隧道二衬脱空声振检测试验研究

窦顺1,贺磊2,郑静1,王念秦2

铁道工程学报 ›› 2017, Vol. 34 ›› Issue (7) : 66-71.

PDF(623 KB)
PDF(623 KB)
铁道工程学报 ›› 2017, Vol. 34 ›› Issue (7) : 66-71.
隧道工程

隧道二衬脱空声振检测试验研究

  • 窦顺1,贺磊2,郑静1,王念秦2
作者信息 +

Experimental Research on the Acoustic-vibration Detection of Tunnel Secondary Liner Void

  • DOU Shun1, HE Lei2, ZHENG Jing1, WANG Nian-qin2
Author information +
文章历史 +

摘要

摘要:研究目的:隧道二衬脱空严重威胁线路的安全运营,及时开展检测、预防工作尤为重要。为解 决现有检测方法中存在的问题,降低检测成本,本文进行声振检测试验研究,通过对响应信号进行频 谱转换、特征值提取、神经网络拟合以得到初步的脱空判定标准。研究结论:(1)严重脱空信号的低频成分(1 000 Hz左右)突出,密实信号的高频成分(8 000 Hz左右)突出, 轻微脱空信号表现为比例相近的低、高频成分均匀分布,曲线为多峰值形态;(2)主峰值频率、次峰 值频率及峰值下降率可作为信号特征值,全面反映二衬结构状态;(3)将特征值与对应二衬结构状态 作为输入、输出值进行BP神经网络训练,并对网络进行测试,预测值的均方误差较小(mse=6.475e-5 ),网络具有较好的预测能力,能够初步实现对二衬结构状态的定性识别判定;(4)该研究成果可为 声振法在隧道二衬脱空检测中的工程运用提供理论依据。

Abstract

Abstract:Research purposes: The void of tunnel secondary liner threats the safe operation of road seriously, timely detection and prevention work is particularly significant. In order to solve the problems in existing detection methods, reduce the detection cost, this paper conducts the experimental research of acoustic-vibration detection, through the frequency conversion, extraction of eigenvalues and the neural network fitting of the response signal, the preliminary criterion for the void determination is obtained.  Research conclusions:(1)Low frequency component (about 1 000 Hz) of serious void signal is prominent, high frequency component (about 8 000 Hz) of no void signal is prominent, the slight void signal shows uniform distribution of low and high frequency components in similar proportion, and the curve is in a multi peak shape.(2)The main peak frequency, secondary peak frequency and decline rate of peaks can be regarded as the signal eigenvalues, and reflect the secondary liner structure state in the round.(3)Taking the eigenvalues and the corresponding secondary liner structure state as the input and output value for BP neural network training, through testing the network, the mean square error of predictive value is small (mse=6.475e-5), which means the network has a good prediction ability and can realize the qualitative identification and determination of secondary liner structure state initially.(4)The research results can provide theoretical basis for engineering application of acoustic-vibration method in tunnel secondary liner void detection.

关键词

关键词:隧道二衬脱空 / 声振检测法 / 神经网络判定

Key words

Key words: void of tunnel secondary liner / acoustic-vibration detection method / neural network judgement

引用本文

导出引用
窦顺1, 贺磊2, 郑静1, . 隧道二衬脱空声振检测试验研究[J]. 铁道工程学报, 2017, 34(7): 66-71
DOU Shun1, HE Lei2, ZHENG Jing1, et al. Experimental Research on the Acoustic-vibration Detection of Tunnel Secondary Liner Void[J]. Journal of Railway Engineering Society, 2017, 34(7): 66-71

PDF(623 KB)

6336

Accesses

0

Citation

Detail

段落导航
相关文章

/