为解决传统技术难以准确监测水下隧道接缝三维变形的难题，结合分布式光纤应变传感技术和人工智能技术机器学习算法提出隧道接缝三维变形感知新方法。首先，利用分布式光纤应变传感技术，设计隧道接缝变形光纤监测的方法，依据光纤应变与变形量的理论关系，建立隧道接缝三维变形计算模型，得到12组数据。其中，每组共有4 000个隧道接缝变形量与光纤应变的数据。然后，提出基于决策树、随机森林和支持向量机的两级递进机器学习分类新算法，实现对隧道接缝三维变形量的准确计算，隧道接缝变形的计算精确度可达0.1 mm。研究发现，相比于决策树和随机森林2种机器学习算法，支持向量机对隧道接缝三维变形计算具有较高的准确性和稳定性。
It is difficult to accurately monitor the threedimensional
deformation of joints in underwater tunnels based on traditional methods. A new
threedimensional deformation sensing method for tunnel
joints is proposed based on distributed fiber optic strain sensing technology
and artificial intelligence machine learning algorithm. Firstly, a monitoring
method of joint deformation of tunnel is designed by using distributed optical
fiber strain sensor. According to the theoretical relationship between the
optical fiber strain and the joint deformation, a threedimensional deformation model of tunnel joints is established, and
12 sets of data are obtained. There are 4 000 data of tunnel joint deformation
and optical fiber strain in each set. Then, a new twolevel
progressive classification algorithm based on machine learning methods,
including decision tree, random forest, and support vector machine, is proposed
to accurately calculate the threedimensional
deformation of tunnel joints. The accuracy of tunnel joint deformation can
reach 0.1 mm. It is found that the support vector machine has higher accuracy
and stability for threedimensional deformation of
tunnel joints compared with the machine learning algorithms of decision tree
and random forest.
