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Applying Deep Learning to Continuous Bridge Deflection Detected by Fiber Optic Gyroscope for Damage Detection

机译：将深度学习应用于光纤陀螺仪检测到的连续桥梁变形以进行损伤检测

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摘要

Improving the accuracy and efficiency of bridge structure damage detection is one of the main challenges in engineering practice. This paper aims to address this issue by monitoring the continuous bridge deflection based on the fiber optic gyroscope and applying the deep-learning algorithm to perform structural damage detection. With a scale-down bridge model, three types of damage scenarios and an intact benchmark were simulated. A supervised learning model based on the deep convolutional neural networks was proposed. After the training process under ten-fold cross-validation, the model accuracy can reach 96.9% and significantly outperform that of other four traditional machine learning methods (random forest, support vector machine, k-nearest neighbor, and decision tree) used for comparison. Further, the proposed model illustrated its decent ability in distinguishing damage from structurally symmetrical locations.
机译：提高桥梁结构损伤检测的准确性和效率是工程实践中的主要挑战之一。本文旨在通过监测基于光纤陀螺仪的连续桥梁挠度并应用深度学习算法进行结构损伤检测来解决这一问题。利用缩小的桥梁模型，模拟了三种类型的破坏情景和完整的基准。提出了一种基于深度卷积神经网络的监督学习模型。经过十次交叉验证的训练过程后，模型的准确率可达到96.9％，明显优于其他四种用于比较的传统机器学习方法（随机森林，支持向量机，k最近邻和决策树） 。此外，所提出的模型说明了其从结构对称位置区分损坏方面的良好能力。

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