Determination of appropriate updating parameters for effective life-cycle management of deteriorating structures under uncertainty

Abstract

Effective life-cycle management of deteriorating structures should be based on an accurate and reliable damage propagation prediction model under uncertainty. Appropriate updating with inspected information can decrease errors between the detected and predicted damage and finally improve the accuracy and reliability of life-cycle management of a deteriorating structure. This paper presents an approach to determine the most appropriate probabilistic parameters to update the damage propagation prediction model. The presented approach includes the comparison-based method and parametric global sensitivity analysis (GSA). In the comparison-based method, the most appropriate updating parameters are determined based on the mean absolute error (MAE), Kullback–Leibler (KL) divergency, and Bhattacharyya distance by considering all the combinations of probabilistic parameters related to damage propagation prediction model. Furthermore, the GSA can be also applied to select the most appropriate parameters where sensitivity indices of the combination of the probabilistic parameters are compared. Finally, a comparison of assessment values for the most appropriate parameters selected from comparison-based method and GSA is presented. The approach presented in this paper can be applied for any damage propagation model. Superstructures of two existing bridges under corrosion and fatigue are applied as illustrative examples.

Keywords:

• Bhattacharyya distance
• corrosion
• damage propagation
• fatigue
• global sensitivity analysis
• Kullback–Leibler divergency
• mean absolute error
• parameter determination
• updating

Acknowledgements

The support by a grant from Wonkwang University in 2020 is gratefully acknowledged. The opinions presented in this paper are those of the authors and do not necessarily reflect the views of the sponsoring organizations.