A machine learning-based strain estimation method for structural members in a building is presented The relationship between the strain responses of structural members is determined using a convolutional neural network (CNN) For accurate strain estimation, correlation analysis is introduced to select the optimal CNN model among responses from multiple structural members. The optimal CNN model trained using the response of the structural member with a high degree of correlation with the response of the target structural member is utilized to estimate the strain of the target structural member The proposed correlation-based technique can also provide the next best CNN model in case of defects in the sensors used to construct the optimal CNN. Validity is examined through the application of the presented technique to a numerical study on a three-dimensional steel structure and an experimental study on a steel frame specimen.

中文翻译：

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使用卷积神经网络的基于测量数据相关性的建筑结构应变估计技术

提出了一种基于机器学习的建筑物结构构件应变估计方法 使用卷积神经网络 (CNN) 确定结构构件应变响应之间的关系 为了准确估计应变，引入相关分析来选择最佳 CNN 模型来自多个结构成员的回应。利用与目标结构构件的响应高度相关的结构构件的响应训练的最佳CNN模型来估计目标结构构件的应变所提出的基于相关性的技术还可以提供次佳的CNN模型，以防用于构建最佳 CNN 的传感器存在缺陷。