Model Validation and Uncertainty Quantification, Volume 3

Name: Model Validation and Uncertainty Quantification, Volume 3 | Proceedings of the 41st IMAC, A Conference and Exposition on Structural Dynamics 2023
Brand: Springer
Price: 255.73 EUR
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Proceedings of the 41st IMAC, A Conference and Exposition on Structural Dynamics 2023

Roland Platz Garrison Flynn Kyle Neal Scott Ouellette(Editor)

Springer (Publisher)

1st Edition

Published on 6. October 2023

IX, 210 pages

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978-3-031-37003-8 (ISBN)

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Content

Intro
Preface
Contents
1 Introducing a Round-Robin Challenge to Quantify Model Form Uncertainty in Passive and Active Vibration Isolation
1.1 Introduction
1.2 Experimental Test Environment
1.3 Test and Analysis
1.4 Conclusion
References
2 An Uncertainty-Aware Measure of Model Calibration Flexibility
2.1 Introduction
2.2 Methodology
2.3 Case Study I: Polynomial Regressions
2.4 Conclusion
References
3 Quantifying Model Form Uncertainty in Spring-Mass-Damper Systems
3.1 Introduction
3.2 Spring-Mass-Damper Models
3.3 Enriched Model
3.4 Observations and Predictions
3.5 Hierarchical Bayesian Calibration
3.6 Validation
3.7 Results
3.8 Conclusion
References
4 Event Detection Using Floor Vibrations with a Probabilistic Framework
4.1 Introduction
4.2 Methods
4.3 Probabilistic Localization Model
4.4 Results and Discussion
4.5 Conclusions
4.6 Disclaimers
References
5 Advancing Model Credibility for Linked Multi-physics Surrogate Models Within a Coupled Digital Engineering Workflow of Nuclear Deterrence Systems
5.1 Introduction
5.2 Background
5.3 Process
5.4 Conclusion
Reference
6 Estimating the Effect of Noise on Various ARMA-Based Damage-Sensitive Features
6.1 Introduction
6.2 Background
6.3 Methods
6.4 Results
6.5 Conclusion
References
7 Bayesian Model Updating for System and Damage Identification of Bridges Using Synthetic and Field Test Data
7.1 Introduction
7.2 Formulation
7.3 Verification
7.4 Validation
7.5 Conclusions
References
8 Static and Dynamic Characterization of a Vibration Decoupling Element Based on a Metamaterial Structure
8.1 Introduction
8.2 Simplified Preliminary Numerical Analysis
8.3 Experimental Analysis
8.4 Young's Modulus Estimation
8.5 Conclusion
References
9 Incorporating Uncertainty in Mechanics-Based Synthetic Data Generation for Deep Learning -Based Structural Monitoring
9.1 Introduction
9.2 Methodology
9.2.1 Structural Modeling
9.2.2 Uncertainty Assessment and Data Augmentation
9.2.3 Damage State Classification
9.3 Case Study of Experimental Concentrically Braced Frame
9.3.1 Experimental and Numerical Modeling
9.3.2 Damage Classification
9.4 Conclusions
References
10 Aerodynamic Load Estimation in Wind Turbine Drivetrains Using a Bayesian Data Assimilation Approach
10.1 Introduction
10.2 Bayesian Inference Method
10.3 Wind Turbine Drivetrain Model
10.4 Results
10.5 Conclusions
References
11 Rail Roughness Profile Identification from Vibration Data via Mixing of Reduced-Order Train Models and Bayesian Filtering
11.1 Introduction
11.2 Train-Track Interaction Model
11.3 Train Model Reduction
11.4 Rail Roughness Profile Identification
11.5 Numerical Application
11.6 Conclusion
References
12 Optimal Sensor Placement for Developing Reliable Digital Twins of Structures
12.1 Introduction
12.2 Information Gain Accounting for Uncertainties
12.3 Cost-Effective OSP for Multiple Monitoring Tasks
12.4 Conclusions
References
13 DataSEA: Mature, Modern Data Management Enabling Sustainable Data Strategy
13.1 Introduction
13.2 DataSEA
13.2.1 Flexible, Purposeful Data Architecture
13.2.2 A ``Self-Documenting'' Data Approach
13.2.3 Ensuring the Data Remains at the Forefront in Decision-Making
13.3 Conclusion
References
14 Optimal Sensor Placement Considering Operational Sensor Failures for Structural Health Monitoring Applications
14.1 Introduction
14.2 Problem Definition
14.3 Objective Function Focused on Reliable Sensor Measurements
14.4 Results
14.5 Conclusions
References
15 Sequential Harmonic Component Tracking for Underdetermined Blind Source Separation in a Multitarget Tracking Framework
15.1 Introduction
15.2 Related Works
15.3 Problem Formulation
15.4 Feature-Aided SMC-PHD for Harmonic Component Tracking
15.5 Multitarget Tracking to Unsupervised Multilabel Classification
15.6 Numerical Simulations and Discussion
15.7 Conclusions
References
16 Physics-Based Corrosion Reliability Analysis of Miter Gates Using Multi-scale Simulations and Adaptive Surrogate Modeling
