Sample size determination in geotechnical site investigation considering spatial variation and correlation

• Published in: Canadian geotechnical journal Vol. 56; no. 7; pp. 992 - 1002
• Main Authors: Wang, Yu, Guan, Zheng, Zhao, Tengyuan
• Format: Journal Article
• Language: English
• Published: Ottawa NRC Research Press 01-07-2019; Canadian Science Publishing NRC Research Press
• Subjects: Accuracy; Bayesian analysis; Bayesian method; Building codes; champ aléatoire; Compression; Compression tests; compressive sensing; Cone penetration tests; Correlation; Correlation analysis; détection de compression; Engineering; Geomaterials; Geotechnical data; Geotechnical engineering; geotechnical site investigation; Geotechnology; Investigations; Laboratory tests; méthode bayésienne; Probability theory; Questions; random field; Sample size; Samples; Site planning; Size determination; Soil properties; Spatial variations; Statistical methods; Statistics; Studies; taille de l’échantillon; Variation; étude géotechnique du site
• DOI: 10.1139/cgj-2018-0474
• EISSN: 1208-6010
• ISSN: 0008-3674

Site investigation is a fundamental element in geotechnical engineering practice, but only a small portion of geomaterials is sampled and tested during site investigation. This leads to a question of sample size determination: how many samples are needed to achieve a target level of accuracy for the results inferred from the samples? Sample size determination is a well-known topic in statistics and has many applications in a wide variety of areas. However, conventional statistical methods, which mainly deal with independent data, only have limited applications in geotechnical site investigation because geotechnical data are not independent, but spatially varying and correlated. Existing design codes around the world (e.g., Eurocode 7) only provide conceptual principles on sample size determination. No scientific or quantitative method is available for sample size determination in site investigation considering spatial variation and correlation of geotechnical properties. This study performs an extensive parametric study and develops a statistical chart for sample size determination with consideration of spatial variation and correlation using Bayesian compressive sensing or sampling. Real cone penetration test data and real laboratory test data are used to illustrate application of the proposed statistical chart, and the method is shown to perform well.