Reliability Analysis of Quick Sand Condition

详细信息    查看全文

• 作者：Pijush Samui ; Pradeep Kurup ; S. Dhivya ; J. Jagan
• 关键词：Quick sand ; First order reliability method ; Generalized regression neural network ; Gaussian process regression ; Multivariate adaptive regression spline
• 刊名：Geotechnical and Geological Engineering
• 出版年：2016
• 出版时间：April 2016
• 年：2016
• 卷：34
• 期：2
• 页码：579-584
• 全文大小：477 KB
• 参考文献：Alonso EE (1976) Risk analysis of slopes and its application to slopes in Canadian sensitive clays. Geotechnique 26(3):453–472CrossRef
  Baecher GB, Christian JT (2003) Reliability and statistics in geotechnical engineering. Wiley, Chichester
  Basser H, Karami H, Shamshirband S, Jahangirzadeh A, Akib S, Saboohi H (2014) Predicting optimum parameters of a protective spur dike using soft computing methodologies—a comparative study. Comput Fluids 97:168–176CrossRef
  Cornell CA (1971) First-order uncertainty analysis of soils deformation and stability. In: Proceedings of 1st international conference on application of probability and statistics in soil and structural engineering (ICAPI), Hong Kong, pp 129–44
  Friedman JH (1991) Multivariate adaptive regression splines. Ann Stat 19:1–141CrossRef
  Goh A, Zhang WG (2014) An improvement to MLR model for predicting liquefaction-induced lateral spread using multivariate adaptive regression splines. Eng Geol 170:1–10CrossRef
  Gu D, Hu H (2012) Spatial Gaussian process regression with mobile sensor networks. IEEE Trans Neural Netw Learn Syst 23(8):1279–1290CrossRef
  Husein Malkawi AI, Hassan WF, Abdulla FA (2000) Uncertainty and reliability analysis applied to slope stability. Struct Saf 22(2):161–187CrossRef
  Jin H, Chen X, Yang J, Wu L (2014) Adaptive soft sensor modeling framework based on just-in-time learning and kernel partial least squares regression for nonlinear multiphase batch processes. Comput Chem Eng 71:77–93CrossRef
  Kaveh A, Fahimi-Farzam M, Kalateh-Ahani M (2012) Time-history analysis based optimal design of space trusses: the CMA evolution strategy approach using GRNN and WA. Struct Eng Mech 44(3):379–403CrossRef
  Kumar P, Singh Y (2013) Comparative analysis of software reliability predictions using statistical and machine learning methods. Int J Intell Syst Technol Appl 12(3–4):230–253
  Liu Z, Tesfamariam S (2012) Prediction of lateral spread displacement: data-driven approaches. Bull Earthq Eng 10(5):1431–1454CrossRef
  Nearing GS, Gupta HV, Crow WT (2013) Information loss in approximately Bayesian estimation techniques: a comparison of generative and discriminative approaches to estimating agricultural productivity. J Hydrol 507:163–173CrossRef
  Sekulic S, Kowalski BR (1992) MARS: a tutorial. J Chemom 6:199–216CrossRef
  Singh S, Murthy TVR (2013) Neural network-based sensor fault accommodation in flight control system. Journal of Intelligent Systems 22(3):317–333
  Specht DF (1990) Probabilistic neural networks. Neural Netw 3:109–118CrossRef
  Specht DF (1991) A general regression neural network. IEEE Trans Neural Netw 2(6):568–576CrossRef
  Tang WH, Yucemen WS, Ang AHS (1976) Probability-based short term design of slopes. Can Geotech J 13(3):201–215CrossRef
  Vanmarcke EH (1977) Reliability of earth slopes. J Geotech Eng Div ASCE 103(11):1227–1246
  Wu TH, Kraft LM (1970) Safety analysis of slopes. J Soil Mech Found Div ASCE 96(2):609–630
  
• 作者单位：Pijush Samui (1)
  Pradeep Kurup (2)
  S. Dhivya (3)
  J. Jagan (3)
  
  1. Department of Civil Engineering, NIT Patna, Bihar, India
  2. Department of Civil and Environmental Engineering, University of Massachusetts Lowell, 1 University Ave., Lowell, MA, 01854, USA
  3. Centre for Disaster Mitigation and Management, VIT University, Vellore, 632014, India
  
• 刊物类别：Earth and Environmental Science
• 刊物主题：Earth sciences
  Geotechnical Engineering
  Hydrogeology
  Terrestrial Pollution
  Waste Management and Waste Technology
  Civil Engineering
  
• 出版者：Springer Netherlands
• ISSN：1573-1529

文摘

First order reliability method (FORM) is generally used for reliability analysis in geotechnical engineering. This article adopts generalized regression neural network (GRNN) based FORM, Gaussian process regression (GPR) based FORM and multivariate adaptive regression spline (MARS) based FORM for reliability analysis of quick sand condition. GRNN is related to the radial basis function (RBF) network. GPR is developed based on probabilistic framework. MARS is a nonparametric regression technique. A comparative study has been carried out between the developed models. The performance of GPR based FORM and MARS based FORM match well with the FORM. This article gives the alternative methods for reliability analysis of quick sand condition.