A framework of structural damage detection for civil structures using a combined multi-scale convolutional neural network and echo state network
Engineering with Computers, ISSN: 1435-5663, Vol: 39, Issue: 3, Page: 1771-1789
2023
- 43Citations
- 61Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Structural health monitoring (SHM) has become a notable method to ensure structural safety, yet the ability of existing damage detection techniques need improvements on extracting structural information from SHM data. Echo state networks (ESN) and multi-scale convolutional neural networks (MSCNN) proved effective in analyzing time and frequency domain data for civil structures. However, these models cannot identify structural information in the time–frequency domain. This study proposes a novel ESN-MSCNN combined model to effectively extract the time–frequency features of civil structures for damage detection. Firstly, vibration signal data is transformed into continuous time and Fourier spaces via data augmentation operation. Secondly, the ESN and MSCNN structures extract time and frequency domain features from preprocessed data, respectively. Finally, two combined features are fed into two fully connected layers to evaluate the degree of structural damage. Experiments on a scaled bridge and an IASC-ASCE benchmark building indicated that the proposed ESN-MSCNN model outperforms the state-of-the-art models for structural damage detection.
Bibliographic Details
Springer Science and Business Media LLC
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