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Multivariate adaptive regression splines model for reliability assessment of serviceability limit state of twin caverns
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  • Multivariate adaptive regression splines model for reliability assessment of serviceability limit state of twin caverns
  • Multivariate adaptive regression splines model for reliability assessment of serviceability limit state of twin caverns
저자명
Zhang. Wengang,Goh. Anthony T.C.
간행물명
Geomechanics & engineering
권/호정보
2014년|7권 4호|pp.431-458 (28 pages)
발행정보
테크노프레스
파일정보
정기간행물|ENG|
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이 논문은 한국과학기술정보연구원과 논문 연계를 통해 무료로 제공되는 원문입니다.
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기타언어초록

Construction of a new cavern close to an existing cavern will result in a modification of the state of stresses in a zone around the existing cavern as interaction between the twin caverns takes place. Extensive plane strain finite difference analyses were carried out to examine the deformations induced by excavation of underground twin caverns. From the numerical results, a fairly simple nonparametric regression algorithm known as multivariate adaptive regression splines (MARS) has been used to relate the maximum key point displacement and the percent strain to various parameters including the rock quality, the cavern geometry and the in situ stress. Probabilistic assessments on the serviceability limit state of twin caverns can be performed using the First-order reliability spreadsheet method (FORM) based on the built MARS model. Parametric studies indicate that the probability of failure $P_f$ increases as the coefficient of variation of Q increases, and $P_f$ decreases with the widening of the pillar.