4.6 Article

A Decision Tree for Rockburst Conditions Prediction

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Review Engineering, Civil

Rockburst in underground excavations: A review of mechanism, classification, and prediction methods

Mahdi Askaripour et al.

Summary: Technical challenges in underground mining increase in complexity as mining goes deeper. One major challenge is rock mass stability and the risk of rockburst events. To overcome these challenges, advanced solutions and best practices must be developed. Rockbursts are common in underground mines and pose a threat to safety and operations. Understanding the conditions and mechanisms leading to rockbursts, and improving risk assessment methods, is necessary. This paper provides a summary of the current understanding of rockbursts in the mining industry, discusses their classifications and mechanisms, and reviews empirical prediction methods.

UNDERGROUND SPACE (2022)

Article Computer Science, Interdisciplinary Applications

Performance evaluation of hybrid FFA-ANFIS and GA-ANFIS models to predict particle size distribution of a muck-pile after blasting

Jian Zhou et al.

Summary: The study optimized the parameters of ANFIS using the firefly algorithm and genetic algorithm, comparing the accuracy of particle size prediction models and finding the ANFIS-GA model performed the best.

ENGINEERING WITH COMPUTERS (2021)

Article Computer Science, Interdisciplinary Applications

Investigating the effective parameters on the risk levels of rockburst phenomena by developing a hybrid heuristic algorithm

Jian Zhou et al.

Summary: This paper integrates the firefly algorithm and artificial neural network to accurately predict the risk of rockburst in deep mines and tunnels. The hybrid model successfully determines different hazard levels under various conditions and offers new solutions for classification based on success rates.

ENGINEERING WITH COMPUTERS (2021)

Article Metallurgy & Metallurgical Engineering

Rockburst prediction in hard rock mines developing bagging and boosting tree-based ensemble techniques

Wang Shi-ming et al.

Summary: Rockburst prediction in hard rock mines was examined using three tree-based ensemble methods, with the dataset evaluated using six widely accepted indices. The study found that bagging algorithm performed best in predicting the potential of rockburst compared to other algorithms and empirical criteria methods.

JOURNAL OF CENTRAL SOUTH UNIVERSITY (2021)

Article Computer Science, Artificial Intelligence

Cost-sensitive KNN classification

Shichao Zhang

NEUROCOMPUTING (2020)

Article Computer Science, Interdisciplinary Applications

Evaluation of rockburst occurrence and intensity in underground structures using decision tree approach

Ebrahim Ghasemi et al.

ENGINEERING WITH COMPUTERS (2020)

Article Computer Science, Interdisciplinary Applications

Long-term prediction of rockburst hazard in deep underground openings using three robust data mining techniques

Roohollah Shirani Faradonbeh et al.

ENGINEERING WITH COMPUTERS (2019)

Article Construction & Building Technology

Developing intelligent classification models for rock burst prediction after recognizing significant predictor variables, Section 2: Designing classifiers

Sajjad Afraei et al.

TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY (2019)

Article Construction & Building Technology

Rockburst prediction in kimberlite with unsupervised learning method and support vector classifier

Yuanyuan Pu et al.

TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY (2019)

Article Engineering, Geological

In-situ and experimental investigations of rockburst precursor and prevention induced by fault slip

Cai-Ping Lu et al.

INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES (2018)

Article Construction & Building Technology

Rockburst prediction and classification based on the ideal-point method of information theory

Chen Xu et al.

TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY (2018)

Review Construction & Building Technology

Evaluation method of rockburst: State-of-the-art literature review

Jian Zhou et al.

TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY (2018)

Review Engineering, Geological

Review of published rockburst events and their contributing factors

Ali Keneti et al.

ENGINEERING GEOLOGY (2018)

Article Engineering, Environmental

Experimental study of factors affecting fault slip rockbursts in deeply buried hard rock tunnels

Fanzhen Meng et al.

BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT (2017)

Article Automation & Control Systems

Training ANFIS Using the Enhanced Bees Algorithm and Least Squares Estimation

Hosein Marzi et al.

INTELLIGENT AUTOMATION AND SOFT COMPUTING (2017)

Article Construction & Building Technology

Predicting rock burst hazard with incomplete data using Bayesian networks

Ning Li et al.

TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY (2017)

Article Engineering, Multidisciplinary

The Use of Data Mining Techniques in Rockburst Risk Assessment

Luis Ribeiro e Sousa et al.

ENGINEERING (2017)

Article Computer Science, Interdisciplinary Applications

Classification of Rockburst in Underground Projects: Comparison of Ten Supervised Learning Methods

Jian Zhou et al.

JOURNAL OF COMPUTING IN CIVIL ENGINEERING (2016)

Article Metallurgy & Metallurgical Engineering

Feasibility of stochastic gradient boosting approach for predicting rockburst damage in burst-prone mines

Jian Zhou et al.

TRANSACTIONS OF NONFERROUS METALS SOCIETY OF CHINA (2016)

Article Metallurgy & Metallurgical Engineering

Prediction of rock burst classification using cloud model with entropy weight

Ke-ping Zhou et al.

TRANSACTIONS OF NONFERROUS METALS SOCIETY OF CHINA (2016)

Article Engineering, Geological

Knowledge-based and data-driven fuzzy modeling for rockburst prediction

Amoussou Coffi Adoko et al.

INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES (2013)

Article Geosciences, Multidisciplinary

Prediction of rock burst classification using the technique of cloud models with attribution weight

Zaobao Liu et al.

NATURAL HAZARDS (2013)

Article Metallurgy & Metallurgical Engineering

Prediction of rockburst classification using Random Forest

Long-jun Dong et al.

TRANSACTIONS OF NONFERROUS METALS SOCIETY OF CHINA (2013)

Article Construction & Building Technology

Numerical simulation on rockburst of underground opening triggered by dynamic disturbance

W. C. Zhu et al.

TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY (2010)