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基于大样本不完整数据的岩爆致因特征及预测模型
刘国锋, 杜程浩, 丰光亮, 晏长根, 李胜峰, 徐鼎平
PDF(2962 KB)
PDF(2962 KB)
基于大样本不完整数据的岩爆致因特征及预测模型
Causative Characteristics and Prediction Model of Rockburst Based on Large and Incomplete Data Set
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为判别影响岩爆的敏感性因素并构建不完整数据条件下的岩爆预测方法，在收集到429组国内外岩爆案例的基础上建立大样本数据库，归纳总结岩爆致因分布特征及规律，选取埋深、岩石单轴抗压强度、岩石单轴抗拉强度、围岩最大切向应力、弹性应变能量指数、岩体完整性系数6个评价指标，利用贝叶斯网络建立基于大样本不完整数据集的岩爆概率预测模型，并进行敏感性分析和工程应用.分析发现围岩最大切向应力与岩体完整性系数对岩爆的影响较大，所建模型对信息缺失率为20%的岩爆案例预测吻合率达83.3%，且预测效果优于常用岩爆经验判据.结果表明所选取的预测指标能够综合考虑岩爆的影响因素，所建立模型对于深部岩爆灾害的预测具有适用性和可靠性.
In order to distinguish the sensitivity factors affecting rockburst and construct a rockburst prediction method under the condition of incomplete data cases, a large sample database is established on the basis of collecting 429 groups of rockburst cases at home and abroad, and the distribution characteristics and regulation of rockburst disaster-inducing factors were summarized. Six evaluation indexes, including buried depth, uniaxial compressive strength of rock, uniaxial tensile strength of rock, maximum tangential stress of surrounding rock, rock elastic energy index and integrity coefficient of rock mass, are selected to establish a rockburst probability prediction model based on large and incomplete data set by using Bayesian network, and the sensitivity analysis and engineering application are carried out. Through analysis, it is found that the maximum tangential stress of surrounding rock and the integrity coefficient of rock mass have a great influence on rockburst. The model has a prediction coincidence rate of 83.3% for rockburst cases with information loss rate of 20%, and the prediction effect is better than the commonly used empirical criterion of rockburst. The results show that the prediction indexes selected in this paper can comprehensively consider the influencing factors of rockburst, and the established model has applicability and reliability for the prediction of deep rockburst disasters.
岩爆 / 致灾因素 / 敏感性分析 / 概率预测 / 不完整数据集 / 灾害地质
rockburst / disaster-inducing factor / sensitivity analysis / probabilistic prediction / incomplete data set / hazard geology
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