The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Structural Damage Detection with Automatic Feature‐Extraction through Deep Learning
Open accessAbstract
Structural damage detection is still a challenging problem owing to the difficulty of extracting damage‐sensitive and noise‐robust features from structure response. This article presents a novel damage detection approach to automatically extract features from low‐level sensor data through deep learning. A deep convolutional neural network is designed to learn features and identify damage locations, leading to an excellent localization accuracy on both noise‐free and noisy data set, in contrast to another detector using wavelet packet component energy as the input feature. Visualization of the features learned by hidden layers in the network is implemented to get a physical insight into how the network works. It is found the learned features evolve with the depth from rough filters to the concept of vibration mode, implying the good performance results from its ability to learn essential characteristics behind the data.
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ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
Backpropagation Applied to Handwritten Zip Code Recognition
LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.541 · Cited by 11,891
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
Simonyan, K., Vedaldi, A., & Zisserman, A. (2013) · arXiv · doi:10.48550/arxiv.1312.6034 · Cited by 5,370
Building high-level features using large scale unsupervised learning
Le, Q. V. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6639343 · Cited by 1,695
Cited by
Machine learning in materials science
Wei, J., Chu, X., Sun, X., Xu, K., Deng, H., Chen, J., Wei, Z., & Lei, M. (2019) · InfoMat · doi:10.1002/inf2.12028 · Cited by 1,069
Road Damage Detection and Classification Using Deep Neural Networks with Smartphone Images
Maeda, H., Sekimoto, Y., Seto, T., Kashiyama, T., & Omata, H. (2018) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12387 · Cited by 870
Automatic Pixel‐Level Crack Detection and Measurement Using Fully Convolutional Network
Yang, X., Li, H., Yu, Y., Luo, X., Huang, T., & Yang, X. (2018) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12412 · Cited by 852
Azimi, M., Eslamlou, A., & Pekcan, G. (2020) · Sensors · doi:10.3390/s20102778 · Cited by 769
Sun, L., Shang, Z., Xia, Y., Bhowmick, S., & Nagarajaiah, S. (2020) · Journal of Structural Engineering · doi:10.1061/(asce)st.1943-541x.0002535 · Cited by 731
Metadata
- Authors
- Lin, Y., Nie, Z., & Ma, H.
- Venue
- Computer-Aided Civil and Infrastructure Engineering
- Publisher
- Elsevier BV
- Published
- 2017-11-10
- Volume
- 32
- Issue
- 12
- Pages
- 1025-1046
- ISSN
- 1093-9687 (ISSN-L)
- 1467-8667
- OpenAlex
- W2767522444
- Cited by
- 634
- References
- 7
- FWCI
- 50.23
- Type
- journal article
- Language
- en
- Open access
- hybrid
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