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001442387 019__ $$a1273915581$$a1273973817$$a1287776945
001442387 020__ $$a9783030760045$$q(electronic bk.)
001442387 020__ $$a3030760049$$q(electronic bk.)
001442387 020__ $$z9783030760038
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001442387 0247_ $$a10.1007/978-3-030-76004-5$$2doi
001442387 035__ $$aSP(OCoLC)1273678192
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001442387 049__ $$aISEA
001442387 050_4 $$aTA345$$b.I58 2021
001442387 08204 $$a620.001/51$$223
001442387 1112_ $$aInternational Modal Analysis Conference$$n(39th :$$d2021)
001442387 24510 $$aData science in engineering.$$nVolume 9 :$$bproceedings of the 39th IMAC, a conference and exposition on structural dynamics 2021 /$$cRamin Madarshahian, Francois Hemez, editors.
001442387 24630 $$aIMAC
001442387 264_1 $$aCham :$$bSpringer,$$c[2022]
001442387 264_4 $$c©2022
001442387 300__ $$a1 online resource :$$billustrations (chiefly color)
001442387 336__ $$atext$$btxt$$2rdacontent
001442387 337__ $$acomputer$$bc$$2rdamedia
001442387 338__ $$aonline resource$$bcr$$2rdacarrier
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001442387 347__ $$bPDF
001442387 4901_ $$aConference proceedings of the Society for Experimental Mechanics series
001442387 500__ $$aInternational conference proceedings.
001442387 5050_ $$aChapter 1. Towards a Population-based Structural Health Monitoring, Part V: Networks and Databases -- Chapter 2. Active Learning of Post-Earthquake Structural Damage with Co-Optimal Information Gain and Reconnaissance Cost -- Chapter 3. Uncertainty-Quantified Damage Identification for High-Rate Dynamic Systems -- Chapter 4. Real-time Machine Learning of Vibration Signals -- Chapter 5. Data-Driven Identification of Mistuning in Blisks -- Chapter 6. On Generating Parametrised Structural Data Using Conditional Generative Adversarial Networks -- Chapter 7. Best Paper: On an Application of Graph Neural Networks in Population Based SHM -- Chapter 8. Estimation of Elastic Band Gaps Using Data-Driven Model -- Chapter 9. Damage Localization on Lightweight Structures with Non-Destructive Testing and Machine Learning Techniques -- Chapter 10. Challenges for SHM from Structural Repairs: An Outlier-informed Domain Adaptation Approach -- Chapter 11. On the Application of Heterogeneous Transfer Learning to Population-based Structural Health Monitoring -- Chapter 12. An Unsupervised Deep Auto-Encoder with One-Class Support Vector Machine for Damage Detection -- Chapter 13. Identifying Operations- and Environmental-Insensitive Damage Features -- Chapter 14. Hybrid Concrete Crack Segmentation and Quantification Across Complex Backgrounds without Big Training Dataset -- Chapter 15. Digital Stroboscopy using Event-Driven Imagery -- Chapter 16. Managing System Inspections for Health Monitoring: A Probability of Query Approach -- Chapter 17. Parameter Estimation for Dynamical Systems Under Continuous and Discontinuous Gaussian Noise Using Data Assimilation Techniques -- Chapter 18. Model Reduction of Geometrically Nonlinear Structures via Physics-Informed Autoencoders -- Chapter 19. Techniques to Improve Robustness of Video-Based Sensor Networks -- Chapter 20. Grey-Box Modelling via Gaussian Process Mean Functions for Mechanical Systems -- Chapter 21. On Topological Data Analysis for SHM; An Introduction to Persistent Homology -- Chapter 22. Heteroscedastic Gaussian Processes for Localising Acoustic Emission -- Chapter 23. Transferring Damage Detectors Between Tailplane Experiments -- Chapter 24. High-Rate Structural Health Monitoring and Prognostics: An Overview -- Chapter 25. One Versus All: Best Practices in Combining Multi-Hazard Damage Imagery Training Datasets for Damage Detection for a Deep Learning Neural Network -- Chapter 26. High-Rate Damage Classification and Lifecycle Prediction via Deep Learning -- Chapter 27. A Generalized Technique for Full-field Blind Identification of Travelling Waves and Complex Modes from Video Measurements with Hilbert Transform -- Chapter 28. Privacy-Preserving Structural Dynamics -- Chapter 29. Abnormal Behavior Detection of the Indian River Inlet Bridge through Cross Correlation Analysis of Truck Induced Strains -- Chapter 30. A Video-Based Crack Detection in Concrete Surfaces -- Chapter 31. Bayesian Graph Neural Networks for Strain-Based Crack Localization -- Chapter 32. Routing of Public and Electric Transportation Systems Using Reinforcement Learning -- Chapter 33. Vibration based Damage Detection and Identification in a CFRP Truss with Deep Learning and Finite Element Generated Data -- Chapter 34. Parametric Amplification in a Stochastic Nonlinear Piezoelectric Energy Harvester via Machine Learning.
001442387 506__ $$aAccess limited to authorized users.
001442387 520__ $$aData Science and Engineering Volume 9: Proceedings of the 39th IMAC, A Conference and Exposition on Structural Dynamics, 2021, the ninth volume of nine from the Conference, brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Data Science in Engineering, including papers on: Data Science in Engineering Applications Engineering Mathematics Computational Methods in Engineering.
001442387 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed October 13, 2021).
001442387 650_0 $$aEngineering$$xData processing$$vCongresses.
001442387 650_0 $$aStructural dynamics$$vCongresses.
001442387 650_6 $$aIngénierie$$xInformatique$$vCongrès.
001442387 650_6 $$aConstructions$$xDynamique$$vCongrès.
001442387 655_7 $$aConference papers and proceedings.$$2fast$$0(OCoLC)fst01423772
001442387 655_7 $$aConference papers and proceedings.$$2lcgft
001442387 655_7 $$aActes de congrès.$$2rvmgf
001442387 655_0 $$aElectronic books.
001442387 7001_ $$aMadarshahian, Ramin,$$eeditor.
001442387 7001_ $$aHemez, Francois,$$eeditor.
001442387 77608 $$iPrint version:$$aInternational Modal Analysis Conference (39th : 2021).$$tData science in engineering. Volume 9.$$dCham : Springer, [2022]$$z3030760030$$z9783030760038$$w(OCoLC)1246352590
001442387 830_0 $$aConference proceedings of the Society for Experimental Mechanics series.
001442387 852__ $$bebk
001442387 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-76004-5$$zOnline Access$$91397441.1
001442387 909CO $$ooai:library.usi.edu:1442387$$pGLOBAL_SET
001442387 980__ $$aBIB
001442387 980__ $$aEBOOK
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