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220822 ||| eng |
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|a 9783036542386
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|a books978-3-0365-4238-6
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|a 9783036542379
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| 100 |
1 |
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|a Yang, Yang
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| 245 |
0 |
0 |
|a Symmetry in Structural Health Monitoring
|h Elektronische Ressource
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| 260 |
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|a Basel
|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2022
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| 300 |
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|a 1 electronic resource (310 p.)
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| 653 |
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|a seismic mitigation
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| 653 |
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|a cable clamp
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| 653 |
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|a substructure shake table testing
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| 653 |
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|a force analysis
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| 653 |
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|a improved YOLOv4
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| 653 |
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|a dynamic response
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| 653 |
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|a genetic algorithm
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| 653 |
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|a mixed sensitivity
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| 653 |
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|a flatness calculation datum
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| 653 |
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|a finite element method
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| 653 |
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|a crack detection
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| 653 |
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|a surface flatness of initial support of tunnel
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| 653 |
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|a frequency domain
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| 653 |
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|a high formwork
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| 653 |
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|a time-frequency extraction
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| 653 |
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|a time domain
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| 653 |
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|a stress trend prediction
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| 653 |
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|a deep learning
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| 653 |
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|a integration algorithm
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| 653 |
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|a digital twin
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| 653 |
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|a History of engineering & technology / bicssc
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| 653 |
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|a intelligent construction
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| 653 |
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|a safety assessment
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| 653 |
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|a data anomaly detection
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| 653 |
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|a Technology: general issues / bicssc
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| 653 |
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|a three-dimensional laser scanning
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| 653 |
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|a near and far field
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| 653 |
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|a seismic pulse
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| 653 |
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|a H∞ control
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| 653 |
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|a slippage
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| 653 |
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|a artificial neutral network
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| 653 |
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|a technical codes
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| 653 |
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|a BPNN
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| 653 |
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|a vibration
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| 653 |
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|a frequency-domain integration approach (FDIA)
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| 653 |
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|a time delay
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| 653 |
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|a multiple square loops (MSL)-string
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| 653 |
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|a pounding mitigation and unseating prevention
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| 653 |
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|a curved surface fitting
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| 653 |
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|a construction process
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| 653 |
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|a self-anchored suspension bridge
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| 653 |
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|a time-frequency domain
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| 653 |
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|a coupling model
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| 653 |
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|a offshore oil platform
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| 653 |
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|a terrestrial laser scanning
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| 653 |
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|a variational mode decomposition (VMD)
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| 653 |
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|a prestressed steel structure
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| 653 |
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|a torsion angle calculation
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| 653 |
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|a RGB
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| 653 |
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|a damper
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| 653 |
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|a concrete surface
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| 653 |
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|a structural health monitoring
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| 653 |
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|a micro inertial measurement unit (MIMU)
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| 653 |
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|a heavy-duty vehicle
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| 653 |
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|a ARMA
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| 653 |
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|a curvedcontinuous girder bridge
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| 653 |
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|a seismic excitation
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| 653 |
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|a Hilbert-Huang transform (HHT)
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| 653 |
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|a convolutional neural network
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| 653 |
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|a road
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| 653 |
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|a collision response
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| 653 |
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|a structural health monitoring (SHM)
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| 653 |
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|a real-time hybrid simulation
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| 700 |
1 |
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|a Lei, Ying
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| 700 |
1 |
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|a Meng, Xiaolin
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| 700 |
1 |
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|a Li, Jun
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
|
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|b DOAB
|a Directory of Open Access Books
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| 500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
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| 024 |
8 |
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|a 10.3390/books978-3-0365-4238-6
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| 856 |
4 |
2 |
|u https://directory.doabooks.org/handle/20.500.12854/87427
|z DOAB: description of the publication
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| 856 |
4 |
0 |
|u https://www.mdpi.com/books/pdfview/book/5615
|7 0
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 900
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| 082 |
0 |
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|a 610
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| 082 |
0 |
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|a 700
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|a 600
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|a 620
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|a In this Special Issue on symmetry, we mainly discuss the application of symmetry in various structural health monitoring. For example, considering the health monitoring of a known structure, by obtaining the static or dynamic response of the structure, using different signal processing methods, including some advanced filtering methods, to remove the influence of environmental noise, and extract structural feature parameters to determine the safety of the structure. These damage diagnosis methods can also be effectively applied to various types of infrastructure and mechanical equipment. For this reason, the vibration control of various structures and the knowledge of random structure dynamics should be considered, which will promote the rapid development of the structural health monitoring. Among them, signal extraction and evaluation methods are also worthy of study. The improvement of signal acquisition instruments and acquisition methods improves the accuracy of data. A good evaluation method will help to correctly understand the performance with different types of infrastructure and mechanical equipment.
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