期刊
KSCE JOURNAL OF CIVIL ENGINEERING
卷 22, 期 3, 页码 941-950出版社
KOREAN SOCIETY OF CIVIL ENGINEERS-KSCE
DOI: 10.1007/s12205-018-1337-3
关键词
slope stability; naive bayes classifier; incomplete data; expectation maximization algorithm; circular failures
资金
- Distinguished Middle-Aged and Young Scientist Encourage and Reward Foundation of Shandong Province [ZR2016EEB11, ZR2016EEB21]
- National Natural Science Foundation of China [51708251, 41602304]
- PhD Foundation of University of Jinan [XBS1648]
Slope stability prediction is of primary concern in identifying terrain that is susceptible to landslides and mitigating the damages caused by landslides. In this study, a Naive Bayes Classifier (NBC) was employed to predict slope stability for a slope subjected to circular failures, based on six input factors: slope height (H), slope angle (alpha), cohesion (c), friction angle (phi), unit weight (gamma), and pore pressure ratio (r (u) ). An expectation maximization algorithm was used to perform parameter learning for the NBC with an incomplete data set of 69 slope cases. The model validation with 13 new cases shows that, when compared to the existing empirical approach, the proposed NBC model yields better performance in terms of both accuracy and applicability (i.e., the NBC allows us to determine the probability of slope stability based on any subset of the six input factors).
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