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220822 ||| eng |
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|a books978-3-0365-0633-3
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| 020 |
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|a 9783036506326
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| 020 |
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|a 9783036506333
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| 100 |
1 |
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|a Chatzi, Eleni
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| 245 |
0 |
0 |
|a Sensor Networks in Structural Health Monitoring: From Theory to Practice
|h Elektronische Ressource
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| 260 |
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|a Basel, Switzerland
|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2021
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| 300 |
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|a 1 electronic resource (164 p.)
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| 653 |
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|a practical applicability
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| 653 |
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|a inertial sensor fusion
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| 653 |
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|a computation time
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| 653 |
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|a sensor placement optimisation
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| 653 |
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|a model updating
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| 653 |
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|a mode shape curvatures
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| 653 |
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|a MEMS
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| 653 |
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|a frequency of entrainment
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| 653 |
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|a damage identification
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| 653 |
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|a instrumented particle
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| 653 |
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|a n/a
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| 653 |
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|a modal identification
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| 653 |
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|a semi-active control
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| 653 |
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|a sediment entrainment
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| 653 |
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|a Technology: general issues / bicssc
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| 653 |
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|a bridges
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| 653 |
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|a structural dynamics
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| 653 |
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|a iterative asset-management
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| 653 |
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|a damage detection and localization
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| 653 |
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|a Bayesian model updating
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| 653 |
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|a swarm-based parallel control (SPC)
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| 653 |
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|a soil–structure interaction (SSI)
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| 653 |
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|a sensor calibration
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| 653 |
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|a Gaussian process regression
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| 653 |
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|a error-domain model falsification
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| 653 |
|
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|a varying environmental and operational conditions
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| 653 |
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|a distributed sensor network
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| 653 |
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|a structural health monitoring
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| 653 |
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|a evolutionary optimisation
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| 653 |
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|a probabilistic data-interpretation
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| 653 |
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|a Internet of Things (IoT)
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| 653 |
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|a autoregressive with exogenous inputs
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| 653 |
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|a adjacent buildings
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| 653 |
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|a mutual information
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| 653 |
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|a Bayesian inference
|
| 700 |
1 |
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|a Dertimanis, Vasilis K.
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| 700 |
1 |
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|a Chatzi, Eleni
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| 700 |
1 |
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|a Dertimanis, Vasilis K.
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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/books978-3-0365-0633-3
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| 856 |
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|u https://directory.doabooks.org/handle/20.500.12854/76277
|3 Volltext
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| 082 |
0 |
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|a 363
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| 082 |
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|a 000
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| 082 |
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|a 610
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|a 576
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|a 700
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|a 600
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| 520 |
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|a The intense development of novel data-driven and hybrid methods for structural health monitoring (SHM) has been demonstrated by field deployments on large-scale systems, including transport, wind energy, and building infrastructure. The actionability of SHM as an essential resource for life-cycle and resilience management is heavily dependent on the advent of low-cost and easily deployable sensors Nonetheless, in optimizing these deployments, a number of open issues remain with respect to the sensing side. These are associated with the type, configuration, and eventual processing of the information acquired from these sensors to deliver continuous behavioral signatures of the monitored structures. This book discusses the latest advances in the field of sensor networks for SHM. The focus lies both in active research on the theoretical foundations of optimally deploying and operating sensor networks and in those technological developments that might designate the next generation of sensing solutions targeted for SHM. The included contributions span the complete SHM information chain, from sensor design to configuration, data interpretation, and triggering of reactive action. The featured papers published in this Special Issue offer an overview of the state of the art and further proceed to introduce novel methods and tools. Particular attention is given to the treatment of uncertainty, which inherently describes the sensed information and the behavior of monitored systems.
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