16.1 Introduction
16.2 Surrogate Modeling of Macro-scale Structural Analysis Model
16.3 Adaptive Surrogate Modeling for Corrosion Reliability Analysis at Meso-scale
16.4 Results
References
17 Adaptive Randomized Sketching for Dynamic Nonsmooth Optimization
17.1 Introduction
17.2 Dynamic Optimization Problem Formulation
17.3 Low-Memory Matrix Approximation
17.4 Sketched Trust-Region Algorithm
17.5 Inexact Gradient Computation via Sketched State
17.6 Numerical Results
17.7 Conclusion
References
18 Predicting Nonlinear Structural Dynamic Response of ODE Systems Using Constrained Gaussian Process Regression
18.1 Introduction
18.2 Background
18.3 Methodology
18.4 Case Study
18.5 Conclusion
References
19 Probabilistic Model Updating for Structural Health Monitoring Using a Likelihood-Free Bayesian Inference Method
19.1 Introduction
19.2 Damage Detection Using a New Likelihood-Free Bayesian Inference Method
19.3 Case Study
19.4 Conclusion
References
20 Deep Learning for Image Segmentation and Subsurface Damage Detection Based on Full-Field Surface Strains
20.1 Introduction
20.2 Deep Learning Architecture
20.3 Dataset
20.4 Data Augmentation
20.5 Training and Hyperparameters
20.6 Hyperparameter Tuning
20.7 Predictions
20.8 Conclusion
References
21 A Spatio-Temporal Model for Response and Distributed Wave Load Estimation on Offshore Wind Turbines
21.1 Introduction
21.2 Problem Formulation
21.3 Gaussian Process Model
21.4 Case Study
21.5 Results
21.6 Conclusions
References
22 Identification of Axial Forces in Structural Rod Members Under Compression by a Modal Approach
22.1 Introduction
22.2 Description of the Tests
22.3 Modal Identification
22.4 Numerical Analyses
22.5 Conclusions
23 Digital Twin Output Functions and Statistical Performance Metrics for Engineering Dynamic Applications
23.1 Introduction
23.2 Types of Data
23.2.1 Digital Twin Output Functions
23.3 Comparing Experimental and Numerical Data/Metrics
23.4 Demonstration
23.4.1 Experimental Data
23.4.2 Numerical Data
23.4.3 Comparison
23.5 Remarks
References
24 Next-Generation Non-contact Strain-Sensing Method Using Strain-Sensing Smart Skin (S4) for Static and Dynamic Measurement
24.1 Introduction
24.2 Strain-Sensing Smart Skin (S4)
24.3 2D Static Strain Mapping
24.4 Single Point Dynamic Strain Measurement
24.5 Conclusion
References
25 Online Structural Model Updating for Ship Structures Considering Impact and FatigueDamage
25.1 Introduction
25.2 Background
25.3 Analysis
25.4 Conclusion
References
26 Detuning Optimization of Nonlinear Mistuned Bladed Disks Using a Probabilistic LearningTool
26.1 Introduction
26.2 Background
26.3 Analysis
26.4 Conclusion
References
27 Model-Based Inspection Planning for Large-Scale Structures Using Unmanned Aerial Vehicles
27.1 Introduction
27.2 Framework for Inspection and Maintenance Planning
27.3 Bayesian Optimization
27.4 Results
27.5 Conclusion
References
28 The Effect of Temporal Correlations on State Estimation Through Variational BayesianInference
28.1 Introduction
28.2 Methodology
28.3 Analysis
28.4 Conclusion
References
29 On the Selection and Validation of Component Damage Models for Prediction of Damage-State Behavior of a Truss Bridge
29.1 Introduction
29.2 Truss Bridge: Structure, Model, and Substructures
29.3 Submodel Validation
29.3.1 Experimental Data
29.3.2 Parameter Calibration
29.3.3 Model Selection and Validation
29.4 Assembly-Level Prediction
29.4.1 Experimental Data
29.4.2 Model Testing
29.5 Conclusions
References
30 Surrogate Aerodynamics Modeling Applied to Surrogate Structural Dynamical Systems
30.1 Introduction
30.2 Background
30.3 Process
30.4 Surrogate Pressure Model
30.5 Surrogate Mechanics Model
30.6 Conclusion
31 Footbridge Vibration Predictions and Interaction with Walking Load Model Decisions
Nomenclature
31.1 Introduction
31.2 Bridges Assumed for the Study
31.3 Modelling of Walking Loads
31.3.1 Basic Load Model Assumptions
31.3.2 Out-of-Range Considerations
31.4 Methodology
31.5 Results
31.5.1 Investigation I
31.5.2 Investigation II
31.5.3 Investigation III
31.6 Conclusion and Discussion
References
32 Assembling Uncertainty Effects on the Dynamic Response of Nominally Identical Motorbike Components
32.1 Introduction
32.2 Case Study
32.3 Experimental Setup
32.4 Experimental Modal Analysis
32.5 Conclusion
References
Correction to: Assembling Uncertainty Effects on the DynamicResponse of Nominally Identical Motorbike Components

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Model Validation and Uncertainty Quantification, Volume 3

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