Classification Method for Railway Tunnel Secondary Lining Cold Joint Detection based on CNN-BiLSTM-SVM Model with Improved Hybrid Leader Algorithm

Frontiers in Computing and Intelligent Systems Pub Date : 2023-11-27 DOI:10.54097/fcis.v6i1.05

Honggu Zhu, Jiaye Wu

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Abstract

Cold joints pose great safety risks to the safe operation of railways. In view of the existing cold joint detection methods, which have low detection efficiency and difficulty in data analysis, a tunnel secondary lining cold joint detection classification method based on the improved hybrid leader CNN-BiLSTM-SVM model is proposed. First, the Rayleigh wave method is used to extract the waveform information of the cold joints. Secondly, CNN-BILSTM is used to perform feature extraction and fusion processing on the waveform information and then input into the support vector machine, and the improved hybrid leader algorithm is used to optimize the parameters in the SVM. Finally, the information is input into the optimized CNN-BiLSTM-SVM to obtain the cold joints detection classification results. In order to verify the effectiveness of this method, the waveform data collected using the Rayleigh wave method in the tunnel under construction and the verified coring detection results are used as the data set. The results show that the results of this method are higher than the unoptimized CNN-BILSTM-SVM and the CNN-BILSTM-SVM optimized by the seagull optimization algorithm and the sparrow search optimization algorithm.

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基于 CNN-BiLSTM-SVM 模型与改进型混合先导算法的铁路隧道二次衬砌冷接缝检测分类方法

冷缝给铁路的安全运营带来了极大的安全隐患。针对现有冷缝检测方法检测效率低、数据分析困难等问题，提出了一种基于改进型混合领导者 CNN-BiLSTM-SVM 模型的隧道二次衬砌冷缝检测分类方法。首先，利用瑞利波方法提取冷缝的波形信息。其次，使用 CNN-BILSTM 对波形信息进行特征提取和融合处理，然后输入支持向量机，并使用改进的混合领导者算法优化 SVM 中的参数。最后，将信息输入优化后的 CNN-BiLSTM-SVM，得到冷接头检测分类结果。为了验证该方法的有效性，使用了在建隧道中使用瑞利波方法采集的波形数据和经过验证的取芯检测结果作为数据集。结果表明，该方法的结果高于未优化的 CNN-BILSTM-SVM，以及通过海鸥优化算法和麻雀搜索优化算法优化的 CNN-BILSTM-SVM。

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Frontiers in Computing and Intelligent Systems

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