Machine Learning-Based Distraction-Free Method for Measuring the Optical Displacement of Long-Span Bridge Structures

Abstract

A photogrammetric displacement measurement method based on machine learning was proposed to improve the robustness to environmental disturbances. (1) To reduce the target positioning error caused by environmental vibration especially atmospheric turbulence, a machine learning-based weighted location algorithm combined with an adaptive window selection strategy was developed. In an outdoor displacement table experiment, the proposed method’s root mean squared error (RMSE) is 0.04 mm when the distance is 50 m, showing better accuracy and stability. (2) To complement/correct the missing or anomalous data caused by adverse external conditions, such as severe occlusion or camera shaking, a fast data self-diagnosis using a correlation vector machine was performed to make full use of the full-field measurement results obtained from the images. The applicability of the proposed method in extreme cases was demonstrated in designed experiments and an actual displacement measurement task of a long-span bridge subjected to vortex-induced vibration.