| 229 | 0 | 73 |
| 下载次数 | 被引频次 | 阅读次数 |
岩爆灾害已成为制约深部工程安全建设的关键因素。针对深埋全断面隧道掘进机（tunnel boring machine,TBM）隧道岩爆影响因素复杂、量化困难、指标权重自适应性弱、模糊评价隶属度随机性不足等问题，建立了一种深埋TBM隧道岩爆预警方法。首先，基于多个深埋TBM隧道工程案例建立数据库；接着，运用相关系数法、互信息法和ReliefF算法等过滤式方法（filter method,FM）特征选择技术，筛选并构建了岩爆多源预警指标体系；然后，提出了基于相关性理论的权重自适应调整与动态更新机制，并结合云模型（cloud model,CM）和模糊综合评价（fuzzy comprehensive evaluation,FCE）理论，建立了模糊隶属云模型；最后，将该方法应用于中国西部某深埋TBM隧道，以验证其准确性。结果表明，该预警方法能显著提升岩爆风险预测准确率，可为深埋TBM隧道施工安全提供技术支撑。
Abstract：Rockburst disasters have become a critical factor hindering the safe construction of deep underground projects. To address the complex factors affecting rockbursts in deeply buried tunnel boring machine(TBM) tunnels, the challenges of quantification, weak adaptability of index weights, and insufficient randomness in fuzzy evaluation membership degrees, an early warning method for rockbursts in deeply buried TBM tunnels was developed. Firstly, a database was established based on multiple cases of deeply buried TBM tunnel projects. Then, using filter methods feature selection techniques including correlation coefficient methods, mutual information methods, and ReliefF, a multi-source rockburst early warning index system was screened and constructed. Next, an adaptive weight adjustment strategy and dynamic update mechanism based on correlation theory were proposed, and a fuzzy membership cloud model was established by combining the cloud model(CM) and fuzzy comprehensive evaluation(FCE) theory. Finally, the method was applied in a deeply buried TBM tunnel in western China to verify its accuracy. The results show that the early warning method can significantly improve the accuracy of rockburst risk prediction, providing reliable technical support for the safe construction of deeply buried TBM tunnels.
[1]冯夏庭，肖亚勋，丰光亮，等.岩爆孕育过程研究[J].岩石力学与工程学报，2019, 38(4):649-673.FENG X T, XIAO Y X, FENG G L, et al. Study on the development process of rockbursts[J]. Chinese Journal of Rock Mechanics and Engineering, 2019, 38(4):649-673.
[2]陈炳瑞，冯夏庭，明华军，等.深埋隧洞岩爆孕育规律与机制：时滞型岩爆[J].岩石力学与工程学报，2012, 31(3):561-569.CHEN B R, FENG X T, MING H J, et al. Evolution law and mechanism of rockburst in deep tunnel:Time delayed rockburst[J]. Chinese Journal of Rock Mechanics and Engineering,2012, 31(3):561-569.
[3] FENG G L, MA J G, CHEN B R, et al. Microseismic energy and intensity criterion of rockburst in deep TBM tunnels:A case study of the Neelum-Jhelum hydropower project[J]. Journal of Central South University, 2023,30(5):1695-1709.
[4] YIN X, CHENG S, YU H, et al. Probabilistic assessment of rockburst risk in TBM-excavated tunnels with multi-source data fusion[J]. Tunnelling and Underground Space Technology,2024, 152:105915.
[5] ZHAO H, CHEN B, ZHU C. Decision tree model for rockburst prediction based on microseismic monitoring[J].Advances in Civil Engineering, 2021, 2021:8818052.
[6] ZHANG Y, FENG X T, YAO Z, et al. Study on warning method for fault rockburst in deep TBM tunnels[J]. Rock Mechanics and Rock Engineering, 2024, 57(8):5557-5574.
[7] LI D, LIU Z, ARMAGHANI D J, et al. Novel ensemble intelligence methodologies for rockburst assessment in complex and variable environments[J]. Scientific Reports, 2022, 12:1844.
[8] ZHOU J, GUO H, KOOPIALIPOOR M, et al. Investigating the effective parameters on the risk levels of rockburst phenomena by developing a hybrid heuristic algorithm[J].Engineering with Computers, 2021, 37(3):1679-1694.
[9]杨玲，魏静.基于支持向量机和增强学习算法的岩爆烈度等级预测[J].地球科学，2023, 48(5):2011-2023.YANG L, WEI J. Prediction of rockburst intensity grade based on SVM and adaptive boosting algorithm[J]. Earth Science,2023, 48(5):2011-2023.
[10]LIANG W, ZHAO G, WU H, et al. Risk assessment of rockburst via an extended MABAC method under fuzzy environment[J]. Tunnelling and Underground Space Technology, 2019, 83:533-544.
[11]裴启涛，李海波，刘亚群，等.基于改进的灰评估模型在岩爆中的预测研究[J].岩石力学与工程学报，2013,32(10):2088-2093.PEI Q T, LI H B, LIU Y Q, et al. Rockburst prediction based on a modified grey evaluation model[J]. Chinese Journal of Rock Mechanics and Engineering, 2013, 32(10):2088-2093.
[12]LIU R, YE Y, HU N, et al. Classified prediction model of rockburst using rough sets-normal cloud[J]. Neural Computing and Applications, 2019, 31(12):8185-8193.
