A machine learning method for inclinometer lateral deflection calculation based on distributed strain sensing technology

Abstract

Due to its unique advantages, the distributed fiber optical sensing (DFOS) technology has been used to study the performance of inclinometer so as to monitor landslide deformation. Strain distribution of inclinometer can be obtained by distributed strain sensing (DSS) cables, and the strain-deflection relationship can be established by using the widely accepted methods (e.g., the quadratic integral method and classical conjugate beam method). However, the application of quadratic integral method and classical conjugate beam method are based on many assumptions, and there will be remarkable deviation between calculated deflection and actual displacement with the increase of integral length. Given this, a new deflection calculation method based on machine learning is proposed. Through learning on the monitoring data, an implicit function model between depth, strain, and measured displacement is established by using the BP (back propagation) neural network algorithm. The efficiency of the proposed model has been verified against measured displacement, which demonstrates the capability of this method for landslide deformation prediction. Compared with the traditional integral method, the lateral deflection curve of inclinometer calculated by the proposed method is closer to the actual measured displacement both in trend and values. The proposed model shows great potential in the application of deflection calculation in engineering.