Data Science in Engineering, Volume 10

Proceedings of the 41st IMAC, A Conference and Exposition on Structural Dynamics 2023
Herausgegeben:Madarshahian, Ramin; Hemez, François

• Gebundenes Buch

Produktbeschreibung

Data Science in Engineering, Volume 10: Proceedings of the 41st IMAC, A Conference and Exposition on Structural Dynamics, 2023, the tenth volume of ten 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:

Novel Data-driven Analysis MethodsDeep Learning Gaussian Process AnalysisReal-time Video-based AnalysisApplications to Nonlinear Dynamics and Damage DetectionHigh-rate Structural Monitoring and Prognostics

Produktdetails

• Conference Proceedings of the Society for Experimental Mechanics Series
• Verlag: Springer / Springer Nature Switzerland / Springer, Berlin
• Artikelnr. des Verlages: 978-3-031-34945-4
• 2023
• Seitenzahl: 200
• Erscheinungstermin: 8. Dezember 2023
• Englisch
• Abmessung: 285mm x 215mm x 17mm
• Gewicht: 755g
• ISBN-13: 9783031349454
• ISBN-10: 3031349458
• Artikelnr.: 67885435

Autorenporträt

Ramin Madarshahian-Company: Kount, an Equifax company, Boise, ID, USA ; Francois Hemez-Lawrence Livermore National Laboratory, Livermore, CA, USA

Inhaltsangabe

Chapter 1. A Meta-Learning Approach to Population-Based Modelling of Structure.-, Chapter 2 State Space Reconstruction from Embeddings of Partial Observables in Structural Dynamic Systems for Structure-Preserving Data-Driven Methods.-, Chapter 3 Chapter 2. State Space Reconstruction from Embeddings of Partial Observables in Structural Dynamic Systems for Structure-Preserving Data-Driven Methods.-, Chapter 4 Composite Neural Network Framework for Modeling Impulsive Nonlinear Dynamic Responses.-, Chapter 5 Towards physics-based metrics for transfer learning in dynamics.-, Chapter 6 Principal Component Analysis of Monitoring Data of a High-Rise Building: The Case Study of Palazzo Lombardia.-, Chapter 7 Optimal Contact-Impact Force Model Selection for Damage Detection in Ball Bearings.-, Chapter 8 Simulation Error Influence on Damage Identification Classifiers Trained by Numerical Data.-, Chapter 9 Structural Health Monitoring in the Context ofNon-Equilibrium Phase Transitions.-, Chapter 10 Synthetic Thermal Image Data Generation using Attention-Based Generative Adversarial Network for Concrete Internal Damage Segmentation.-, Chapter 11 Optimal Fiber Optic Sensor Placement Framework for Structural Health Monitoring of an Aircraft's Wing Spar.-, Chapter 12 Construction Noise Cancellation with Feedback Active Control using Machine Learning.-, Chapter 13 Physics-Informed Data-Driven Reduced-Order Model for Turbomachinery Blisk.-, Chapter 14 High-rate Structural Health Monitoring: Part-II Embedded System Design.-, Chapter 15 Damage Quantification under High-Rate Dynamic Loading and Data Augmentation using Generative Adversarial Network.-, Chapter16 Output-only versus Direct Input-output Structural Condition Monitoring Methods.-, Chapter 17 High-rate Structural Health Monitoring: Part-III Algorithm.-, Chapter 18 A population form via hierarchical Bayesian modelling of the FRF.-, Chapter 19 Lupos: Open-source Scientific Computing in Structural Dynamics.-, Chapter 20 Expert Knowledge-Driven Condition Assessment of Railway Welds from Axle Box Accelerations using Random Forests and Bayesian Logistic Regression.-, Chapter 21 On quantifying data normalisation via cointegration with topological methods.-, Chapter 22 Automatic Selection of Optimal Structures for Population-based Structural Health Monitoring.-, Chapter 23 Online back-propagation of recurrent neural network for forecasting nonstationary structural responses.

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Chapter 1. A Meta-Learning Approach to Population-Based Modelling of Structure.-, Chapter 2 State Space Reconstruction from Embeddings of Partial Observables in Structural Dynamic Systems for Structure-Preserving Data-Driven Methods.-, Chapter 3 Chapter 2. State Space Reconstruction from Embeddings of Partial Observables in Structural Dynamic Systems for Structure-Preserving Data-Driven Methods.-, Chapter 4 Composite Neural Network Framework for Modeling Impulsive Nonlinear Dynamic Responses.-, Chapter 5 Towards physics-based metrics for transfer learning in dynamics.-, Chapter 6 Principal Component Analysis of Monitoring Data of a High-Rise Building: The Case Study of Palazzo Lombardia.-, Chapter 7 Optimal Contact-Impact Force Model Selection for Damage Detection in Ball Bearings.-, Chapter 8 Simulation Error Influence on Damage Identification Classifiers Trained by Numerical Data.-, Chapter 9 Structural Health Monitoring in the Context ofNon-Equilibrium Phase Transitions.-, Chapter 10 Synthetic Thermal Image Data Generation using Attention-Based Generative Adversarial Network for Concrete Internal Damage Segmentation.-, Chapter 11 Optimal Fiber Optic Sensor Placement Framework for Structural Health Monitoring of an Aircraft’s Wing Spar.-, Chapter 12 Construction Noise Cancellation with Feedback Active Control using Machine Learning.-, Chapter 13 Physics-Informed Data-Driven Reduced-Order Model for Turbomachinery Blisk.-, Chapter 14 High-rate Structural Health Monitoring: Part-II Embedded System Design.-, Chapter 15 Damage Quantification under High-Rate Dynamic Loading and Data Augmentation using Generative Adversarial Network.-, Chapter16 Output-only versus Direct Input-output Structural Condition Monitoring Methods.-, Chapter 17 High-rate Structural Health Monitoring: Part-III Algorithm.-, Chapter 18 A population form via hierarchical Bayesian modelling of the FRF.-, Chapter 19 Lupos: Open-source Scientific Computing in Structural Dynamics.-, Chapter 20 Expert Knowledge-Driven Condition Assessment of Railway Welds from Axle Box Accelerations using Random Forests and Bayesian Logistic Regression.-, Chapter 21 On quantifying data normalisation via cointegration with topological methods.-, Chapter 22 Automatic Selection of Optimal Structures for Population-based Structural Health Monitoring.-, Chapter 23 Online back-propagation of recurrent neural network for forecasting nonstationary structural responses.