E-Book, Englisch, Band 270, 1078 Seiten, eBook
Rizzo / Milazzo European Workshop on Structural Health Monitoring
1. Auflage 2022
ISBN: 978-3-031-07322-9
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark
EWSHM 2022 - Volume 3
E-Book, Englisch, Band 270, 1078 Seiten, eBook
Reihe: Lecture Notes in Civil Engineering
ISBN: 978-3-031-07322-9
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
1;Preface;6
2;Organization;12
2.1;Program Chairs;12
2.2;Program Committee;12
3;Contents;13
4;Guided Waves in Structures for SHM;24
5;Numerical Investigations on the Influence of Prestress on Lamb Wave Propagation;25
5.1;1 Introduction;25
5.2;2 Modeling Approach and Mechanical Description;26
5.2.1;2.1 Modeling Approach;26
5.2.2;2.2 Mechanical Description;27
5.2.3;2.3 Dispersion Diagrams via the Eigenvalue Problem;29
5.3;3 Experimental and Numerical Results;30
5.3.1;3.1 Dispersion Diagram Without Prestress;31
5.3.2;3.2 Influence of Prestress in Experimental and Numerical Data;31
5.4;4 Conclusion and Outlook;33
5.5;References;33
6;Damage Assessment in Composite Material Using Air-Coupled Transducers;35
6.1;Abstract;35
6.2;1 Introduction;35
6.3;2 Experimental Set-Up;36
6.4;3 Non-contact ACT-Based Elastic Waves Generation;37
6.5;4 Dispersion Curves Extraction;38
6.6;5 Teflon Inserts Localization Results;39
6.6.1;5.1 Full Wavefield Results - Piezoelectric Transducer;40
6.6.2;5.2 Full Wavefield Results - Air-Coupled Transducers;40
6.7;6 Conclusions;43
6.8;Acknowledgements;44
6.9;References;44
7;A Numerical Study on Baseline-Free Damage Detection Using Frequency Steerable Acoustic Transducers;46
7.1;Abstract;46
7.2;1 Introduction;46
7.3;2 Damage Detection with Frequency Steerable Acoustic Transducers;47
7.4;3 Numerical Simulations;49
7.4.1;3.1 Numerical Model;49
7.4.2;3.2 FSAT Directivity Characteristics;49
7.4.3;3.3 Damage Scenarios;50
7.5;4 Results of Damage Detection and Localization;51
7.6;5 Conclusions and Future Plans;54
7.7;Acknowledgments;54
7.8;References;55
8;Influence of Operational and Environmental Conditions on Lamb Wave Signals;56
8.1;Abstract;56
8.2;1 Introduction;56
8.3;2 Measurement Set-Up;57
8.4;3 Evaluation of Measured Data;58
8.4.1;3.1 Temperature Effect;58
8.4.2;3.2 Damage Effect;62
8.5;4 Conclusion;64
8.6;Acknowledgment;64
8.7;References;65
9;Use of Deep Learning Techniques for Damage Localization in Aeronautical Composite Structures;66
9.1;Abstract;66
9.2;1 Introduction;66
9.3;2 Methods and Experimental Setup;67
9.3.1;2.1 Convolutional Neural Network for Distances Estimation;67
9.3.2;2.2 Damage Localization Algorithm;68
9.3.3;2.3 Experimental Setup;69
9.3.4;2.4 Additional Data Acquisition for Testing;71
9.4;3 Results;71
9.4.1;3.1 CNN Model Training;71
9.4.2;3.2 Damage Localization and Imaging Results;72
9.5;4 Conclusion;73
9.6;References;73
10;Temperature and Damage-Affected Lamb Wave Signals in a Composite Sandwich Plate;74
10.1;Abstract;74
10.2;1 Introduction;74
10.3;2 Materials and Methods;75
10.4;3 Results;76
10.4.1;3.1 Climatic Chamber;76
10.4.2;3.2 Temperature and Damage Affected Signals;78
10.4.3;3.3 Impact Damage;78
10.5;4 Discussion;81
10.6;5 Conclusion;81
10.7;Acknowledgments;81
10.8;References;81
11;Numerical Investigation of Application of Unidirectional Generation to Improve Signal Interpretation of Circumferential Guided Waves in Pipes for Defect Detection;83
11.1;Abstract;83
11.2;1 Introduction;83
11.3;2 Circumferential SH Waves and Unidirectional Generation;85
11.3.1;2.1 Circumferential SH Waves;85
11.3.2;2.2 Unidirectional Generation of SH Waves;85
11.3.3;2.3 Reflection from a Defect;86
11.4;3 Finite Element Model;87
11.5;4 Results and Discussion;88
11.6;5 Conclusion;91
11.7;Acknowledgments;91
