4.7 Article

Developing the structural analysis considering fuzzy performance levels

期刊

APPLIED SOFT COMPUTING
卷 115, 期 -, 页码 -

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ELSEVIER
DOI: 10.1016/j.asoc.2021.108180

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Structural performance assessment; Uncertainty; Fuzzy set theory; Fuzzy performance levels; Fuzzy number comparison

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This paper focuses on addressing uncertainties in building performance levels and loading conditions through evaluation process, utilizing fuzzy numbers and fuzzy set theory to model and estimate performance levels and structural responses, and proposing a novel fuzzy decision-making approach to ensure comprehensive assessment of building performance.
The concept of performance-based engineering in the seismic design and performance evaluation of structures has gained a significant development over the past few decades. The sample performance levels in current building codes are categorized into two levels that represent incipient damage and incipient collapse. Therefore, the performance limits of buildings involve numerous uncertainties such as the lack of clarity in their definition and ambiguity in the individual judgments. The main objective of this paper is to properly address the uncertainties in the performance levels and loading conditions through the evaluation process. The limit state for each performance level is defined as fuzzy numbers to model the fuzziness inherent in their interpretation. Moreover, the fuzzy set theory is adopted to estimate the desired fuzzy structural responses, considering the uncertainty involved in loading conditions within the analysis process. In addition, a novel fuzzy decision-making approach, which includes a comparison of the developed fuzzy sets (i.e., fuzzy structural response and fuzzy performance levels), is proposed to ensure a comprehensive assessment of the building performance. Numerical examples are also given to establish the application of the proposed method. The results demonstrate that the new comparing approach can make a reliable judgment, particularly in questionable cases of decision-making that are in line with the embedded context. (c) 2021 Elsevier B.V. All rights reserved.

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