Estimation of Random Field Parameters for Soil Properties: A Bayesian Method with a New Prior Distribution
Publication: Geo-Risk 2023
ABSTRACT
Characterization of the spatial variability of soil properties is a prerequisite for risk and reliability analysis of geotechnical structures. The spatial variability of soil properties is usually characterized by random field parameters, including mean value, standard deviation, and autocorrelation distance. Due to the limited and sparse data usually available in geotechnical practice, the estimated random field parameters usually suffer from large uncertainty. To address this issue, this paper proposes a Bayesian estimation method that uses a novel prior probability distribution derived from soil data and the likelihood ratio test statistics. The unique feature of the proposed probability distribution is that it has an analytical form, making it able to be readily used in Bayesian updating. The effectiveness of the proposed method is demonstrated using simulated soil data. The results show that the Bayesian estimation with this prior information significantly reduces the uncertainty of the random field parameters, compared with the estimation of the maximum likelihood method and that of a Bayesian method using a uniform prior distribution.
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Information & Authors
Information
Published In
Geo-Risk 2023
Pages: 59 - 68
History
Published online: Jul 20, 2023
ASCE Technical Topics:
- Analysis (by type)
- Bayesian analysis
- Disaster risk management
- Engineering fundamentals
- Geomechanics
- Geometry
- Geotechnical engineering
- Mathematics
- Parameters (statistics)
- Probability
- Risk management
- Soil analysis
- Soil mechanics
- Soil properties
- Spatial variability
- Statistical analysis (by type)
- Statistics
- Structural behavior
- Structural engineering
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