Model Validation and Uncertainty Quantification, Volume 3

Preface	6
Contents	7
1 Nondestructive Consolidation Assessment of Historical Camorcanna Ceilings by Scanning Laser Doppler Vibrometry	10
1.1 Introduction	10
1.2 Camorcanna Vaults Structure	11
1.3 Case Study: Paintings in Greppi's Manor Vault	11
1.4 Testing Equipment and Measurement Set-Up	12
1.5 Experimental FRF Analysis	14
1.6 Modal Analysis	14
1.7 FRF Analysis	16
1.8 Conclusions	16
References	19
2 The Need for Credibility Guidance for Analyses Quantifying Margin and Uncertainty	20
2.1 Introduction	20
2.1.1 What Is QMU	20
2.1.2 Why Measure Credibility?	21
2.1.3 History of Credibility in CompSim	22
2.2 Important Elements for QMU Credibility	22
2.2.1 Requirement Definition and QoI Selection	23
2.2.2 Data Quality	23
2.2.3 Model Uncertainty	24
2.2.4 Calibration/Parameter Estimation	25
2.2.5 Validation	25
2.3 Evaluating Credibility	26
2.4 Example Application	26
2.4.1 Requirement Definition and QoI Selection	26
2.4.2 Data Quality	27
2.4.3 Model Uncertainty	28
2.4.4 Calibration/Parameter Estimation	28
2.4.5 Validation	29
2.4.6 Summary of Credibility Assessment	29
2.5 Summary	31
References	31
3 Failure Behaviour of Composites Under Both Vibration Loading and Environmental Conditions	33
3.1 Introduction	33
3.2 Experimental and Numerical Methods	34
3.2.1 Experimental Procedure	34
3.2.2 Experimental Results	34
3.2.3 Numerical Approach	37
3.2.4 Analytical Results	38
3.3 Conclusion	38
References	39
4 Verification and Validation for a Finite Element Model of a Hyperloop Pod Space Frame	41
4.1 Introduction	41
4.2 Frame Design	41
4.3 Verification and Validation	42
4.4 Mesh Verification	42
4.5 Modal Testing and Model Calibration	43
4.5.1 Test Setup	43
4.5.2 Results	43
4.5.3 Discussion of Errors	43
4.6 Torsional Testing and Model Validation	44
4.6.1 Test Setup	44
4.6.2 Results	45
4.6.3 Discussion of Errors	45
4.7 Conclusion	47
References	48
5 Investigating Nonlinearities in a Demo Aircraft Structure Under Sine Excitation	49
5.1 Introduction	49
5.2 Description of the Test Item	50
5.3 Experimental and Numerical Analysis of the Aircraft	51
5.3.1 Preliminary FE Model	51
5.3.2 Experimental Modal Analysis	52
5.3.3 Plane Model Validation and Updating	54
5.4 Modal Identification of the Aircraft with the Pylons	55
5.5 Nonlinear Identification	58
5.5.1 Nonlinear Detection Based on Time Series and FRF Inspection	59
5.5.2 Best Linear Approximation Estimation with Oddmultisines Excitation	61
5.5.3 Best Linear Approximation Estimation with Sine-Sweep Excitation	62
5.5.4 Nonlinear Characterisation	63
5.6 Conclusion	64
References	64
6 Sensor Placement for Multi-Fidelity Dynamics Model Calibration	66
6.1 Introduction	66
6.2 Background	67
6.2.1 Damping Calibration	67
6.2.2 Bayesian Calibration	67
6.2.3 Kullback–Leibler Divergence	68
6.3 Sensor Location Optimization	68
6.4 Numerical Example	68
6.4.1 Problem Description	68
6.4.2 Sensor Location Optimization Results	70
6.5 Conclusion	70
References	71
7 Application of Cumulative Prospect Theory to Optimal Inspection Decision-Making for Ship Structures	72
7.1 Introduction	72
7.2 Cumulative Prospect Theory	73
7.3 Reliability Assessment	74
7.3.1 Limit State Function	74
7.3.2 Ultimate Bending Capacity	74
7.3.3 Still Water Bending Moment	75
7.3.4 Wave-Induced Bending Moment	75
7.4 Ship Life-Cycle Cost	76
