Bayesian Supervised Learning of Site-Specific Geotechnical Spatial Variability from Sparse Measurements-Reference-Cited by-同舟云学术

Bayesian Supervised Learning of Site-Specific Geotechnical Spatial Variability from Sparse Measurements

Published:2020-06 Issue:2 Volume:6 Page:04020019
ISSN:2376-7642
Container-title:ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
language:en
Short-container-title:ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng.

Author:

Hu Yue¹,Wang Yu²^ORCID,Zhao Tengyuan³,Phoon Kok-Kwang⁴

Affiliation:

1. Ph.D. Student, Dept. of Architecture and Civil Engineering, City Univ. of Hong Kong, Kowloon, Hong Kong SAR, China.

2. Associate Professor, Dept. of Architecture and Civil Engineering, City Univ. of Hong Kong, Kowloon, Hong Kong SAR, China (corresponding author). ORCID: .

3. Associate Professor, School of Human Settlements and Civil Engineering, Xi’an Jiaotong Univ., Xi’an 710049, China.

4. Distinguished Professor, Dept. of Civil and Environmental Engineering, National Univ. of Singapore, 21 Lower Kent Ridge Rd., Singapore.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Safety, Risk, Reliability and Quality,Building and Construction,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/AJRUA6.0001059

Reference65 articles.

1. Seismic site conditions in Gangneung, Korea, based on Rayleigh-wave dispersion curves and topographic data

2. Stable signal recovery from incomplete and inaccurate measurements

3. Bayesian model comparison and selection of spatial correlation functions for soil parameters

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