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Intelligent optimization and machine learning algorithms for structural anomaly detection using seismic signals
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DOI：10.1016/j.ymssp.2019.106250.png)
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摘要
En 中文
The lack of anomaly detection methods during mechanized tunnelling can cause financial loss and deficits in drilling time. On-site excavation requires hard obstacles to be recognized prior to drilling in order to avoid damaging the tunnel boring machine and to adjust the propagation velocity. The efficiency of the structural anomaly detection can be increased with intelligent optimization techniques and machine learning. In this research, the anomaly in a simple structure is detected by comparing the experimental measurements of the structural vibrations with numerical simulations using parameter estimation methods. (C) 2019 Elsevier Ltd. All rights reserved.
Keyword:
Structural anomaly detection
Kalman filter
Unscented hybrid simulated annealing
Gaussian processes
Deep Gaussian covariance network
Inverse problem
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期刊
IF:
8.9
论文数:
1.3W
被引数:
6.6W
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