Slope safety evaluation by integrating multi-source monitoring information

A systematic method is presented for evaluating the slope safety utilizing multi-source monitoring information. First, a Bayesian network with continuously distributed variables for a slope involving the factor of safety, multiple monitoring indexes and their influencing parameters (e.g. friction angle and cohesion) is constructed. Then the prior probabilities for the Bayesian network are quantified considering model and parameter uncertainties. After that, multi-source monitoring information is used to update the probability distributions of the soil or rock model parameters and the factor of safety using Markov chain Monte Carlo simulation. An example of a slope with multiple monitoring parameters is presented to illustrate the proposed methodology. The method is able to integrate multi-source information based on slope stability mechanisms, and update the soil or rock parameters, the slope factor of safety, and the failure probability with the integrated monitoring information. Hence the evaluation becomes more reliable with the support of multiple sources of site-specific information.

[1]  Wilson H. Tang,et al.  Probability concepts in engineering planning and design , 1984 .

[2]  Yuan Yong.  Application of multi-sensor target tracking to multi-station monitoring data fusion in landslide , 2006 .

[3]  Francesca Bozzano,et al.  Research and development of advanced technologies for landslide hazard analysis in Italy , 2010 .

[4]  Finn V. Jensen,et al.  Bayesian Networks and Decision Graphs , 2001, Statistics for Engineering and Information Science.

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

[6]  Li Min Zhang,et al.  Analysis of human risks due to dam break floods—part 2: application to Tangjiashan landslide dam failure , 2012, Natural Hazards.

[7]  Daniel Straub,et al.  Bayesian Network Enhanced with Structural Reliability Methods: Application , 2010, 1203.5985.

[8]  James Llinas,et al.  Handbook of Multisensor Data Fusion : Theory and Practice, Second Edition , 2008 .

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

[10]  J. Zhang,et al.  Stability analysis of rainfall-induced slope failure: a review , 2011 .

[11]  S. Au.  Rain-induced slope instability in Hong Kong , 1998 .

[12]  David B. Dunson,et al.  Bayesian Data Analysis , 2010 .

[14]  Chin-Chuan Han,et al.  Multisource Data Fusion for Landslide Classification Using Generalized Positive Boolean Functions , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[15]  H. H. Einstein,et al.  Warning systems for natural threats , 2007 .

[16]  Neil Dixon,et al.  Quantification of slope displacement rates using acoustic emission monitoring , 2007 .

[17]  Fausto Guzzetti,et al.  Rainfall thresholds for the possible occurrence of landslides in Italy , 2010 .

[18]  Paul McCombie,et al.  Displacement based multiple wedge slope stability analysis , 2009 .

[19]  Sanjay Kumar Shukla,et al.  Stability analysis of multi-directional anchored rock slope subjected to surcharge and seismic loads , 2011 .

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

[21]  W. M. Brown,et al.  Real-Time Landslide Warning During Heavy Rainfall , 1987, Science.

[22]  Timothy D. Stark,et al.  Reliability in back analysis of slope failures , 1999 .

[23]  C. Bucher,et al.  A fast and efficient response surface approach for structural reliability problems , 1990 .

[24]  Adrienne Grêt-Regamey,et al.  Spatially explicit avalanche risk assessment linking Bayesian networks to a GIS , 2006 .

[25]  L. Ermini,et al.  Artificial Neural Networks applied to landslide susceptibility assessment , 2005 .

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

[27]  Bruce R. Ellingwood,et al.  A new look at the response surface approach for reliability analysis , 1993 .

[28]  Li Min Zhang,et al.  CALIBRATION OF METHODS FOR DESIGNING LARGE-DIAMETER BORED PILES: SERVICEABILITY LIMIT STATE , 2009 .

[29]  Filippo Catani,et al.  Monitoring, prediction, and early warning using ground-based radar interferometry , 2010 .

[30]  Herbert Einstein,et al.  Warning System for Natural Threats , 2016 .

[31]  Daniel Straub,et al.  Bayesian Network Enhanced with Structural Reliability Methods: Methodology , 2010, 1203.5986.

[32]  Natasha Smith,et al.  Bayesian networks for system reliability reassessment , 2001 .

[33]  C. F. Lee,et al.  Landslide characteristics and, slope instability modeling using GIS, Lantau Island, Hong Kong , 2002 .

[34]  Li Min Zhang,et al.  Analysis of human risks due to dam-break floods—part 1: a new model based on Bayesian networks , 2012, Natural Hazards.

[35]  P. Finlay,et al.  The relationship between the probability of landslide occurrence and rainfall , 1997 .

[36]  C. F. Lee,et al.  A dynamic model for rainfall-induced landslides on natural slopes , 2003 .

[37]  F. S. Wong,et al.  Slope Reliability and Response Surface Method , 1985 .

[38]  Jie Zhang,et al.  Characterizing geotechnical model uncertainty , 2009 .