基于分布式光纤应变传感和深度学习的混凝土缺陷预测方法研究

Research on Concrete Defect Prediction Method Based on Distributed Optical Fiber Strain Sensing and Deep Learning

摘要: 针对钢筋混凝土结构健康监测，提出了一种基于分布式光纤应变传感的缺陷预测方法。通过构建通用人工神经网络，对缺陷样本进行深度学习训练，可自动实现特征提取和分类识别，避免了人工建模方法的复杂性。通过开展缺陷模拟实验，对缺陷预测方法准确性进行了验证。实验表明，经过深度学习的分类识别模型可实现缺陷样本准确预测，准确率达到99%以上。

Abstract: A defect prediction method based on distributed optical fiber strain sensing is proposed for the health monitoring of reinforced concrete structures. By constructing a general artificial neural network and performing deep learning training on defective samples, feature extraction and classification and recognition can be automatically realized, avoiding the complexity of manual modeling methods. By carrying out defect simulation experiments, the accuracy of the defect prediction method was verified. Experiments show that the classification and recognition model after deep learning can achieve accurate prediction of defect samples, with an accuracy rate of over 99%.