Updating performance of high rock slopes by combining incremental time-series monitoring data and three-dimensional numerical analysis

Abstract Predicted performance of a complex geotechnical system is subject to errors due to the uncertainties associated with both the prediction model and the system parameters. This paper aims to develop a multi-step updating method to reduce the uncertainties of the prediction model and system parameters using incremental time-series monitoring data, and to enhance the prediction of the future performance of the complex geotechnical system. The multi-step updating method considers inherent uncertainty of the system, model uncertainty and measurement uncertainty. The prediction is updated and improved step by step with new monitoring information using Bayes’ theorem. Two realistic geotechnical cases including a basement excavation and a multi-stage excavation of a high rock slope are presented for illustration. The multi-step updating method integrates the theoretical computational model with observational evidence. With more monitoring information being incorporated, the prediction becomes closer to the actual performance of the system, and the distributions of the system parameters become closer to the reality.

[1]  Bak Kong Low,et al.  Probabilistic analysis of underground rock excavations using response surface method and SORM , 2011 .

[2]  Peter Lumb,et al.  The Variability of Natural Soils , 1966 .

[3]  Jie Zhang,et al.  Bayesian Framework for Characterizing Geotechnical Model Uncertainty , 2009 .

[4]  Youssef M. A. Hashash,et al.  Simplified Model for Wall Deflection and Ground-Surface Settlement Caused by Braced Excavation in Clays , 2007 .

[5]  Doug Stead,et al.  Integration of field characterisation, mine production and InSAR monitoring data to constrain and calibrate 3-D numerical modelling of block caving-induced subsidence , 2012 .

[6]  D. V. Griffiths,et al.  Three-Dimensional Probabilistic Foundation Settlement , 2005 .

[7]  Zhou Chuangbing Stability analysis of abutment slope at left bank of Jinping-I Hydropower Project during construction , 2012 .

[8]  Hongwei Huang,et al.  Characterising geotechnical model uncertainty by hybrid Markov Chain Monte Carlo simulation , 2012 .

[9]  W. Allen Marr,et al.  Instrumentation and Monitoring of Slope Stability , 2013 .

[10]  Runqiu Huang,et al.  Deformation mechanism and stability evaluation for the left abutment slope of Jinping I hydropower station , 2010 .

[11]  F. López Gayarre,et al.  Predicting blasting propagation velocity and vibration frequency using artificial neural networks , 2012 .

[12]  Bak Kong Low,et al.  Reliability analysis of ground–support interaction in circular tunnels using the response surface method , 2011 .

[13]  Wilson H. Tang,et al.  Probability Concepts in Engineering: Emphasis on Applications to Civil and Environmental Engineering , 2006 .

[14]  D. V. Griffiths,et al.  SLOPE STABILITY ANALYSIS BY FINITE ELEMENTS , 1999 .

[15]  Kok-Kwang Phoon,et al.  Evaluation of Geotechnical Property Variability , 1999 .

[16]  Giovanni B. Crosta,et al.  Failure forecast for large rock slides by surface displacement measurements , 2003 .

[17]  Jie Zhang,et al.  Back analysis of slope failure with Markov chain Monte Carlo simulation , 2010 .

[18]  Dian-Qing Li,et al.  Slope safety evaluation by integrating multi-source monitoring information , 2014 .

[19]  T. Sasaki,et al.  Applicability of the multiple yield model for estimating the deformation of vertical rock walls during large-scale excavations , 2012 .

[20]  Daniel Straub,et al.  Reliability updating with equality information , 2011, 1203.5405.

[21]  K. Phoon,et al.  Characterization of Geotechnical Variability , 1999 .

[22]  C. Hsein Juang,et al.  Reliability Analysis and Updating of Excavation-Induced Ground Settlement for Building Serviceability Assessment , 2008 .

[23]  Chang-Yu Ou,et al.  Characteristics of ground surface settlement during excavation , 1993 .

[24]  Chuangbing Zhou,et al.  A generalized multi-field coupling approach and its application to stability and deformation control of a high slope , 2011 .

[25]  Li Wu,et al.  Knowledge-based and data-driven fuzzy modeling for rockburst prediction , 2013 .