7.5 Optimization Framework	76
7.5.1 Repair Policy	76
7.5.2 Inspection Optimization	77
7.6 Case Study	77
7.7 Conclusions	80
References	80
8 Establishing an RMS von Mises Stress Error Bound for Random Vibration Analysis	82
8.1 Introduction	82
8.2 Theory	83
8.2.1 Theoretical Development of Acceleration Error	84
Reduction to Linear Algebra	84
8.3 Analysis Examples	85
8.3.1 Case 1: Free–Free Beam	85
8.3.2 Case 2: Cantilever Beam Random Vibration	86
8.3.3 Case 3: Combined Beam Random Vibration	90
8.4 Evaluation	93
8.5 Conclusion	95
Appendix 1	96
Appendix 2	97
References	98
9 A Neural Network Surrogate Model for Structural Health Monitoring of Miter Gatesin Navigation Locks	99
9.1 Introduction	99
9.2 Finite Element Modeling	100
9.3 Multi-Layer Artificial Neural Network	101
9.3.1 Preliminary Design	101
9.3.2 Extended Design	101
9.3.3 Cross Verification	103
9.4 Conclusion and Further Work	103
References	104
10 Model Validation Strategy and Estimation of Response Uncertainty for a Bolted Structure with Model-Form Errors	105
10.1 Introduction	105
10.2 Numerical Error Quantification	106
10.3 Model Form Error Estimation (model)	107
10.4 Analysis	107
10.5 Conclusion	111
References	111
11 Characteristic Analysis of Modified Dolly Test: A Sensitivity Study of Initial Conditions on Rollover Outcomes	112
11.1 Introduction	112
11.2 Dynamic Model of the Bus	114
11.3 Sensitivity Analysis Using a Surrogate Model	115
11.4 Summary and Conclusion	117
Appendix	117
References	120
12 Input Estimation of a Full-Scale Concrete Frame Structure with Experimental Measurements	121
12.1 Introduction	121
12.2 Input Estimation Algorithm	122
12.3 Test Structure	123
12.4 Validation of Input Estimation Algorithm	125
12.4.1 Input Estimation with Simulated Measurement	125
12.4.2 Input Estimation with Experimental Measurement	126
12.5 Conclusions	128
References	129
13 Bayesian Estimation of Acoustic Emission Arrival Times for Source Localization	130
13.1 Introduction	130
13.2 Experimental Data	131
13.3 Methodology	131
13.3.1 Automatic Onset Detection Algorithms	131
Floating Threshold	132
AIC Picker	132
The Reciprocal-Based Picker	132
13.3.2 The Shortest Path Model	133
13.3.3 The Proposed Bayesian Picker	133
13.4 Results	134
13.5 Concluding Remark	135
References	136
14 Quantification and Evaluation of Parameter and Model Uncertainty for Passive and Active Vibration Isolation	137
14.1 Introduction	137
14.2 System Description	138
14.2.1 Linear Mathematical Dynamic Model of the One Mass Oscillator for Passive and Active Vibration Isolation	138
14.2.2 Realization of the Test Rig	139
14.2.3 Variation of the Input Parameters	141
14.3 Quantification of Model Uncertainty	143
14.3.1 Quantification of Uncertainty with the Area Validation Metric	143
Passive Vibration Isolation	144
Active Vibration Isolation	145
Comparison of the Model Uncertainty for Passive and Active Vibration Isolation	145
14.3.2 Quantification of Model Uncertainty with a Bayesian Approach	146
Passive Vibration Isolation	146
Active Vibration Isolation	147
Comparison of the Model Uncertainty for Passive and Active Vibration Isolation	147
14.4 Conclusion	148
References	148
15 Bayesian Model Updating of a Five-Story Building Using Zero-Variance Sampling Method	150
15.1 Introduction	150
15.2 Analysis	151
References	152
16 Input Estimation and Dimension Reduction for Material Models	153
