Uncertainty Quantification in Predicting UCS Using Fully Bayesian Gaussian Process Regression with Consideration of Model Class Selection-Reference-Cited by-同舟云学术

Uncertainty Quantification in Predicting UCS Using Fully Bayesian Gaussian Process Regression with Consideration of Model Class Selection

Published:2023-07-20 Issue: Volume: Page:
ISSN:
Container-title:Geo-Risk 2023
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Author:

Song Chao¹¹,Zhao Tengyuan¹¹

Affiliation:

1. School of Human Settlements and Civil Engineering, Xi’an Jiaotong Univ., Xi’an, Shaanxi, China.

Publisher

American Society of Civil Engineers

Link

https://ascelibrary.org/doi/pdf/10.1061/9780784484975.002

Reference22 articles.

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3. Prediction of the strength and elasticity modulus of granite through an expert artificial neural network;Armaghani D. J.;Arab. J. Geosci.,2016

4. Application of genetic programming to predict the uniaxial compressive strength and elastic modulus of carbonate rocks;Beiki M.;Int. J. Rock Mech. Min. Sci.,2013

5. Application of Generalized Regression Neural Networks in Predicting the Unconfined Compressive Strength of Carbonate Rocks

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