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Authors: Raspa, G.*
Moscatelli, M.*
Stigliano, F.*
Patera, A.*
Marconi, F.*
Folle, D.*
Vallone, R.*
Mancini, M.*
Cavinato, G. P.*
Milli, S.*
Coimbra Leite Costa, J. P.*
Title: Geotechnical characterization of the upper Pleistocene-Holocene alluvial deposits of Roma (Italy) by means of multivariate geostatistics: Cross-validation results
Title of journal: Engineering Geology
Series/Report no.: /101 (2008)
Publisher: Elsevier
Issue Date: 2008
DOI: 10.1016/j.enggeo.2008.06.007
Keywords: Alluvial deposits
Geotechnical properties
Principal Component Analysis
Multivariate geostatistics
Abstract: We are presenting an attempt to evaluate the spatial variability of geotechnical parameters in the upper Pleistocene–Holocene alluvial deposits of Roma (Italy) by means of multivariate geostatistics. The upper Pleistocene–Holocene alluvial deposits of Roma are sensitive to high levels of geohazard. They occupy a sizable and significant part of the city, being the foundation for many monuments, historical neighborhoods, and archaeological areas, and the main host of the present and future subway lines. We have stored information from more than 2000 geotechnical boreholes crossing the alluvial deposits into a relational database. For the present study, only the boreholes with lithologic/textural interpretation and geotechnical information were selected. The set includes 283 boreholes and 719 samples, which have a set of geotechnical information comprising physical properties and mechanical parameters. Techniques of multivariate statistics and geostatistics were combined and compared to evaluate the estimation methods of the mechanical parameters, with special reference to the drained friction angle from direct shear test (φ′). Principal Component Analysis was applied to the dataset to highlight the relationships between the geotechnical parameters. Through cross-validation analysis, multiple linear regression, kriging, and cokriging were tested as estimators of φ′. Cross-validation demonstrates that the cokriging with granulometries as auxiliary variables is the most suitable method to estimate φ′. In addition to proving that cokriging is a good estimator of φ′, cross-validation demonstrates that input data are coherent and this allows us to use them for estimation of geotechnical parameters, although they come from different laboratories and different vintages. Nevertheless, to get the same good results of cross-validation in estimation, it is necessary for granulometries to be available at grid points. Since this information being not available at all grid points, it is expected that, in the future, textural information can be derived in an indirect way, i.e., from lithologic/textural spatial reconstructions.
Appears in Collections:05.01.04. Statistical analysis
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