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大直徑盾構隧道成型質量巡檢方法研究

Molding quality inspection method for large-diameter shield tunnels

  • 摘要: 針對因工業應用成本限制,中、小盾構隧道成型質量無損檢測技術遷移至大直徑盾構隧道時精度、速度折損嚴重的問題,以巡檢車為載體,集成二維激光掃描儀、編碼器和計算機等設備,研制了大盾構隧道成型質量巡檢車,并提出一種基于數字圖像的盾構質量非對稱巡檢方法. 分析大直徑盾構的施工環境,濾除地面、車體點云,并采用鄰域向量法提取中軸線,建立隧道中心坐標系. 偏心布置巡檢路線,按照點云密度將采樣點云分為稠密側和稀疏側點云,通過不同方法實現對管片接縫特征的拾取:將稠密側點云繞中軸線展開為二維灰度圖像,并通過縮放、歸一化、梯度閾值分割等方法實現接縫圖像分割;基于直線方程對接縫進行分類,結合管片結構、布置點位,推導出稀疏側接縫與稠密側接縫的線性分布公式,間接拾取稀疏側接縫. 根據接縫特征點計算兩側管片邊緣點云簇,計算管片錯臺量;剔除接縫點云簇,使用最小二乘法擬合隧道點云,計算隧道橢圓度. 最后在某大直徑盾構隧道進行巡檢試驗,試驗結果表明:成型質量巡檢車在十四米盾構隧道中巡檢速度為3 km·h?1,與傳統方法的錯臺量檢測偏差小于2 mm,橢圓度檢測偏差小于0.1%,可以滿足大直徑盾構隧道成型質量巡檢的高速度、高精度、低成本需求.

     

    Abstract: With the development of tunnel construction, the detection, control, and treatment of all kinds of tunnel lining diseases have received increased attention. Therefore, tunnel nondestructive testing technology is widely used as an intelligent emerging technology. Due to the cost limitations of industrial applications, nondestructive testing technology of medium- and small-shield tunnels experiences serious losses in accuracy and speed when transferred to large-diameter shield tunnels. A large shield tunnel forming quality inspection vehicle is developed by integrating a two-dimensional (2D) laser scanner with a wheel shaft encoder and an industrial computer. Based on this equipment, an asymmetric shield quality inspection method based on digital images is proposed. By analyzing the construction environment of a large-diameter shield tunnel, the ground and vehicle body point clouds are filtered, the central axis is extracted using the neighborhood vector method, and the central coordinate system of the tunnel is established. The inspection route is arranged eccentrically, and the sampling point clouds are divided into dense and sparse side point clouds according to their tunnel density. Moreover, different methods are used to pick up the joint features of the segment. The dense side point cloud is expanded around the central axis into a 2D gray image, and the joint image is segmented by scaling, normalization, and gradient threshold segmentation. According to the classification of joints based on the linear equation, the linear distribution formula of sparse and dense side joints is deduced by combining the segment structure and the point placement, with the sparse side joints picked up indirectly. According to the joint characteristic points, the edge point clusters on both sides of the segment and the segment misalignment are calculated. The least square method is used to fit the tunnel point cloud and calculate tunnel ellipticity. Tests are conducted in a large-diameter shield tunnel to verify the effectiveness of the inspection method, and the following conclusions are made from the test results: The inspection speed of the molding quality inspection vehicle in the 14-meter shield tunnel is 3 km·h?1; compared with traditional detection methods, the average deviation of segment dislocation detection is less than 2 mm; the average deviation of tunnel contour maximum deformation detection is less than 2 mm, and the average deviation of ovality detection is less than 0.1%. This meets the high-speed, high-precision, and low-cost requirements of large-diameter shield tunnel molding quality inspections.

     

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