Model uncertainty approximation using a copula-based approach for reliability based design optimization

Peng, Hao; Xi, Zhimin; Yang, Ren‐Jye

doi:10.1007/s00158-016-1530-2

Cited by 24 publications

(2 citation statements)

References 63 publications

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“…Accepted manuscript to appear in IJSSD 4 applied a GP modeling to correct a bias function and quantify model uncertainty based on one accurate model of a vehicle.In another attempt, uncertainty from an inadequate model of a real physical system has been addressed through integrating the model bias into reliability-based design optimization and the results were verified by two vehicle models (Pan et al 2016). Accordingly, this paper proposes a structural performance assessment approach, integrating a modular Bayesian approach (MBA) into structural reliability analysis.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Computation-Effective Structural Performance Assessment Using Gaussian Process-Based Finite Element Model Updating and Reliability Analysis

Moravej

Chan

Jesus

et al. 2020

Int. J. Str. Stab. Dyn.

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Structural health monitoring data has been widely acknowledged as a significant source for evaluating the performance and health conditions of structures. However, a holistic framework that efficiently incorporates monitored data into structural identification and, in turn, provides a realistic life-cycle performance assessment of structures is yet to be established. There are different sources of uncertainty, such as structural parameters, computer model bias and measurement errors. Neglecting to account for these factors results in unreliable structural identifications, consequent financial losses, and a threat to the safety of structures and human lives. This paper proposes a new framework for structural performance assessment that integrates a comprehensive probabilistic finite element model updating approach, which deals with various structural identification uncertainties and structural reliability analysis. In this framework, Gaussian process surrogate models are replaced with a finite element model and its associate discrepancy function to provide a computationally efficient and all-round uncertainty quantification. Herein, the structural parameters that are most sensitive to measured structural dynamic characteristics are investigated and used to update the numerical model. Sequentially, the updated model is applied to compute the structural capacity with respect to loading demand to evaluate its as-is performance. The proposed framework’s feasibility is investigated and validated on a large lab-scale box girder bridge in two different health states, undamaged and damaged, with the latter state representing changes in structural parameters resulted from overloading actions. The results from the box girder bridge indicate a reduced structural performance evidenced by a significant drop in the structural reliability index and an increased probability of failure in the damaged state. The results also demonstrate that the proposed methodology contributes to more reliable judgment about structural safety, which in turn enables more informed maintenance decisions to be made.

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Section: Accepted Manuscriptmentioning

confidence: 99%

Computation-Effective Structural Performance Assessment Using Gaussian Process-Based Finite Element Model Updating and Reliability Analysis

Moravej

Chan

Jesus

et al. 2020

Int. J. Str. Stab. Dyn.

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“…This thesis addresses the important research topic of developing model validation for the RBDO process, which can reduce error in designing a reliable product. There has been limited research on model validation for RBDO (Jiang et al, 2013;Pan et al, 2016).…”

Section: Model Verification and Validationmentioning

confidence: 99%

Confidence-based model validation for reliability assessment and its integration with reliability-based design optimization

Moon¹

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First I would like to thank my advisor Professor Kyung. K Choi for his continuous support and academic guidance throughout my Ph.D. studies. I appreciate all his effort and education in my Ph.D. program. The knowledge, skills, and technical writing that I have developed under his guidance will continue to benefit me throughout my future career. I also thank Professors Jia Lu, Hiroyuki Sugiyama, Shaoping Xiao, and Dr. David A. Lamb for their invaluable advices on this research as my thesis committee. I would also like to express my sincere thanks for all the help and encouragements from my senior colleagues, Dr. Hyunkyoo Cho and Dr. Nicholas Gaul. I also would like to acknowledge the technical and financial support of the

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Time-dependent concurrent reliability-based design optimization integrating experiment-based model validation

Wang

Cheng

Jing

2017

Struct Multidisc Optim

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Model uncertainty approximation using a copula-based approach for reliability based design optimization

Cited by 24 publications

References 63 publications

Computation-Effective Structural Performance Assessment Using Gaussian Process-Based Finite Element Model Updating and Reliability Analysis

Computation-Effective Structural Performance Assessment Using Gaussian Process-Based Finite Element Model Updating and Reliability Analysis

Confidence-based model validation for reliability assessment and its integration with reliability-based design optimization

Time-dependent concurrent reliability-based design optimization integrating experiment-based model validation

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