The response sensitivity is computed based on first and/second order sensitivities of eigensolution. The joint probability distribution of the response process is estimated using maximum entropy method. The response from the reduced FE model is compared with the response from full FE model. The analysis solution is validated with Monte Carlo Simulations.
Probabilistic sensitivity analysis methods for design under uncertainty: Probabilistic model reduction M. N. Hegde New Horizons and Better Practices, 2007 Stochastic finite element methods applied to large structures usually require an explicit discretization of random, fields into random variables. The computational effort required in these studies, increase with the increase in number of random variables that enter into the analysis. Based on the global importance measure, it will be possible to identify the probabilistically non-significant variables and reduce the dimension of a random design space. Kullback-Leibler (K-L) entropy measures the divergence from one probability distribution to another, and can be applied both globally and regionally. Global sensitivity analysis (GSA) with respect to basic random variables based on K-L entropy measure is applied to reduce the random variables. This procedure is done before the probability of failure is computed using global response surface. This procedure is very important in the structural reliability analysis involving performance functions with multiple design points and/or regions that influence the failure probability. This eventually reduces the computational effort associated with uncertainty assessment without sacrificing the optimum solution. The present paper deals with such probabilistic model reduction of the stochastic structural dynamic problems in the reliability analysis within the framework of response surface method.