11.8;References;91
12;Guided Waves Benchmark Dataset and Classifier Comparison;93
12.1;1 Introduction;93
12.2;2 Benchmark Dataset Description;94
12.2.1;2.1 General Description and Experimental Setup;94
12.2.2;2.2 Aircraft Specimens Fatigue Subset (Section A);94
12.2.3;2.3 Variable Environmental Conditions Subset (Section B);95
12.2.4;2.4 Composite Specimens Subset (Section C);95
12.3;3 Examples of Testing Approaches;97
12.3.1;3.1 Fatigue Specimen Classification Example;97
12.3.2;3.2 Composite Specimen Localization Example;98
12.4;4 Research Suggestions and Future of the Dataset;98
12.5;5 Summary and Conclusions;100
12.6;References;100
13;Dual Mode Inspection Using Guided Waves and Phased Array Ultrasonics from a Single Transducer;101
13.1;1 Introduction;101
13.2;2 Modal Analysis Formulation;102
13.3;3 Guided Wave Excitation with a Phased Array;104
13.4;4 Simulation Results;105
13.5;5 Experimental Results;107
13.5.1;5.1 Experimental 2DFFT;107
13.5.2;5.2 Guided Wave Scan;107
13.5.3;5.3 Phased Array Inspection;109
13.6;6 Conclusions;109
13.7;References;110
14;Enhanced Simulation of Guided Waves and Damage Localization in Composite Strips Using the Multiresolution Finite Wavelet Domain Method;111
14.1;Abstract;111
14.2;1 Introduction;111
14.3;2 Theoretical Background;113
14.3.1;2.1 Daubechies Wavelets and the Multiresolution Approximation;113
14.3.2;2.2 The Multiresolution Finite Wavelet Domain Method;114
14.3.3;2.3 High-order Layerwise Laminate Theory;115
14.4;3 Numerical Results;116
14.4.1;3.1 Composite Beam with First-order Shear Kinematic Assumptions;116
14.4.2;3.2 Composite Beam Using HLLT;117
14.5;4 Conclusions;119
14.6;References;120
15;Fully Integrated Hybrid “Piezoelectric/Fiber Optic” Acousto-Ultrasound Sensor Network (FAULSense™) SHM System;122
15.1;Abstract;122
15.2;1 Introduction;122
15.2.1;1.1 Advanced Composites in Aircraft Structures;125
15.3;2 Integrated Hybrid Structural Health Monitoring (SHM) System;125
15.4;3 FAULSense System Development and Test Results;127
15.5;4 Conclusions;130
15.6;Acknowledgements;130
15.7;References;130
16;Guided Wave-Gaussian Mixture Model for Damage Quantification Under Uncertainty;131
16.1;Abstract;131
16.2;1 Introduction;131
16.3;2 GW-GMM Based Crack Quantification Method;132
16.3.1;2.1 The GW-GMM Based Crack Quantification Process;132
16.3.2;2.2 Modeling and Migration Process of GMM;133
16.4;3 Validation on the Aircraft Lug Specimen;135
16.4.1;3.1 Experimental Setup;135
16.4.2;3.2 GW Features Extraction Under Uncertainty;136
16.4.3;3.3 Calibration Model of Crack Length with MI;136
16.4.4;3.4 Online Damage Quantitative Diagnosis Results;138
16.5;4 Conclusion;138
16.6;Acknowledgements;138
16.7;References;139
17;Damage Size Quantification Using Lamb Waves by Analytical Model Identification;141
17.1;Abstract;141
17.2;1 Introduction;141
17.3;2 Analytical Model;143
17.3.1;2.1 Actuator Model;143
17.3.2;2.2 Scattering Model;144
17.4;3 Sensor Model;147
17.5;4 Identification Algorithm;147
17.6;5 Application on Simulation Data;148
17.7;6 Conclusion;149
17.8;References;149
18;Efficient Layerwise Time-Domain Spectral Finite Element for Guided Wave Propagation Analysis of Multi-layered Panels;150
18.1;1 Introduction;150
18.2;2 Methodology;152
18.3;3 Numerical Results and Discussion;153
18.3.1;3.1 Free Vibration Analysis;153
18.3.2;3.2 Lamb Wave Propagation;154
18.3.3;3.3 Comparison of SFE with Conventional FE;157
18.3.4;3.4 Lamb Wave Propagation Under Transverse Excitation;157
18.4;4 Conclusions;158
18.5;References;159
19;A Bayesian Approach to Lamb-Wave Dispersion Curve Material Identification in Composite Plates;161
19.1;1 Introduction;161
19.2;2 Methods;163
19.2.1;2.1 Dispersion Curve Solutions for Orthotropic Media;163
