Toward reduced recovery time after extreme events, near real‐time damage diagnosis of structures is critical to provide reliable information. For this task, a fully convolutional encoder–decoder neural network is developed, which considers the spatial correlation of sensors in the automatic feature extraction process through a grid environment. A cost‐sensitive score function is designed to include the consequences of misclassification in the framework while considering the ground motion uncertainty in training. A 10‐story‐10‐bay reinforced concrete (RC) moment frame is modeled to present the design process of the deep learning architecture. The proposed models achieve global testing accuracies of 96.3% to locate damage and 93.2% to classify 16 damage mechanisms. Moreover, to handle class imbalance, three strategies are investigated enabling an increase of 16.2% regarding the mean damage class accuracy. To evaluate the generalization capacities of the framework, the classifiers are tested on 1,080 different RC frames by varying model properties. With less than a 2% reduction in global accuracy, the data‐driven model is shown to be reliable for the damage diagnosis of different frames. Given the robustness and capabilities of the grid environment, the proposed framework is applicable to different domains of structural health monitoring research and practice to obtain reliable information.

中文翻译：

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基于振动的语义损伤分割，用于大规模结构健康监测

为了减少极端事件后的恢复时间，对结构进行接近实时的损坏诊断对于提供可靠的信息至关重要。为此，开发了一个全卷积编码器-解码器神经网络，该网络考虑了通过网格环境进行自动特征提取过程中传感器的空间相关性。成本敏感得分函数旨在在考虑训练中地面运动不确定性的同时，将框架分类错误的后果包括在内。对10层10海湾钢筋混凝土（RC）弯矩框架进行了建模，以展示深度学习体系结构的设计过程。所提出的模型可实现96.3％的总体测试准确度来定位损坏，而93.2％的可对16种损坏机制进行分类。此外，为了解决阶级失衡，研究了三种策略，使平均损坏等级准确性提高了16.2％。为了评估框架的泛化能力，通过更改模型属性，在1,080个不同的RC框架上对分类器进行了测试。数据驱动的模型将全局精度降低了不到2％，对于不同框架的损伤诊断是可靠的。考虑到网格环境的健壮性和功能性，建议的框架适用于结构健康监测研究和实践的不同领域，以获得可靠的信息。数据驱动的模型对于不同框架的损伤诊断是可靠的。考虑到网格环境的健壮性和功能性，建议的框架适用于结构健康监测研究和实践的不同领域，以获得可靠的信息。数据驱动的模型对于不同框架的损伤诊断是可靠的。考虑到网格环境的健壮性和功能性，建议的框架适用于结构健康监测研究和实践的不同领域，以获得可靠的信息。