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Epistemic Uncertainty Quantification of Seismic Damage Assessment

In: Uncertainty Quantification and Model Calibration

Author

Listed:
  • Hesheng Tang
  • Dawei Li
  • Songtao Xue

Abstract

The damage-based structural seismic performance evaluations are widely used in seismic design and risk evaluation of civil facilities. Due to the large uncertainties rooted in this procedure, the application of damage quantification results is still a challenge for researchers and engineers. Uncertainties in damage assessment procedure are important consideration in performance evaluation and design of structures against earthquakes. Due to lack of knowledge or incomplete, inaccurate, unclear information in the modeling, simulation, and design, there are limitations in using only one framework (probability theory) to quantify uncertainty in a system because of the impreciseness of data or knowledge. In this work, a methodology based on the evidence theory is presented for quantifying the epistemic uncertainty of damage assessment procedure. The proposed methodology is applied to seismic damage assessment procedure while considering various sources of uncertainty emanating from experimental force-displacement data of reinforced concrete column. In order to alleviate the computational difficulties in the evidence theory-based uncertainty quantification analysis (UQ), a differential evolution-based computational strategy for efficient calculation of the propagated belief structure in a system with evidence theory is presented here. Finally, a seismic damage assessment example is investigated to demonstrate the effectiveness of the proposed method.

Suggested Citation

  • Hesheng Tang & Dawei Li & Songtao Xue, 2017. "Epistemic Uncertainty Quantification of Seismic Damage Assessment," Chapters, in: Jan Peter Hessling (ed.), Uncertainty Quantification and Model Calibration, IntechOpen.
  • Handle: RePEc:ito:pchaps:115022
    DOI: 10.5772/intechopen.68153
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    More about this item

    Keywords

    damage model; epistemic uncertainty; uncertainty quantification; evidence theory; differential evolution algorithm;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General

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