Forecasting of rockbursts in deep underground engineering based on abstraction ant colony clustering algorithm
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DOI: 10.1007/s11069-014-1561-1
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Cited by:
- Guangliang Feng & Guoqing Xia & Bingrui Chen & Yaxun Xiao & Ruichen Zhou, 2019. "A Method for Rockburst Prediction in the Deep Tunnels of Hydropower Stations Based on the Monitored Microseismicity and an Optimized Probabilistic Neural Network Model," Sustainability, MDPI, vol. 11(11), pages 1-17, June.
- Hongbo Zhang & Nan Li & Wengang Zhang & Xiaofang Pei, 2016. "Experiments to automatically monitor drought variation using simulated annealing algorithm," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 84(1), pages 175-184, October.
- Weizhang Liang & Asli Sari & Guoyan Zhao & Stephen D. McKinnon & Hao Wu, 2020. "Short-term rockburst risk prediction using ensemble learning methods," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 104(2), pages 1923-1946, November.
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Keywords
Rockburst; Forecasting; Deep underground engineering; Clustering; Abstraction ant colony clustering algorithm;All these keywords.
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