Alpha shape based design space decomposition for island failure regions in reliability based design

Treatment of uncertainties in structural design involves identifying the boundaries of the failure domain to estimate reliability. When the structural responses are discontinuous or highly nonlinear, the failure regions tend to be an island in the design space. The boundaries of these islands are to be approximated to estimate reliability and perform optimization. This work proposes Alpha (α) shapes, a computational geometry technique to approximate such boundaries. The α shapes are simple to construct and only require Delaunay Tessellation. Once the boundaries are approximated based on responses sampled in a design space, a computationally efficient ray shooting algorithm is used to estimate the reliability without any additional simulations. The proposed approach is successfully used to decompose the design space and perform Reliability based Design Optimization of a tube impacting a rigid wall and a tuned mass damper.

[1]  Vladimir Kluzner,et al.  alpha-Shape Based Classification with Applications to Optical Character Recognition , 2011, 2011 International Conference on Document Analysis and Recognition.

[2]  I-Tung Yang,et al.  Reliability-based design optimization with cooperation between support vector machine and particle swarm optimization , 2012, Engineering with Computers.

[3]  J. Sobieszczanski-Sobieski,et al.  Optimization of car body under constraints of noise, vibration, and harshness (NVH), and crash , 2001 .

[4]  Jean-Daniel Boissonnat,et al.  Complexity of the delaunay triangulation of points on surfaces the smooth case , 2003, SCG '03.

[5]  G. Ziegler Lectures on Polytopes , 1994 .

[6]  P. Beran,et al.  A multifidelity approach for the construction of explicit decision boundaries: application to aeroelasticity , 2010 .

[7]  David G. Kirkpatrick,et al.  On the shape of a set of points in the plane , 1983, IEEE Trans. Inf. Theory.

[8]  Gerhart I. Schuëller,et al.  A survey on approaches for reliability-based optimization , 2010 .

[9]  A. Basudhar,et al.  Constrained efficient global optimization with support vector machines , 2012, Structural and Multidisciplinary Optimization.

[10]  Hyun-Chul Kim,et al.  Constructing support vector machine ensemble , 2003, Pattern Recognit..

[11]  Kai Hormann,et al.  The point in polygon problem for arbitrary polygons , 2001, Comput. Geom..

[12]  Hasan Kurtaran,et al.  Crashworthiness design optimization using successive response surface approximations , 2002 .

[13]  Herbert Edelsbrunner,et al.  Three-dimensional alpha shapes , 1992, VVS.

[14]  Alan F. Murray,et al.  International Joint Conference on Neural Networks , 1993 .

[15]  J. Beck,et al.  Estimation of Small Failure Probabilities in High Dimensions by Subset Simulation , 2001 .

[16]  A. Basudhar,et al.  An improved adaptive sampling scheme for the construction of explicit boundaries , 2010 .

[17]  Peng Jiang,et al.  Reliability assessment with correlated variables using support vector machines , 2011 .

[18]  Erdem Acar,et al.  Effects of the correlation model, the trend model, and the number of training points on the accuracy of Kriging metamodels , 2013, Expert Syst. J. Knowl. Eng..

[19]  Layne T. Watson,et al.  Pitfalls of using a single criterion for selecting experimental designs , 2008 .

[20]  Mohamed S. Ebeida,et al.  Spoke Darts for Efficient High Dimensional Blue Noise Sampling. , 2014 .

[21]  Irwin King,et al.  A study of the relationship between support vector machine and Gabriel graph , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

[22]  László Szirmay-Kalos,et al.  Worst-case versus average case complexity of ray-shooting , 1998, Computing.

[23]  C. A. Murthy,et al.  Selection of alpha for alpha-hull in R2 , 1997, Pattern Recognit..

[24]  Raphael T. Haftka,et al.  A convex hull approach for the reliability-based design optimization of nonlinear transient dynamic problems , 2007 .

[25]  Antonio Harrison Sánchez,et al.  Limit state function identification using Support Vector Machines for discontinuous responses and disjoint failure domains , 2008 .

[26]  Samy Missoum,et al.  Constrained Ecient Global Optimization with Probabilistic Support Vector Machines , 2010 .

[27]  Achintya Haldar,et al.  A novel reliability evaluation method for large dynamic engineering systems , 2010, 2010 2nd International Conference on Reliability, Safety and Hazard - Risk-Based Technologies and Physics-of-Failure Methods (ICRESH).

[28]  Arthur W. Lees,et al.  Efficient robust design via Monte Carlo sample reweighting , 2007 .

[29]  M. Niranjan,et al.  Sequential support vector machines , 1999, Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468).

[30]  Hong Yan Hao,et al.  Reliability Analysis Method Based on Support Vector Machines Classification and Adaptive Sampling Strategy , 2012 .

[31]  Jack P. C. Kleijnen,et al.  Optimization and Sensitivity Analysis of Computer Simulation Models by the Score Function Method , 1996 .

[32]  K. Choi,et al.  Sampling-Based Stochastic Sensitivity Analysis Using Score Functions for RBDO Problems with Correlated Random Variables , 2011, DAC 2010.

[33]  Robert E. Melchers,et al.  Structural Reliability: Analysis and Prediction , 1987 .

[34]  Samy Missoum,et al.  Local update of support vector machine decision boundaries , 2009 .

[35]  A. Basudhar,et al.  Adaptive explicit decision functions for probabilistic design and optimization using support vector machines , 2008 .

[36]  Ren-Jye Yang,et al.  Optimization of car body under constraints of noise, vibration, and harshness (NVH), and crash , 2000 .

[37]  Samy Missoum,et al.  Reliability-based design optimization using Kriging and support vector machines , 2013 .

[38]  H. Cudney,et al.  Comparison of Probabilistic and Fuzzy Set Methods for Designing under Uncertainty , 1999 .

[39]  B. Youn,et al.  An Investigation of Nonlinearity of Reliability-Based Design Optimization Approaches , 2004 .

[40]  Ren-Jye Yang,et al.  An alternative stochastic sensitivity analysis method for RBDO , 2014 .

[41]  Mark de Berg,et al.  Ray Shooting, Depth Orders and Hidden Surface Removal , 1993, Lecture Notes in Computer Science.

[42]  Andreas Bender,et al.  Alpha Shapes Applied to Molecular Shape Characterization Exhibit Novel Properties Compared to Established Shape Descriptors , 2009, J. Chem. Inf. Model..

[43]  George I. N. Rozvany,et al.  Structural and Multidisciplinary Optimization , 1995 .

[44]  R. Haftka,et al.  Error Amplification in Failure Probability Estimates of Small Errors in Response Surface Approximations , 2007 .

[45]  Bruno Sudret,et al.  Handling Bifurcations in the Optimal Design of Transient Dynamic Problems , 2004 .

[46]  Rex A. Dwyer,et al.  Average-case analysis of algorithms for convex hulls and Voronoi diagrams , 1988 .

[47]  Samy Missoum,et al.  Parallel construction of explicit boundaries using support vector machines , 2012 .

[48]  Kyung K. Choi,et al.  Adaptive virtual support vector machine for reliability analysis of high-dimensional problems , 2013 .