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1 January 2015 Main Factor Sensitivity Analysis Based on Response Surface Model Updating of Port Crane Structure
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Abstract

Zhang, W. and Liu, Y., 2015. Main factor sensitivity analysis based on response surface model updating of port crane structure.

When using the response surface model for model updating, the efficiency is higher than using the traditional sensitivity based on finite element (FE) model updating method, as the simple polynomial response surface (RS) model is adapted to optimize the iteration instead of the complicated finite element model. However, the selection of the parameters to be updated for model updating in the response surface model is usually based on experience, which will lead the updating results to be subjective. In this paper, a main factor sensitivity analysis based RS model updating method was put forward, and the main parameters in the RS model were determined according to the sensitivity analysis of the design parameters in the FE model of the structure onto the multi-frequency optimization object function. By combining the central composite design, quadratic polynomial and least square estimation technique, a set of response surface regression models of structure frequencies were built, and the fgoalattain algorithm was applied to solve and optimize the multi-frequency response surface model. The updating results of the port crane structure model with this method showed that the reasonable main factors could be attained, and that the frequency response surface models could accurately describe the calculated frequencies of the FE model. The updating results were highly accurate and efficient, which means that the method put forward in this paper was applicable to the updating of the FE models of large and complicated port crane structures.

© 2015 Coastal Education and Research Foundation
Weiguo Zhang and Yuan Liu "Main Factor Sensitivity Analysis Based on Response Surface Model Updating of Port Crane Structure," Journal of Coastal Research 73(sp1), (1 January 2015). https://doi.org/10.2112/SI73-029.1
Received: 23 August 2014; Accepted: 17 November 2014; Published: 1 January 2015
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