As geotechnical research and design codes rely increasingly on probabilistic approaches, site characterization should also be conducted in the light of the explicit quantification of the uncertainty and spatial variability in soil properties. This paper provides a practical case-study application of spatial variability analysis of piezocone data obtained at a test site in the Lagoon surrounding the historic city of Venice, in North-Eastern Italy, where unusually dense and regularly spaced CPT test data were available. Empirical semivariograms are calculated for cone resistance, sleeve friction, and porewater pressure, along with the soil behavior classification index obtained from these measurements at a set of reference measurement depths. A number of theoretical semivariogram models are fitted comparatively and best-fit models are selected based on objective criteria and subjective judgment. Characteristic semivariogram parameters, providing information on correlation distance and small-scale variability, are retrieved. Modeling options are explained and results are analyzed and assessed critically.
G. Gottardi, M.R. (2022). Quantitative modelling of spatial variability of piezocone data from Venice lagoon silty soils. Leiden : CRC Press/Balkema, Taylor & Francis Group.
Quantitative modelling of spatial variability of piezocone data from Venice lagoon silty soils
G. Gottardi;M. Ranalli;L. Tonni;
2022
Abstract
As geotechnical research and design codes rely increasingly on probabilistic approaches, site characterization should also be conducted in the light of the explicit quantification of the uncertainty and spatial variability in soil properties. This paper provides a practical case-study application of spatial variability analysis of piezocone data obtained at a test site in the Lagoon surrounding the historic city of Venice, in North-Eastern Italy, where unusually dense and regularly spaced CPT test data were available. Empirical semivariograms are calculated for cone resistance, sleeve friction, and porewater pressure, along with the soil behavior classification index obtained from these measurements at a set of reference measurement depths. A number of theoretical semivariogram models are fitted comparatively and best-fit models are selected based on objective criteria and subjective judgment. Characteristic semivariogram parameters, providing information on correlation distance and small-scale variability, are retrieved. Modeling options are explained and results are analyzed and assessed critically.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.