3D Probabilistic Site Characterization by Sparse Bayesian Learning-Reference-Cited by-同舟云学术

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3D Probabilistic Site Characterization by Sparse Bayesian Learning

Published:2020-12 Issue:12 Volume:146 Page:04020134
ISSN:0733-9399
Container-title:Journal of Engineering Mechanics
language:en
Short-container-title:J. Eng. Mech.

Author:

Ching Jianye¹^ORCID,Huang Wen-Han²,Phoon Kok-Kwang³^ORCID

Affiliation:

1. Professor, Dept. of Civil Engineering, National Taiwan Univ., Taipei 106, Taiwan (corresponding author). ORCID: .

2. Graduate Student, Dept. of Civil Engineering, National Taiwan Univ., Taipei 106, Taiwan.

3. Professor, Dept. of Civil & Environmental Engineering, National Univ. of Singapore, 117576 Singapore. ORCID:

Publisher

American Society of Civil Engineers (ASCE)

Subject

Mechanical Engineering,Mechanics of Materials

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29EM.1943-7889.0001859

Reference46 articles.

1. Transitional Markov Chain Monte Carlo: Observations and Improvements

2. Effect of Cone Penetration Conditioning on Random Field Model Parameters and Impact of Spatial Variability on Liquefaction-Induced Differential Settlements

3. Large Sample Spacing in Evaluation of Vertical Strength Variability of Clayey Soil

4. Scale of Fluctuation for Spatially Varying Soils: Estimation Methods and Values

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2. A novel data-driven 3D site characterisation method: Tucker decomposition-Bayesian compressive sensing and benchmarking study;Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards;2024-08-30

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4. Similarity quantification of soil spatial variability between two cross-sections using auto-correlation functions;Engineering Geology;2024-03

5. Spatial variability characterization of clayey waste soils and its impact on probabilistic stability assessment of a landfill slope;Acta Geotechnica;2024-02-08

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