[13]ZHANG L, ZHANG X, WU J, et al. Rockburst prediction model based on comprehensive weight and extension methods and its engineering application[J]. Bulletin of Engineering Geology and the Environment, 2020, 79(9):4891-4903.
[14]HE S, SONG D, MITRI H, et al. Integrated rockburst early warning model based on fuzzy comprehensive evaluation method[J]. International Journal of Rock Mechanics and Mining Sciences, 2021, 142:104767.
[15]CAI W, DOU L, ZHANG M, et al. A fuzzy comprehensive evaluation methodology for rock burst forecasting using microseismic monitoring[J]. Tunnelling and Underground Space Technology, 2018, 80:232-245.
[16]ZHOU X, ZHANG G, SONG Y, et al. Evaluation of rock burst intensity based on annular grey target decision-making model with variable weight[J]. Arabian Journal of Geosciences, 2019, 12(2):43.
[17]MIAO S J, CAI M F, GUO Q F, et al. Rock burst prediction based on in situ stress and energy accumulation theory[J].International Journal of Rock Mechanics and Mining Sciences,2016, 83:86-94.
[18]LIU X, WANG G, SONG L, et al. A new rockburst criterion of stress-strength ratio considering stress distribution of surrounding rock[J]. Bulletin of Engineering Geology and the Environment, 2022, 82(1):29.
[19]FENG G L, FENG X T, CHEN B R, et al. A microseismic method for dynamic warning of rockburst development processes in tunnels[J]. Rock Mechanics and Rock Engineering, 2015, 48(5):2061-2076.
[20]李天斌，许韦豪，马春驰，等.基于深度学习的隧道微震监测及岩爆预警技术与系统研究[J].岩石力学与工程学报，2024,43(5):1041-1063.LI T B, XU W H, MA C C, et al. Research of technology and system of tunnel microseismic monitoring and rockburst early warning based on deep learning[J]. Chinese Journal of Rock Mechanics and Engineering, 2024, 43(5):1041-1063.
[21]VENKATESH B, ANURADHA J. A review of feature selection and its methods[J]. Cybernetics and Information Technologies, 2019, 19(1):3-26.
[22]BENNASAR M, HICKS Y, SETCHI R. Feature selection using joint mutual information maximisation[J]. Expert Systems with Applications, 2015, 42(22):8520-8532.
[23]KIRA K, RENDELL L A. A practical approach to feature selection[C]//Proceedings of the Ninth International Workshop on Machine Learning. San Francisco,USA:Morgan Kaufmann Publishers Inc., 1992:249-256.
[24]KONONENKO I. Estimating attributes:Analysis and extensions of RELIEF[C]//Machine Learning:ECML-94.Berlin:Springer, 1994:171-182.
[25]ZHOU J, LI X, MITRI H S. Evaluation method of rockburst:State-of-the-art literature review[J]. Tunnelling and Underground Space Technology, 2018, 81:632-659.
[26]PAN J, FENG M, LU Z, et al. Research and application of comprehensive monitoring and early warning platform for coal mine rock burst[J]. Coal Science and Technology, 2021, 49(6):32-41.
[27]MAN SINGH BASNET P, MAHTAB S, JIN A. A comprehensive review of intelligent machine learning based predicting methods in long-term and short-term rock burst prediction[J]. Tunnelling and Underground Space Technology, 2023, 142:105434.
[28]SAATY T L. Decision making—The analytic hierarchy and network processes(AHP/ANP)[J]. Journal of Systems Science and Systems Engineering, 2004, 13(1):1-35.
[29]JU J, SHI W, WANG Y. A risk assessment approach for road collapse along tunnels based on an improved entropy weight method and K-means cluster algorithm[J]. Ain Shams Engineering Journal, 2024, 15(7):102805.
[30]沈进昌，杜树新，罗祎，等.基于云模型的模糊综合评价方法及应用[J].模糊系统与数学，2012, 26(6):115-123.SHEN J C, DU S X, LUO Y, et al. Method and application research on fuzzy comprehensive evaluation based on cloud model[J]. Fuzzy Systems and Mathematics, 2012, 26(6):115-123.
[31]WANG X, LI S, XU Z, et al. An interval fuzzy comprehensive assessment method for rock burst in underground caverns and its engineering application[J]. Bulletin of Engineering Geology and the Environment, 2019, 78(7):5161-5176.
[32]ZHANG G, LIU G, LU Z, et al. Evaluation method for health state of highway tunnel structure based on adaptive comprehensive weighting[J]. Engineering Failure Analysis,2024, 163:108597.
[33]李德毅，孟海军，史雪梅.隶属云和隶属云发生器[J].计算机研究与发展，1995, 32(6):15-20.LI D Y, MENG H J, SHI X M. Membership clouds and membership cloud generators[J]. Journal of Computer Research and Development, 1995, 32(6):15-20.
基本信息:
DOI：10.13578/j.cnki.issn.1671-1556.20250424
中图分类号:U455.31
引用信息:
[1]肖华波,赵龙翔,陈靖文,等.基于FM-CM-FCE的深埋TBM隧道岩爆预警方法[J].安全与环境工程,2025,32(05):66-79.DOI:10.13578/j.cnki.issn.1671-1556.20250424.
基金信息:
国家自然科学基金项目（42177168）; 湖北省杰出青年基金项目（2024AFA068）