谢雄耀, 覃晖. 2010: 探地雷达探测隧道衬砌钢筋的神经网络识别方法. 工程地质学报, 18(S1): 229-233.
    引用本文: 谢雄耀, 覃晖. 2010: 探地雷达探测隧道衬砌钢筋的神经网络识别方法. 工程地质学报, 18(S1): 229-233.
    XIE Xiongyao, QIN Hui. 2010: RECOGNITION OF STEEL REINFORCEMENT IN TUNNEL LINING USING GROUND PENETRATING RADAR AND ARTIFICIAL NEURAL NETWORKS. JOURNAL OF ENGINEERING GEOLOGY, 18(S1): 229-233.
    Citation: XIE Xiongyao, QIN Hui. 2010: RECOGNITION OF STEEL REINFORCEMENT IN TUNNEL LINING USING GROUND PENETRATING RADAR AND ARTIFICIAL NEURAL NETWORKS. JOURNAL OF ENGINEERING GEOLOGY, 18(S1): 229-233.

    探地雷达探测隧道衬砌钢筋的神经网络识别方法

    RECOGNITION OF STEEL REINFORCEMENT IN TUNNEL LINING USING GROUND PENETRATING RADAR AND ARTIFICIAL NEURAL NETWORKS

    • 摘要: 针对目前隧道无损检测中探地雷达图像解释困难的问题,建立山区高速公路隧道衬砌模型,通过模型试验模拟探地雷达沿隧道纵向对二衬内钢筋进行探测,并获得探测数据。采用去直达波、Dewow处理、背景消除和修剪数据矩阵等方法对原始数据进行预处理。将预处理后数据矩阵按重叠率50%分块,并将分块矩阵作为样本数据送入已建立的BP神经网络进行训练与测试,实现神经网络对探测结果的自动识别。试验表明,该方法能够有效地对二衬内表层钢筋进行识别,但对于深层钢筋的识别有所欠缺。

       

      Abstract: Based on the fact that the explanation of image acquired from Ground Penetrating Radar(GPR)is far from an easy job,a model test was carried out.First a physical model of mountain highway tunnel lining was built.Then a test was carried out using ground penetrating radar to simulate detecting steel reinforcement that embedded in final lining along the tunnel longitudinal direction.After data acquisition stage,a preprocessing procedure including Removing DC Component,Dewowing,Removing Global Background and Trimming Data Matrix was implemented on the raw data,in order to improve signal-to-noise ratio.Data that after preprocessing was divided into block matrices with an overlapping rate of 50 percent to make data proper for Artificial Neural Networks.Then they were used for training and testing the already established BP network.The test indicated that the first layer of steel reinforcement in the second tunnel lining can be effectively recognized with this method.

       

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