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Bayesian Learning-Based Data Analysis of Uniaxial Compressive Strength of Rock: Relevance Feature Selection and Prediction Reliability Assessment
He-Qing Mu1; Ka-Veng Yuen2,3
2019-10-30
Source PublicationASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume6Issue:1
Abstract

Estimation on the uniaxial compressive strength (UCS) of rock is an important issue in geotechnical engineering. Empirical relation establishment for UCS estimation is particularly favorable since core sample measurement is expensive, time consuming, and even infeasible. In this paper, two-stage Bayesian learning-based data analysis of UCS of rock is proposed. In the first stage, the sparse Bayesian learning, through the use of the automatic relevance determination (ARD) prior, is adopted to automatically select the relevance features among a set of possible features for the optimal empirical model. In the second stage, the optimal model-based outlier analysis for prediction reliability assessment is performed. The probability of outlier (PO) is utilized as a probabilistic measure for outlierness of a test point. The Gauss-Hermite quadrature is developed for efficiently evaluating the integral for the PO. A binary classification (regular class or outlier class) in the feature space is conducted based on the spatial distribution of the detected regular points and outliers, and the prediction unreliable region is depicted based on the classification result. In the example, the proposed two-stage Bayesian learning is applied for analyzing the UCS of the granite from Macao. The results show that the proposed learning is capable of conducting relevance feature selection and prediction reliability assessment simultaneously.

KeywordClassification Sparse Bayesian Learning Outlier Analysis Uniaxial Compressive Strength
DOI10.1061/AJRUA6.0001030
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaEngineering
WOS SubjectEngineering, Civil
WOS IDWOS:000539489300005
PublisherASCE-AMER SOC CIVIL ENGINEERS, 1801 ALEXANDER BELL DR, RESTON, VA 20191-4400
Scopus ID2-s2.0-85074413576
Fulltext Access
FWCI0.2869075
Citation statistics
Document TypeJournal article
CollectionPersonal research not belonging to the institution
Corresponding AuthorKa-Veng Yuen
Affiliation1.School of Civil Engineering and Transportation,State Key Laboratory of Subtropical Building Science,South China Univ. of Technology,Guangzhou,510640,China
2.State Key Laboratory on Internet of Things for Smart City,Univ. of Macau,999078,Macao
3.School of Civil Engineering and Transportation,South China Univ. of Technology,Guangzhou,510640,China
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
He-Qing Mu,Ka-Veng Yuen. Bayesian Learning-Based Data Analysis of Uniaxial Compressive Strength of Rock: Relevance Feature Selection and Prediction Reliability Assessment[J]. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering,2019,6(1).
APA He-Qing Mu,&Ka-Veng Yuen.(2019).Bayesian Learning-Based Data Analysis of Uniaxial Compressive Strength of Rock: Relevance Feature Selection and Prediction Reliability Assessment.ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering,6(1).
MLA He-Qing Mu,et al."Bayesian Learning-Based Data Analysis of Uniaxial Compressive Strength of Rock: Relevance Feature Selection and Prediction Reliability Assessment".ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering 6.1(2019).
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