Accurate Measurement of Bridge Vibration Displacement via Deep Convolutional Neural Network

流离失所(心理学) 计算机科学 人工神经网络 人工智能 卷积神经网络 振动 算法 结构健康监测 加速度 位移场 深度学习 计算机视觉 工程类 结构工程 声学 有限元法 心理学 物理 经典力学 心理治疗师
作者
Sen Lin,Sen Wang,Tao Liu,Xiaoqin Liu,Chang Liu
出处
期刊:IEEE Transactions on Instrumentation and Measurement [Institute of Electrical and Electronics Engineers]
卷期号:72: 1-16 被引量:3
标识
DOI:10.1109/tim.2023.3291786
摘要

Displacement measurement is an essential method for structural safety assessment and health monitoring, and the static and dynamic characteristics of the structure can be obtained through displacement. In order to overcome the limitations of sensors in vibration measurement of large structures, as well as the poor adaptability of visual measurement algorithms such as machine learning and digital image processing, this paper takes the bridge structure as the research object and introduces deep learning into the field of visual vibration measurement. Moreover, based on the deep convolutional neural network, a new high-precision displacement measurement algorithm of multi-scale feature extraction and fusion is proposed to solve the inaccurate measurement of existing neural networks. Experiments are carried out on bridge models in the laboratory environment and bridges in the real world to verify the adaptability and reliability of the proposed method. At the same time, the time-frequency characteristic curves of different deep learning models, template matching algorithms, and acceleration sensors are compared. The result analysis shows that the vibration displacement trajectory of the algorithm in this paper has the best coincidence with the standard displacement signal. Three experiments have fully verified that the algorithm in this paper has good application potential and implementation space in the field of structural state monitoring.
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