A fast general extension algorithm of Latin hypercube sampling

References

• Shields MD, Zhang JX. The generalization of Latin hypercube sampling. Reliab Eng Syst Saf. 2016;148:96–108. doi: 10.1016/j.ress.2015.12.002
   Web of Science ®Google Scholar
• Steinberg DM, Lin D. Construction of orthogonal nearly Latin hypercubes. Qual Reliab Eng Int. 2015;31(8):1397–1406. doi: 10.1002/qre.1676
   Web of Science ®Google Scholar
• Dey A, Sarkar D. A note on the construction of orthogonal Latin hypercube designs. J Comb Des. 2016;24(3):105–111. doi: 10.1002/jcd.21408
   Web of Science ®Google Scholar
• Kang F, Xu Q, Li JJ. Slope reliability analysis using surrogate models via new support vector machines with swarm intelligence. Appl Math Model. 2016;40(11–12):6105–6120. doi: 10.1016/j.apm.2016.01.050
   Web of Science ®Google Scholar
• Kang F, Li JJ. Artificial bee colony algorithm optimized support vector regression for system reliability analysis of slopes. J Comput Civ Eng. 2016;30(3):04015040. doi: 10.1061/(ASCE)CP.1943-5487.0000514
   Web of Science ®Google Scholar
• Li W, Lu LY, Liu ZZ, et al. HIT-SEDAES: an integrated software environment for simulation experiment design, analysis and evaluation. Int J Model Simul Sci Comput. 2016;7(3):1650027. doi: 10.1142/S1793962316500276
   Web of Science ®Google Scholar
• Kang F, Han SX, Salgado R, et al. System probabilistic stability analysis of soil slopes using Gaussian process regression with Latin hypercube sampling. Comput Geotech. 2015;63:13–25. doi: 10.1016/j.compgeo.2014.08.010
   Web of Science ®Google Scholar
• Tissot JY, Prieur C. A randomized orthogonal array based procedure for the estimation of first and second order Sobol indices. J Statist Comput Simul. 2015;85(7):1358–1381. doi: 10.1080/00949655.2014.971799
   Web of Science ®Google Scholar
• Castaldo P, Palazzo B, Della Vecchia P. Life-cycle cost and seismic reliability analysis of 3D systems equipped with FPS for different isolation degrees. Eng Struct. 2016;125:349–363. doi: 10.1016/j.engstruct.2016.06.056
   Web of Science ®Google Scholar
• Sallaberry CJ, Helton JC, Hora SC. Extension of Latin hypercube samples with correlated variables. Reliab Eng Syst Saf. 2008;93(7):1047–1059. doi: 10.1016/j.ress.2007.04.005
   Web of Science ®Google Scholar
• Liu ZZ, Li W, Yang M. Two general extension algorithms of Latin hypercube sampling. Math Probl Eng. 2015;2015:450492.
   Google Scholar
• Tong C. Refinement strategies for stratified sampling methods. Reliab Eng Syst Saf. 2006;91(10):1257–1265. doi: 10.1016/j.ress.2005.11.027
   Web of Science ®Google Scholar
• Qian PZG. Nested Latin hypercube designs. Biometrika. 2009;96(4):957–970. doi: 10.1093/biomet/asp045
   Web of Science ®Google Scholar
• He X, Qian PZG. Nested orthogonal array-based Latin hypercube designs. Biometrika. 2011;98(3):721–731. doi: 10.1093/biomet/asr028
   Web of Science ®Google Scholar
• Williamson D. Exploratory ensemble designs for environmental models using k-extended Latin hypercubes. Environmetrics. 2015;26(4):268–283. doi: 10.1002/env.2335
   PubMed Web of Science ®Google Scholar
• Vorechovsky M. Hierarchical subset Latin hypercube sampling, PPK 2006 Pravdepodobnost porusovani konstrukci, Brno, Czech Republic; 2006. p. 285–298.
   Google Scholar
• Vorechovsky M. Extension of sample size in Latin hypercube sampling methodology and software. Proceedings of the 3rd symposium on life cycle and sustainability of civil infrastructure systems, Vienna, Austria; 2012. p. 2403–2410.
   Google Scholar
• Vorechovsky M. Hierarchical refinement of Latin hypercube samples. Comput Aided Civil Infrastruct Eng. 2015;30(5):394–411. doi: 10.1111/mice.12088
   Web of Science ®Google Scholar
• Xu J, Chen JJ, Qian PZG. Sequentially refined Latin hypercube designs: reusing every point. J Amer Statist Assoc. 2015;110(512):1696–1706. doi: 10.1080/01621459.2014.993078
   Web of Science ®Google Scholar
• Wang GG. Adaptive response surface method using inherited Latin hypercube design points. J Mech Des. 2003;125(2):210–220. doi: 10.1115/1.1561044
   Web of Science ®Google Scholar
• Blatman G, Sudret B. An adaptive algorithm to build up sparse polynomial chaos expansions for stochastic finite element analysis. Probab Eng Mech. 2010;25(2):183–197. doi: 10.1016/j.probengmech.2009.10.003
   Web of Science ®Google Scholar
• Wei X. Research of global optimization algorithm based on metamodel [Doctoral dissertation of Huazhong University of Science and Technology]; 2012. p. 70–75.
   Google Scholar
• West DB. Introduction to graph theory. Beijing: Pearson Education Asia Limited and China Machine Press; 2006.
   Google Scholar