融合机器视觉与性能分析的运营盾构隧道结构安全状态评价

Safety State Evaluation of Operational Shield Tunnel Structures by Integrating Computer Vision and Performance Analysis

在地铁盾构隧道运维过程中,隧道结构变形和衬砌表观病害是常见的两大类结构安全状态评价指标.为了平衡专家评价的主观性和物理力学模型的客观性,提出了融合机器视觉结构病害检测信息和不同病害下结构性能分析的隧道安全状态评价方法,基于隧道工程专家对于不同分类病害权重比例的评估,同时综合了位置、面积、体积等细化指标对于各项病害的加权影响.该评价方法以移动激光扫描获取的三维点云作为数据基础,通过椭圆拟合计算出盾构隧道横断面的收敛变形值、椭圆度以及环间错台值,采用提出的深度学习模型对衬砌表观的渗漏水和剥落病害进行自动化识别和量化,基于有限元数值模拟分析量化了渗漏水以及剥落病害在不同位置的安全状态权重,并通过信息熵法确定了横向收敛变形以及椭圆度两种病害的权重,得到了隧道结构安全状态评价公式.最后采用无监督机器学习Kmeans++聚类算法得到了安全状态分级的阈值,现场实例验证结果表明,提出的评价方法在效率、客观、全面性等方面均体现了一定的优越性,能够为盾构隧道维保部门制定运维决策提供参考.

In the operation and maintenance of subway shield tunnels,deformation of tunnel structures and apparent lining damage are common indicators for evaluating structural safety states.To balance the subjectivity of expert assessments and the objectivity of physical-mechanical models,a tunnel safety state evaluation method was proposed,integrated computer vision-based structural damage detection information with structural performance analysis.The expert assessment of the weight ratios for different categorized damages was considered,and the weighted influence of refined indicators such as location,area,and volume on various types of damage also were incorporated.3D point clouds obtained from mobile laser scanning is used as the data foundation.The convergence deformation value,ellipticity and dislocation value of the shield tunnel section are calculated through ellipse fitting,and a deep learning model is used to automatically identify and quantify obvious lining leakage and spalling damage.The finite element numerical model was used to quantitatively analyze the safety state weights of leakage and spalling damage at different locations.The information entropy method was used to determine the weights of the two types of damage,lateral convergence deformation and ellipticity,and the tunnel structure safety status evaluation formula was obtained.The unsupervised machine learning method Kmeans++clustering algorithm is used to determine the threshold for safety status classification.Field example verification results show that the evaluation method proposed in this article has advantages in efficiency,objectivity and comprehensiveness,and can provide a reference for maintenance and operation decisions of the shield tunnel department.