A novel approach to assess the ability of a protection barrier to mitigate rockfall hazard

The paper presents a novel approach to assess the ability of a protection barrier to mitigate rockfall hazard. Using a meta-modeling approach, a simplified model of a widely used type of rockfall protection barrier was developed to predict the barrier capability to stop the block. A meta-model was created based on FE simulation results considering six input parameters relevant for the wide variety of impact conditions observed on natural sites. The meta-model was then used in combination with a rockfall trajectory simulation tool to evaluate the efficiency of the barrier to mitigate rockfall hazard for two real cases. The results of the study reveal that the meta-model is effective to accurately predict the response of the barrier for different impact conditions. In addition, the coupling of the meta-model with a rockfall trajectory simulation tool provides a better assessment of the barrier efficiency compared to classical design guidelines as it accounts for the distribution of the various parameters describing the block incident trajectory. This approach appears promising to improve rockfall quantitative hazard assessment and optimize rockfall mitigation strategies.

[1]  F. Nicot,et al.  Nonlinear Discrete Mechanical Model of Steel Rings , 2017 .

[2]  Franck Bourrier,et al.  A Reliability-Based Approach for the Design of Rockfall Protection Fences , 2014, Rock Mechanics and Rock Engineering.

[3]  F. Bourrier,et al.  Analysis of the effect of trees on block propagation using a DEM model: implications for rockfall modelling , 2017, Landslides.

[4]  Sonja Kuhnt,et al.  Design and analysis of computer experiments , 2010 .

[5]  Axel Volkwein,et al.  Numerical simulation of flexible rockfall protection systems , 2005 .

[6]  Eleni Chatzi,et al.  Parameter identification of rockfall protection barrier components through an inverse formulation , 2014 .

[7]  F. Bourrier,et al.  Toward objective rockfall trajectory simulation using a stochastic impact model , 2009 .

[8]  Franck Bourrier,et al.  Accounting for the variability of rock detachment conditions in designing rockfall protection structures , 2016, Natural Hazards.

[9]  Vladimir Vapnik,et al.  The Nature of Statistical Learning , 1995 .

[10]  Klaus Thoeni,et al.  Discrete modelling of hexagonal wire meshes with a stochastically distorted contact model , 2013 .

[11]  Franck Bourrier,et al.  Introducing Meta-models for a More Efficient Hazard Mitigation Strategy with Rockfall Protection Barriers , 2018, Rock Mechanics and Rock Engineering.

[12]  Runze Li,et al.  Design and Modeling for Computer Experiments , 2005 .

[13]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[14]  T. Simpson,et al.  Comparative studies of metamodelling techniques under multiple modelling criteria , 2001 .

[15]  F. Berger,et al.  Real-size experiments and 3-D simulation of rockfall on forested and non-forested slopes , 2006 .

[16]  Franck Bourrier,et al.  A New Approach to Evaluate the Effectiveness of Rockfall Barriers , 2016 .

[17]  J. Corominas,et al.  Quantitative assessment of the residual risk in a rockfall protected area , 2005 .

[18]  Anna Giacomini,et al.  Numerical Modelling of a Low-Energy Rockfall Barrier: New Insight into the Bullet Effect , 2015, Rock Mechanics and Rock Engineering.

[19]  G. Baudat,et al.  Kernel-based methods and function approximation , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[20]  Bruno Sudret,et al.  Global sensitivity analysis using polynomial chaos expansions , 2008, Reliab. Eng. Syst. Saf..

[21]  Guido Gottardi,et al.  Full-scale Modelling of Falling Rock Protection Barriers , 2010 .

[22]  François Nicot,et al.  From a constitutive modelling of metallic rings to the design of rockfall restraining nets , 2001 .

[23]  Guido Gottardi,et al.  Virtual testing of existing semi-rigid rockfall protection barriers , 2015 .

[24]  Bruno Sudret,et al.  Efficient computation of global sensitivity indices using sparse polynomial chaos expansions , 2010, Reliab. Eng. Syst. Saf..

[25]  R. Brereton,et al.  Support vector machines for classification and regression. , 2010, The Analyst.

[26]  Iftikhar Ahmad,et al.  A Review of Classification Approaches Using Support Vector Machine in Intrusion Detection , 2011 .

[27]  David Bertrand,et al.  Full-Scale Dynamic Analysis of an Innovative Rockfall Fence Under Impact Using the Discrete Element Method: from the Local Scale to the Structure Scale , 2012, Rock Mechanics and Rock Engineering.

[28]  G. Gottardi,et al.  A simple model to simulate the full-scale behaviour of falling rock protection barriers , 2010 .

[29]  Guido Gottardi,et al.  Design of falling rock protection barriers using numerical models , 2013 .

[30]  D. Dias,et al.  Probabilistic Analysis of Pressurized Tunnels against Face Stability Using Collocation-Based Stochastic Response Surface Method , 2011 .

[31]  Guido Gottardi,et al.  Three-dimensional numerical modelling of falling rock protection barriers , 2012 .