| 软岩隧道大变形预警分级研究及发展趋势 |
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| 引用本文： | 李晓斌.软岩隧道大变形预警分级研究及发展趋势[J].地质与勘探,2021,57(5):1149-1157. |
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| 作者姓名： | 李晓斌 |
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| 作者单位： | 陕西铁路工程职业技术学院,陕西渭南714000 |
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| 摘 要： | 为实现隧道大变形段的准确预警分级及发展趋势评价,本研究以隧道大变形监测成果为基础,先利用多种评价判据构建隧道大变形预警分级模型,并利用优化长短时记忆神经网络构建大变形预测模型,以实现现状预警分级和发展趋势评价的综合研究。实例分析表明:不同判据评价得到的大变形预警等级存在一定差异,按不利原则,得到大变形段的预警等级为Ⅳ级-红色,应闪光、声音警示,并暂停施工、商讨对策,以切实保证施工安全。同时,通过大变形预测,得到其预测结果的平均相对误差均小于2%,验证了该文预测模型的有效性,且外推预测得出隧道变形仍会持续增加,并无收敛趋势,建议加快大变形加固处理,以避免造成塌方灾害。
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| 关 键 词： | 隧道 大变形 预警等级 变形预测 发展趋势 |
| 收稿时间： | 2020/9/15 0:00:00 |
| 修稿时间： | 2021/4/20 0:00:00 |
Early warning classification of large deformation in soft rock tunnels and its development trend |
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| Li Xiaobin.Early warning classification of large deformation in soft rock tunnels and its development trend[J].Geology and Prospecting,2021,57(5):1149-1157. |
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| Authors: | Li Xiaobin |
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| Institution: | Shaanxi Railway Institute,Weinan, Shaanxi |
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| Abstract: | The purpose of this paper is to help realize the accurate classification of early warning classification of large deformation sections in tunnels and discuss its development trend. Based on monitoring results of large deformation of tunnels, firstly a variety of evaluation criteria are used to construct an early warning classification model of tunnel large deformation. Then the optimized long-term and short-term memory neural network is employed to construct a large deformation prediction model, so as to facilitate the comprehensive research of the current situation of early warning classification and development trend evaluation. The case analysis shows that there are certain differences in the early warning levels of large deformation evaluated by different criteria. According to the unfavorable principle, the warning level of large deformation sections is grade IV - red, which should flash and sound warning, suspend construction and discuss countermeasures to ensure construction safety. At the same time, large deformation prediction is performed with the average relative error less than 2%, which verifies the effectiveness of the prediction model. Besides the extrapolation prediction shows that the tunnel deformation will continue to increase without a convergence trend. Thus it is suggested to speed up the large deformation reinforcement to avoid the possible collapse. |
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| Keywords: | tunnel large deformation early warning level deformation prediction development trend |
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