16.1 Introduction	153
16.2 Physical Measurements	154
16.3 Computer Model Simulation	154
16.4 Methodology	156
16.4.1 Bayesian Statistics and Estimation	156
16.4.2 Emulation	157
16.4.3 Sensitivity Analysis	157
16.5 Results	157
16.6 Conclusions and Future Work	160
References	161
17 Augmented Sequential Bayesian Filtering for Parameter and Modeling Error Estimation of Linear Dynamic Systems	162
17.1 Introduction	162
17.2 Methodology	163
References	164
18 On-Board Monitoring of Rail Roughness via Axle Box Accelerations of Revenue Trains with Uncertain Dynamics	165
18.1 Introduction	165
18.2 Description of the Method	166
18.3 Numerical Implementation	167
18.4 Conclusions	168
References	169
19 Bayesian Identification of a Nonlinear Energy Sink Device: Method Comparison	170
19.1 Introduction	170
19.2 Background	171
19.3 Analysis	172
19.4 Conclusion	172
References	172
20 Calibration of a Large Nonlinear Finite Element Model of a Highway Bridge with Many Uncertain Parameters	173
20.1 Introduction	173
20.2 FE Model Updating	174
20.3 Application Example: Marga-Marga Bridge	174
20.3.1 Description of the Structure, FE Model, and Input Motion	174
20.3.2 Response Simulation	176
20.3.3 Sensitivity Analysis	178
20.3.4 Estimation of Model Parameters	178
20.3.5 Response Prediction Errors	181
20.3.6 Computational Cost	181
20.4 Conclusions	183
References	183
21 Deep Unsupervised Learning for Condition Monitoring and Prediction of High Dimensional Data with Application on Windfarm SCADA Data	184
21.1 Introduction	184
21.2 Description of the Method	185
21.2.1 Simulated SCADA Dataset	185
21.2.2 Variational Autoencoder	185
21.2.3 Sampling from the Trained Model	186
21.2.4 Using the Joint Distribution for Predictions	188
21.2.5 Using the VAE for Probabilistic Condition Monitoring	188
21.3 Conclusions	189
References	191
22 Influence of Furniture on the Modal Properties of Wooden Floors	192
22.1 Introduction	192
22.2 Computational Model for Wooden Floor with Furniture	193
22.2.1 Finite-Element Model of Rectangular Wooden Floor	193
22.2.2 Stochastic Model of Elevated Non-structural Mass	195
22.3 Analysis of a Rectangular Wooden Floor with Furnishing	195
22.3.1 Case 1: Non-structural Mass Placed Over the Joists	195
22.3.2 Case 2: Non-structural Mass Placed Between the Joists	197
22.4 Discussion and Conclusion	198
References	199
23 Optimal Sensor Placement for Response Reconstruction in Structural Dynamics	200
23.1 Introduction	200
23.2 Background	201
23.3 Optimal Sensor Placement Formulation	203
23.4 Conclusion	204
References	204
24 Finite Element Model Updating Accounting for Modeling Uncertainty	206
24.1 Introduction	206
24.2 Problem Formulation	206
24.2.1 Parameter-Only Estimation Based on the UKF	207
24.2.2 Dual Approach Accounting for Modeling Uncertainty	208
24.3 Validation Study	208
24.3.1 Cases of Modeling Uncertainty	210
Earthquake Input Motions	211
FE Model Updating Results	212
24.4 Conclusion	215
References	216
25 Model-Based Decision Support Methods Applied to the Conservation of Musical Instruments: Application to an Antique Cello	217
25.1 Introduction	217
25.2 Analysis	218
25.3 Results	219
25.4 Conclusion	221
References	221
26 Optimal Sensor Placement for Response Predictions Using Local and Global Methods	222
26.1 Introduction	222
26.2 OED Formulation for Response Predictions	223
