Stability Risk Assessment of Underground Rock Pillars Using Logistic Model Trees

Ning Li, Masoud Zare, Congke Yi and Rafael Jimenez
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Ning Li: School of Mining Engineering, Anhui University of Science and Technology, Huainan 232001, China
Masoud Zare: United Consulting Group Ltd., Norcross, GA 30071, USA
Congke Yi: ETSI Caminos, Canales y Puertos, Technical University of Madrid, 28040 Madrid, Spain
Rafael Jimenez: ETSI Caminos, Canales y Puertos, Technical University of Madrid, 28040 Madrid, Spain

IJERPH, 2022, vol. 19, issue 4, 1-19

Abstract: Pillars are important structural elements that provide temporary or permanent support in underground spaces. Unstable pillars can result in rock sloughing leading to roof collapse, and they can also cause rock burst. Hence, the prediction of underground pillar stability is important. This paper presents a novel application of Logistic Model Trees (LMT) to predict underground pillar stability. Seven parameters—pillar width, pillar height, ratio of pillar width to height, uniaxial compressive strength of rock, average pillar stress, underground depth, and Bord width—are employed to construct LMTs for rock and coal pillars. The LogitBoost algorithm is applied to train on two data sets of rock and coal pillar case histories. The two models are validated with (i) 10-fold cross-validation and with (ii) another set of new case histories. Results suggest that the accuracy of the proposed LMT is the highest among other common machine learning methods previously employed in the literature. Moreover, a sensitivity analysis indicates that the average stress, p , and the ratio of pillar width to height, r , are the most influential parameters for the proposed models.

Keywords: rock pillar; Logistic Model Trees (LMT); stability prediction; cross-validation (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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