Fully distributed optical fiber sensing technology allows the high-density strain to measure the overall curvature and cross-section deformation of tunnels. However, there are few studies on the use of longitudinal strain along the tunnel to measure the cross-section convergence deformation, and the method of obtaining the strain along the tunnel loop is costly. To address this issue, a method of monitoring the cross-section deformation of tunnels using the strain data is proposed. First, a model of the relationship between strain and deformation in tunnels is constructed to obtain the overall settlement using the longitudinal strain. Second, based on the finite element method (FEM), the deformation law about the strain measured points and non-measured points on the cross-section of the tunnel is proposed, and on this basis, the correlation coefficient is presented. Using the product of overall settlement and correlation coefficient, the cross-section deformation at non-measured points is obtained. The results of numerical examples shown that the proposed method can effectively expand the monitoring scale and realize high-density cross-section deformation measurement of tunnels.
We propose a new method to combine the Knn algorithm with the neural network. Due to the size of the training set affects the accuracy of the training results, we propose a neural network with selective training set. Its main method is changed the size of the training set by a control variable to get the most suitable training data. The advantage of this method is that the training set can be adjusted to achieve a trade-off between the positioning accuracy and the training set size. The result of Knn algorithm positioning is more accurate by adding BP (Back Propagation) selective neural network algorithm.
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