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Experimental Vibration Analysis for Civil Engineering Structures: EVACES 2023 - Volume 2 2023 ed. [Kõva köide]

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  • Formaat: Hardback, 763 pages, kõrgus x laius: 235x155 mm, kaal: 1334 g, 430 Illustrations, color; 76 Illustrations, black and white; XX, 763 p. 506 illus., 430 illus. in color., 1 Hardback
  • Sari: Lecture Notes in Civil Engineering 433
  • Ilmumisaeg: 29-Aug-2023
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3031391160
  • ISBN-13: 9783031391163
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Experimental Vibration Analysis for Civil Engineering Structures: EVACES 2023 - Volume 2 2023 ed.Suurem pilt
  • Formaat: Hardback, 763 pages, kõrgus x laius: 235x155 mm, kaal: 1334 g, 430 Illustrations, color; 76 Illustrations, black and white; XX, 763 p. 506 illus., 430 illus. in color., 1 Hardback
  • Sari: Lecture Notes in Civil Engineering 433
  • Ilmumisaeg: 29-Aug-2023
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3031391160
  • ISBN-13: 9783031391163
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783031391163.html
Märksõnad:
This volume presents peer-reviewed contributions from the 10th International Conference on Experimental Vibration Analysis for Civil Engineering Structures (EVACES), held in Milan, Italy on August 30-September 1, 2023. The event brought together engineers, scientists, researchers, and practitioners, providing a forum for discussing and disseminating the latest developments and achievements in all major aspects of dynamic testing for civil engineering structures, including instrumentation, sources of excitation, data analysis, system identification, monitoring and condition assessment, in-situ and laboratory experiments, codes and standards, and vibration mitigation. The topics included but were not limited to: damage identification and structural health monitoring; testing, sensing and modeling; vibration isolation and control; system and model identification; coupled dynamical systems (including human–structure, vehicle–structure, and soil–structure interaction); and application of advanced techniques involving the Internet of Things, robot, UAV, big data and artificial intelligence.
Optimization of structural health monitoring for bridges networks by
combining traditional and innovative techniques.- Value of Seismic Structural
Health Monitoring Information for management of civil structures under
different prior knowledge scenarios.- On the modeling of multi-sensors
vibration-based monitoring systems and integrity management.- Efficient
subspace-based operational modal analysis using video-based
vibrationmeasurements.- Model order selection for uncertainty quantification
in subspace-based OMA of Vestas V27 blade.- Modal Analysis of a Steel Truss
Bridge under Varying Environmental Conditions.- Linear System Identification
and Bayesian Model Updating of the UC San Diego Geisel Library.- FE model
updating of cable-stayed bridges based on the experimental estimate of
cable forces and modal parameters.- Physics-based and machine-learning models
for braking impact factors.- Dynamic tests with hard braking heavy vehicles
on a motorway bridge.- Determining braking forces on bridges using monitored
traffic data and stochastic simulation.- Fusing modal parameters and
curvature influence lines for damage localization under vehicle
excitation.- An Unsupervised Learning Method for Indirect Bridge Structural
Health Monitoring.- Using contact residual responses of a 3-DOF scooter to
identify first few frequencies of the footbridge.- Automatic drive-by bridge
damage detection via a clustering algorithm.- A Drive-by Bridge Damage
Localisation Method with an Instrumented Vehicle.- A Data-Driven Approach for
Monitoring Railway Tracks using Dynamic Responses Collected by an In-Service
Train.- Bridge response fusion drive-by-bridge inspection by means of model
updates.- Drive-by Bridge Deflection Estimating Method Based on Track
Irregularities Measured on a Train: Extension to Multiple Bridge
Sections.- Roadway roughness profile identification from vehicle acceleration
by means of dynamic regularized least square minimization.