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Published in: Soft Computing 14/2019

10-05-2018 | Methodologies and Application

An approach using Dempster–Shafer evidence theory to fuse multi-source observations for dam safety estimation

Authors: Huaizhi Su, Jie Ren, Zhiping Wen

Published in: Soft Computing | Issue 14/2019

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Abstract

Considering the limitations existing in the single-effect quantity analysis method of dam service behavior reasoning, the dam service behavior multi-effect quantities fusion reasoning model is built and implemented based on the study of improved methods about classic Dempster–Shafer (D–S) evidence fusion technologies. Focusing on the problems that classic D–S evidence fusion rules fail or the reasoning results are contrary to the intuition and convention due to the high-conflict evidences, the calculation methods of compatibility coefficient measurement matrices between any two basic probability assignment functions of evidences (E-BPAF) and between any two basic probability assignment functions of focal elements (FE-BPAF) are provided, respectively, based on the compatibility analysis of any two E-BPAFs and any two FE-BPAFs. The weight matrices about any two E-BPAFs and any two FE-BPAFs for initial BPAFs are defined, respectively, through compatibility coefficient matrices. Then, the comprehensive weight matrix for initial BPAFs is introduced to unify weight matrices. By analyzing and comparing some examples and existing study results, the performance test of the proposed method is conducted and the correctness and rationality are also verified. Finally, combining with an instance of a gravity arch dam project, the proposed method is utilized to fuse the multi-information from measuring points on the dam abutment, and the reasonable reasoning results are obtained about the dam service behavior.

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Metadata
Title
An approach using Dempster–Shafer evidence theory to fuse multi-source observations for dam safety estimation
Authors
Huaizhi Su
Jie Ren
Zhiping Wen
Publication date
10-05-2018
Publisher
Springer Berlin Heidelberg
Published in
Soft Computing / Issue 14/2019
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-018-3220-z

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