26.3 Global Sampling-Based Approach	225
26.4 Local Sensitivity-Based Approach	225
26.5 Application: Simple Linear Model	226
26.5.1 Comparison with Monte Carlo Integration	227
26.5.2 Sensitivity-Based Method	229
26.6 Conclusions	229
References	229
27 Incorporating Uncertainty in the Physical Substructure During Hybrid Substructuring	230
27.1 Introduction	230
References	232
28 Applying Uncertainty Quantification to Structural Systems: Parameter Reduction for Evaluating Model Complexity	233
28.1 Introduction	233
28.2 Modular Active Spring-Damper System Description	235
28.3 Stiffness Regression Models	237
28.3.1 Piecewise Linear Polynomials	238
28.3.2 Cubic Polynomial	238
28.3.3 Piecewise Power Functions	239
28.3.4 Elastic Foot Stiffness	240
28.4 Damping Regression Models	240
28.4.1 Piecewise Linear Polynomials	241
28.4.2 Cubic Polynomial	242
28.4.3 Elastic Foot Damping	242
28.5 Solving the Equation of Motion	242
28.6 Uncertainty Quantification	244
28.6.1 Sensitivity Analysis	244
28.6.2 Uncertainty Quantification Frameworks	246
References	247
29 Non-unique Estimates in Material Parameter Identification of Nonlinear FE Models Governed by Multiaxial Material Models Using Unscented Kalman Filtering	249
29.1 Introduction	249
29.2 FE Model Updating as Parameter-Only Estimation Problem	250
29.3 Bayesian Parameter Estimation	250
29.3.1 Unscented Kalman Filtering	251
29.4 Application Example	251
29.4.1 Simulation	253
29.4.2 Estimation	254
29.4.3 Validation	256
29.5 Conclusions	256
References	257
30 On Key Technologies for Realising Digital Twins for Structural Dynamics Applications	258
30.1 Introduction	258
30.2 Building a Digital Twin	259
30.2.1 Objectives of a Digital Twin	259
30.2.2 Example Layout of Simulation Digital Twin	260
30.2.3 Data-Augmented Modelling	261
30.2.4 Numerical Example	261
30.2.5 Implications for Digital Twin Technology	262
30.3 Conclusions	263
References	263
31 Hygro-mechanical Modelling of Wood and Glutin-based Bond Lines of Wooden CulturalHeritage Objects	264
31.1 Introduction	264
31.2 Methods	264
31.3 Results and Discussion	265
31.4 Conclusion and Outlook	266
References	266
32 Modelling of Sympathetic String Vibrations in the Clavichord Using a Modal Udwadia-Kalaba Formulation	268
32.1 Introduction	268
32.2 Model U-K	269
32.3 Results and Conclusion	269
References	271
33 Modeling and Stochastic Dynamic Analysis of a Piezoelectric Shunted Rotating Beam	272
33.1 Introduction	272
33.2 Background	273
33.3 Analysis	273
33.4 Conclusion	274
References	274
34 On Digital Twins, Mirrors and Virtualisations	275
34.1 Introduction	275
34.2 Mirrors	277
34.2.1 Basic Definitions	277
34.2.2 Hybrid Models and Uncertainty	278
34.2.3 The Environment and Virtualisation	279
34.2.4 The Turing Mirror	279
34.3 Examples	280
34.3.1 A Simple Example: Context Change	280
34.3.2 An Example Concerning Assembly	281
34.3.3 An Example Concerning Structural Health Monitoring	282
34.3.4 Multi-Fidelity Models: Refinement and Relaxation	284
34.3.5 An Example Concerning Design	284
34.4 Discussion and Conclusions	284
References	285
35 Applications of Reduced Order and Surrogate Modeling in Structural Dynamics	286
35.1 Introduction	286
35.2 Reduced Order Modeling in Seismic Loss Assessment	287
35.3 Surrogate Modeling for Posterior Sampling	287
References	288

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