Identification Method of Optimal Copula Correlation Characteristic for Geological Parameters of Roof Structure

Jiazeng Cao, Tao Wang (), Chuanqi Zhu, Jianxin Yu, Xu Chen and Xin Zhang
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Jiazeng Cao: State Key Laboratory for Geomechanics and Deep Underground Engineering, School of Mechanics and Civil Engineering, China University of Mining and Technology, Xuzhou 221116, China
Tao Wang: State Key Laboratory for Geomechanics and Deep Underground Engineering, School of Mechanics and Civil Engineering, China University of Mining and Technology, Xuzhou 221116, China
Chuanqi Zhu: State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines, Anhui University of Science & Technology, Huainan 232001, China
Jianxin Yu: School of Civil Engineering, Henan Polytechnic University, Jiaozuo 454003, China
Xu Chen: School of Civil Engineering, Henan Polytechnic University, Jiaozuo 454003, China
Xin Zhang: China Railway 18 Bureau Group Co., Ltd., Tianjin 300222, China

Sustainability, 2023, vol. 15, issue 20, 1-18

Abstract: Limited by the actual investigation of coal mine engineering, the measured data obtained are often based on small sample characteristics. How to probabilistically de-integrate the prior information to obtain meaningful statistical values has received increasing attention from geotechnical engineers. In this study, an optimal copula function identification method for multidimensional geotechnical structures of coal mine roofs under the Bayesian approach is proposed. Firstly, the characterization method of multidimensional roof parameter correlation structures is proposed based on copula theory, and 167 sets of measured data from 24 coal mines at home and abroad are collected to study the measured identification results using the Bayesian method. Secondly, Monte Carlo simulation is utilized to compare the correct recognition rates of the commonly used AIC criterion and the Bayesian approach under different correlation structures. Finally, the influencing factors affecting the successful recognition rate of the Bayesian approach are analyzed. The results show that compared with the traditional AIC criterion, the Bayesian approach has more marked advantages in correctly recognizing the multidimensional parameter structures of roofs, and the number of measured samples, the strength of correlation coefficients, and the prior information have a major effect on the correct recognition rate of the optimal copula function under different real copula functions. In addition, the commonly used Gaussian copula has a better characterization effect in characterizing the multidimensional parameter correlation structure of the coal mine roofs, which can be prioritized to be used as a larger prior probability function in the evaluation process.

Keywords: copula theory; Bayesian approach; multidimensional parameters; correct recognition rate; roof correlation structure (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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