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A data-driven structural damage identification approach using deep convolutional-attention-recurrent neural architecture under temperature variations
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DOI：10.1016/j.engstruct.2022.115311.png)
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Abstract
En 中文
In recent years, by emerging deep learning (DL) based algorithms, researchers have been exploring DL-based models to identify structural damage through data-driven approaches. DL-based data-driven techniques using autonomous feature extraction from raw sequential data are more robust under environmental variations. Extraction of robust features while considering the sequential dependencies will significantly improve the ac-curacy of damage identification by these techniques. In this regard, various architectures of DL-based models have been proposed. This study presents a novel DL-based model that utilizes both one-dimensional convolutional neural network (1DCNN) and recurrent neural network (RNN) variants using an attention mechanism. Attention mechanism improves the performance of the 1DCNN-RNN variants model precisely when its input data is raw acceleration time-history, as a kind of sequential data. The IASC-ASCE phase II and Qatar University grandstand simulator benchmarks are used to evaluate the proposed model by comparing its performance with DL-based neural network architectures that could be equivalent to this combination. Moreover, the environmental variable which affects structural response is also examined. Results demonstrate that the CNN-ATT-biGRU model ar-chitecture has the best accuracy and appropriate training time and model size among nine compared architectures.
Keywords:
Structural health monitoring (SHM)
Deep Learning
1DCNN
RNN variants
Attention mechanism
Environmental effects
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