Finite Element Model Updating Using Response Surface Method

proximation model, which traditionally is a polynomial and therefore is less expensive to evaluate.This makes RSM very useful to FE model updating because optimizing the FE model to match measured data to FE model generated data is a computationally expensive exercise.Furthermore, the calculation of the gradients that are essential when traditional optimization methods, such as conjugate gradient methods, are used is computationally expensive and often encounters numerical problems such as ill-conditioning.RSM tends to be immune to such problems when used for FE model updating.This is largely because RSM solves a crude approximation of the FE model rather than the full FE model which is of high dimensional order.The multi-layer perceptron (MLP) 6 is used to approximate the response equation.The RSM is particularly useful for optimizing systems that are evolving as a function of time, a situation that is prevalent in model-based fault diagnostics found in the manufacturing sector.To date, RSM has been used extensively to optimize complex models and processes 7,8 .In summary, the RSM is used because of the following reasons: (1) the ease of implementation that includes low computational time; (2) the suitability of the approach to the manufacturing sector where model-based methods are often used to monitor structures that evolve as a function of time.FE model updating has been used widely to detect damage in structures 9 .When implementing FE updating methods for damage identification, it is assumed that the FE model is a true dynamic representation of the structure and this is achieved through FE model updating.This means that changing any physical parameter of an element in the FE model is equivalent to introducing damage in that region.There are two approaches that are used in FE updating: direct methods and iterative