Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/157178
Title: Research on machine learning based rockburst intensity prediction model
Authors: Mu, Xinyi
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Mu, X. (2022). Research on machine learning based rockburst intensity prediction model. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157178
Abstract: Rockburst is one of the difficult problems in large underground geotechnical and deep resource extraction projects, and accurate prediction of rockburst intensity level has important engineering significance and academic value. However, traditional prediction models are affected by a variety of complex factors, and their effectiveness needs to be improved in terms of index weight determination and practical engineering applications. In this dissertation, based on the established rockburst intensity level prediction database, two types of rockburst intensity level prediction models are established using machine learning techniques, and the effectiveness of the prediction models is verified.
URI: https://hdl.handle.net/10356/157178
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

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