Bayesian Learning–Based Data Analysis of Uniaxial Compressive Strength of Rock: Relevance Feature Selection and Prediction Reliability Assessment
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 6, Issue 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.
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Acknowledgments
This work was supported by the Natural Science Foundation of Guangdong Province, China (2017A030313262), Pearl River S&T Nova Program of Guangzhou (201806010172), Science and Technology Program of Guangzhou (201804020069), the Fundamental Research Funds for the Central Universities (2018ZD42), and the Research Committee of the University of Macau under Research Grant No. MYRG2015-00048-FST. This generous support is gratefully acknowledged.
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Published In
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 6 • Issue 1 • March 2020
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©2019 American Society of Civil Engineers.
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Received: Oct 8, 2018
Accepted: May 15, 2019
Published online: Oct 30, 2019
Published in print: Mar 1, 2020
Discussion open until: Mar 30, 2020
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