التفاصيل البيبلوغرافية
| العنوان: |
多源数据融合的深埋隧道岩爆预测方法. (Chinese) |
| Alternate Title: |
Prediction method of rockburst in deep buried tunnel based on multi-source data fusion. (English) |
| المؤلفون: |
张平, 任松, 吴斐, 刘跃, 陈星宇 |
| المصدر: |
Journal of Southeast University / Dongnan Daxue Xuebao; May2024, Vol. 54 Issue 3, p707-716, 10p |
| Abstract (English): |
To improve the accuracy of rockburst prediction in unexcavated areas during tunnel design, a multi・ source data fusion method for tunnel rockburst prediction was proposed. By integrating different geological formation from tunnel survey and construction stages, a refined dynamic three -dimensional geological model of the tunnel was established using weighted fusion technology based on evidence correlation coefficients. One method for determining the likelihood of rockburst based on the strength theory and another for predicting the intensity of rockburst based on the energy theory were proposed. The Hoek・Brown strength criterion was used to determine whether rockburst occurred in the surrounding rock, and the energy storage limit threshold and energy release index were applied to classify the rockburst intensity level. The rockburst prediction method was applied to a tunnel in Sichuan. The results show that the proposed method can accurately predict the local rockburst within 30 m in front of the tunnel face. The prediction results and the tunnel excavation of the actual rockburst coincide with the rate of 95. 8 %. Therefore, this prediction method can predict the intensity of rockburst before tunnel construction and provide guidance for the prevention and control of rockbursts in tunnels. [ABSTRACT FROM AUTHOR] |
| Abstract (Chinese): |
为提高隧道设计阶段未开挖区域的岩爆预测准确性, 提出了一种多源数据融合的隧道岩爆预测方 法.综合隧道勘察和施工阶段的不同地质信息, 采用基于证据关联系数的加权融合技术, 构建了隧道精细 化三维动态地质模型.建立了基于强度理论的岩爆可能性判定方法和基于能量理论的岩爆烈度预测方法, 通过Hoek-Brown强度准则判断围岩是否发生岩爆,利用储能极限阈值和能量释放指数划分岩爆烈度, 并 将其应用于四川某隧道工程中.结果表明, 所提方法可以实现隧道掌子面前方30 m范围内的岩爆精准预 测, 预测结果与隧道开挖实际岩爆的吻合率为95.8%.因此, 该预测方法能够在隧道施工前预判岩爆烈 度, 为隧道岩爆防治提供指导. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |