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Multi‐criteria decision making for seismic intensity measure selection considering uncertainty
Author(s) -
Qian Jing,
Dong You
Publication year - 2020
Publication title -
earthquake engineering and structural dynamics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.218
H-Index - 127
eISSN - 1096-9845
pISSN - 0098-8847
DOI - 10.1002/eqe.3280
Subject(s) - multiple criteria decision analysis , topsis , weighting , probabilistic logic , selection (genetic algorithm) , ideal solution , measure (data warehouse) , mathematical optimization , computer science , pairwise comparison , analytic hierarchy process , data mining , mathematics , operations research , machine learning , artificial intelligence , medicine , physics , radiology , thermodynamics
Summary Seismic intensity measure (IM) selection is associated with consideration of multiple criteria, and there are uncertainties within the selection process. In this paper, a novel multi‐criteria decision making (MCDM) approach by incorporating stochastic multi‐criteria acceptability analysis (SMAA) with technique for order preference by similarity to ideal solution (TOPSIS) is proposed to solve the stochastic decision making problem of IM selection. TOPSIS provides an alternative rank function, and the SMAA is used to address the uncertainties within the IM selection. The performance criteria (e.g., efficiency, proficiency, practicality, sufficiency, and correlation) are evaluated for the investigated structural components, and the decision matrix is formulated based on the criteria of each IM alternative. Furthermore, the importance of the component to system reliability is quantified in a probabilistic manner using nonlinear time history analysis and serves as the weighting factors in MCDM stage. The holistic acceptability indices indicating the overall acceptability levels of IM alternatives are computed by the proposed approach. Additionally, the effects of different IMs (e.g., average spectral acceleration, peak ground velocity, and spectral acceleration) on probabilistic seismic loss and resilience are investigated to further support the IM selection. The proposed approach is illustrated on a highway bridge, and the results are presented.