A new multi-objective approach to finite element model updating
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- A new multi-objective approach to finite element model updating
- Jin, Seung-Seop; Cho, Soojin; Jung, Hyung-Jo; Lee, Jong-Jae; Yun, Chung Bang
- Ambient vibration test; Finite-element model updating; Multiobjective function; Non-dominated sorting genetic algorithm-II; Objective function values; Objective functions; Pareto-optimal front; Trade-off relationship
- Issue Date
- ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
- JOURNAL OF SOUND AND VIBRATION, v.333, no.11, pp.2323 - 2338
- The single objective function (SOF) has been employed for the optimization process in the conventional finite element (FE) model updating. The SOF balances the residual of multiple properties (e.g., modal properties) using weighting factors, but the weighting factors are hard to determine before the run of model updating. Therefore, the trial-and-error strategy is taken to find the most preferred model among alternative updated models resulted from varying weighting factors. In this study, a new approach to the FE model updating using the multi-objective function (MOF) is proposed to get the most preferred model in a single run of updating without trial-and-error. For the optimization using the MOF, non-dominated sorting genetic algorithm-II (NSGA-II) is employed to find the Pareto optimal front. The bend angle related to the trade-off relationship of objective functions is used to select the most preferred model among the solutions on the Pareto optimal front. To validate the proposed approach, a highway bridge is selected as a test-bed and the modal properties of the bridge are obtained from the ambient vibration test. The initial FE model of the bridge is built using SAP2000. The model is updated using the identified modal properties by the SOF approach with varying the weighting factors and the proposed MOF approach. The most preferred model is selected using the bend angle of the Pareto optimal front, and compared with the results from the SOF approach using varying the weighting factors. The comparison shows that the proposed MOF approach is superior to the SOF approach using varying the weighting factors in getting smaller objective function values, estimating better updated parameters, and taking less computational time.
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