多层岩土环境的溶洞智能识别方法研究

INTELLIGENT RECOGNITION METHOD OF KARST CAVES IN MULTI-LAYER ROCK–SOIL ENVIRONMENT

  • 摘要: 提出了一种基于双向长短期记忆(bidirectional long short-term memory, BiLSTM) 神经网络的多层岩土环境溶洞三维定量智能识别方法。首先,借鉴浅层地震反射波法原理,建立含有单个无填充球形孔洞的多层岩土结构模型,并计算桩锤激振下地表的加速度响应信号;其次,针对不同溶洞工况大量建模,以获取不同工况下的响应信号作为数据集;最后,基于BiLSTM设计了双数据通道分离架构网络模型,实现了对多层岩土环境下不同溶洞工况的定量识别。研究表明,本文所提出方法能够对不同岩土体结构下溶洞的三维位置和直径进行定量识别,且在3 m容差范围内的识别准确率达到了98%以上。

     

    Abstract: This paper proposes a three-dimensional quantitative intelligent recognition method for caves in multi-layer rock–soil environment based on bidirectional long short-term memory (BiLSTM) neural network. Firstly, referring to the principle of shallow seismic reflection wave method, a multi-layer rock–soil structure model containing a single unfilled spherical cavity is established, and the acceleration response signals of the ground surface under pile hammer excitation are calculated. Secondly, the response signals under different working conditions are obtained as data sets by lots of simulation models for different working conditions. Finally, a dual channel separated architecture network model is designed based on BiLSTM, which realizes the quantitative identification of different karst cave working conditions in multi-layer rock–soil environment. The results show that the method proposed in this paper can quantitatively identify the three-dimensional position and diameter of caves under different rock-soil structures, and the identification accuracy within the tolerance error of 3 m has reached more than 98%.

     

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