Probabilistic Characterization of Site-Specific Inherent Variability of Undrained Shear Strength Using Both Indirect and Direct Measurements
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 4, Issue 1
Abstract
Determining geotechnical design parameters (e.g., undrained shear strength ) is an essential step in the analysis and design of geotechnical structures at a particular site. The estimated design parameters are affected by the actual variability (i.e., inherent variability) of design parameters concerned, measurement errors due to imperfections in the measuring process, and transformation uncertainty associated with the transformation model linking measured parameters [e.g., standard penetration test (SPT) data] to design parameters (e.g., ). These uncertainties are lumped together as the total variability of design parameters. It is the actual variability resulting from natural geological factors, not the total variability, that directly affects the underlying mechanism and actual response of geotechnical structures. Proper characterization of site-specific geotechnical inherent variability is, hence, pivotal to accurately estimating the actual responses of geotechnical structures at a site. This paper develops a Bayesian sequential updating (BSU) approach for probabilistic characterization of site-specific inherent variability of of clay. The proposed BSU approach systematically combines prior knowledge (e.g., engineering judgment and experience) and site-specific information from both indirect (e.g., SPT data) and direct (e.g., values from triaxial tests) measurements to inversely infer the inherent variability of at a particular site. It accounts, explicitly and quantitatively, for effects of measurement errors and transformation uncertainty on the probabilistic characterization of site-specific inherent variability of . The proposed approach is illustrated and validated using real-life and simulated data. It is shown that the proposed approach provides proper probabilistic characterization of site-specific inherent variability of based on available information from multiple sources. Sensitivity studies are also performed to explore effects of measurement errors on the performance of the proposed approach.
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Acknowledgments
The work described in this paper was supported by grants from National Key Research and Development Program of China (Project No. 2016YFC0800208), and the National Natural Science Foundation of China (Project Nos. 51409196, 51579190, 51528901, and 51679174). The financial support is gratefully acknowledged.
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Published In
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 4 • Issue 1 • March 2018
Copyright
©2017 American Society of Civil Engineers.
History
Received: Jan 26, 2017
Accepted: Jul 11, 2017
Published online: Dec 8, 2017
Published in print: Mar 1, 2018
Discussion open until: May 8, 2018
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