Title: A Bayesian Evaluation of Information for Foundation Engineering Design
Abstract:Summary by Limin Zhang: This was an early-career paper of Prof. Wilson Tang when he was an assistant professor at the University of Illinois at Urbana-Champaign, two years after he received his Ph.D. ...Summary by Limin Zhang: This was an early-career paper of Prof. Wilson Tang when he was an assistant professor at the University of Illinois at Urbana-Champaign, two years after he received his Ph.D. degree from Stanford University. In this paper, a framework has been developed through the use of Bayesian statistics whereby various sources of information, from subjective judgement to direct or indirect measurements, can be combined to give an overall prediction of the desired soil parameter. Expressions are derived for the resultant probabilistic distribution of the mean soil parameter, such as mean shear strength or mean compressibility, for the case where the basic soil parameter is normally distributed, and where linear calibration relationships exit between indirect measurements and the mean actual measurement. The expressions derived in this paper form a building block for decision analysis of problems concerning the gathering of information in foundation engineering. The selection of the optimal data-collection program is found to depend on the relative cost factors, the variability of the soil parameter as well as the calibration error of the indirect measurements. This paper lays a good foundation for practical use of Bayesian updating in geotechnical engineering design.Read More
Publication Year: 2017
Publication Date: 2017-06-01
Language: en
Type: article
Indexed In: ['crossref']
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Cited By Count: 9
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