LEADER 04121nam 2200781z- 450 001 9910557504303321 005 20220111 035 $a(CKB)5400000000044513 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/76277 035 $a(oapen)doab76277 035 $a(EXLCZ)995400000000044513 100 $a20202201d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aSensor Networks in Structural Health Monitoring: From Theory to Practice 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 online resource (164 p.) 311 08$a3-0365-0632-2 311 08$a3-0365-0633-0 330 $aThe 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. 517 $aSensor Networks in Structural Health Monitoring 606 $aTechnology: general issues$2bicssc 610 $aadjacent buildings 610 $aautoregressive with exogenous inputs 610 $aBayesian inference 610 $aBayesian model updating 610 $abridges 610 $acomputation time 610 $adamage detection and localization 610 $adamage identification 610 $adistributed sensor network 610 $aerror-domain model falsification 610 $aevolutionary optimisation 610 $afrequency of entrainment 610 $aGaussian process regression 610 $ainertial sensor fusion 610 $ainstrumented particle 610 $aInternet of Things (IoT) 610 $aiterative asset-management 610 $aMEMS 610 $amodal identification 610 $amode shape curvatures 610 $amodel updating 610 $amutual information 610 $an/a 610 $apractical applicability 610 $aprobabilistic data-interpretation 610 $asediment entrainment 610 $asemi-active control 610 $asensor calibration 610 $asensor placement optimisation 610 $asoil-structure interaction (SSI) 610 $astructural dynamics 610 $astructural health monitoring 610 $aswarm-based parallel control (SPC) 610 $avarying environmental and operational conditions 615 7$aTechnology: general issues 700 $aChatzi$b Eleni$4edt$01293625 702 $aDertimanis$b Vasilis K$4edt 702 $aChatzi$b Eleni$4oth 702 $aDertimanis$b Vasilis K$4oth 906 $aBOOK 912 $a9910557504303321 996 $aSensor Networks in Structural Health Monitoring: From Theory to Practice$93022675 997 $aUNINA