Estimation of trend and random components of conditional random field using Gaussian process regression

By I. Yoshida, Y. Tomizawa, Y. Otake. 2021

96 citations recorded by the source corpus.

Abstract A method is proposed for simultaneously estimating the trend and random component of soil properties at arbitrary locations using Gaussian process regression with the superposition of multiple Gaussian random fields. The proposed method is applied to the estimation of the one-dimensional spatial distributions of the trend component of three synthetic datasets. A comparison of three covariance functions, namely Gaussian, Markovian, and binary noise, indicates that Gaussian covariance is most suitable for trend estimation. The scale of fluctuation and the standard deviation of the random component of the examples are estimated using the maximum likelihood estimation method. The proposed method is also applied to the estimation of the three-dimensional spatial distribution of the trend and random components based on measured cone penetration test data. It is shown that the trend and random components at arbitrary locations can be estimated. The Whittle-Matern covariance function is found to be more suitable than the Markovian covariance function for the estimation of the random component of the cone penetration test data based on the Akaike information criterion and the Bayesian information criterion.

Subfields: Civil and Structural Engineering, Environmental Engineering, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/829da635eafddd5e55cbb0702d5b7eaf82167c89

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Civil and Structural Engineering

1. A. Stuedlein
2. Jianye Ching
3. Zhiyong Yang
4. Kok-Kwang Phoon
5. Dianqing Li
6. Z. Cao
7. James L. Beck
8. Xiao-Song Tang

In Environmental Engineering

1. Kok-Kwang Phoon
2. Jianye Ching
3. George Stefanou
4. S. Quek

In Safety, Risk, Reliability and Quality

1. A. Stuedlein
2. Zhiyong Yang
3. Jianye Ching
4. Kok-Kwang Phoon
5. Dianqing Li
6. Z. Cao
7. Marcos A. Valdebenito
8. Gerhart I. Schuëller

Citations in the corpus

• Quasi-site-specific multivariate probability distribution model for sparse, incomplete, and three-dimensional spatially varying soil data (2021, methodology): “This method is termed GPR-MUSIC-X in this paper, because it is based on a variant of the Gaussian process regression (GPR) (e.g. Neal 1998; Rasmussen and Williams 2016; Yoshida, Tomizawa, and Otake 2021).”