19.2.2;2.2 Measuring {,};164
19.2.3;2.3 Estimating Elastic Constants;164
19.2.4;2.4 Sampling over the Posterior Distribution;165
19.3;3 Results;166
19.4;4 Conclusions and Further Work;169
19.5;References;170
20;Damage Detection Based on Voltage Transfer Ratio Approach and Bayesian Classifier;172
20.1;1 Introduction;172
20.2;2 Bayesian Decision Model;173
20.3;3 Experiment Description and Experimental Data Evaluation;177
20.4;4 Summary;181
20.5;References;181
21;Remote Excitation Ultrasonic Waveguide-Based SHM for Critical Applications;182
21.1;Abstract;182
21.2;1 Introduction;182
21.2.1;1.1 Structural Health Monitoring (SHM);182
21.2.2;1.2 Ultrasonic Guided Wave-Based Waveguide;183
21.3;2 Experimental Setup;184
21.3.1;2.1 Design of Waveguide;184
21.4;3 SHM of Plate Structure;185
21.4.1;3.1 Generation of S0 Mode in Plate;185
21.4.2;3.2 Generation of SH0 Mode in Plate;186
21.5;4 Fem Simulation Studies;188
21.6;5 Conclusion;190
21.7;References;190
22;Development of GUI Based Tool for the Visualization of the FBG Spectrum Subjected to Guided Waves;192
22.1;Abstract;192
22.2;1 Introduction;192
22.3;2 Models Studied and Calculation Process Used;193
22.3.1;2.1 Numerical Settings;193
22.3.2;2.2 FBG Based Software;195
22.3.3;2.3 Theoretical Calculations and Background;195
22.4;3 GUI Matlab Based Software;197
22.4.1;3.1 FBG Strain Calculations;197
22.4.2;3.2 Validation;199
22.5;4 Conclusion;200
22.6;Acknowledgements;200
22.7;References;200
23;Weld Defect Location Method of U-Shaped Crane Boom Based on Helical Guided Waves;202
23.1;Abstract;202
23.2;1 Introduction;202
23.3;2 Theoretical Background;203
23.3.1;2.1 Elliptical Imaging Algorithm;203
23.3.2;2.2 Intelligent Defect Location Algorithm;205
23.4;3 Propagation Characteristics of Helical Guided Waves in U-shaped Boom;208
23.5;4 Experimental Validation;210
23.6;5 Conclusion;215
23.7;6 Declaration of Conflicting Interests;215
23.8;Funding;216
23.9;References;216
24;The Effect of the Infill Density in 3D-Printed PLA on Lamb Waves’ Propagation Characteristics and their Sensitivity to the Presence of Damage;217
24.1;Abstract;217
24.2;1 Introduction;217
24.3;2 Experiment Setup;218
24.4;3 Dispersion Analysis;220
24.5;4 Damage Imaging;222
24.6;5 Conclusions;224
24.7;Acknowledgments;224
24.8;References;224
25;Monitoring Dendrite Formation in Aqueous Zinc Batteries with SH0 Guided Waves;226
25.1;Abstract;226
25.2;1 Measurement Principle and Experimental Setup;227
25.2.1;1.1 Measurement Principle;227
25.2.2;1.2 Experimental Setup;227
25.2.3;1.3 Measurement Protocol;228
25.3;2 Results;229
25.3.1;2.1 Optical Results;229
25.3.2;2.2 Ultrasonic Results;229
25.4;3 Discussion;230
25.5;4 Conclusion;232
25.6;References;232
26;Stress Monitoring of Plates by Means of Nonlinear Guided Waves;234
26.1;1 Introduction;234
26.2;2 Equations of Motion;235
26.3;3 Finite Element Simulations;236
26.4;4 The Second-Harmonic Resonant Wave;238
26.4.1;4.1 Dependence of the Response on the Initial State of Prestress;239
26.4.2;4.2 Resonance Condition and Interaction Between Primary and Secondary Waves;240
26.5;5 Conclusion;241
26.6;References;242
27;Assessment of Stringer Debonding by Guided Wave Inspection in Composite Structures;243
27.1;1 Introduction;243
27.2;2 Introduction;244
27.2.1;2.1 Propagation Model;244
27.2.2;2.2 Damage Detection Approach;246
27.2.3;2.3 Numerical Model;246
27.3;3 Results;248
27.4;4 Discussion;250
27.5;5 Concluding Remarks;251
27.6;References;251
28;Computational Lamb Wave Analysis of a CFRP Shortened Curing Cycle;253
28.1;Abstract;253
28.2;1 Introduction;253
28.3;2 Experimental Cure Monitoring;255
28.3.1;2.1 Ultrasonic Cycle Shortening;255
28.3.2;2.2 Dynamic Mechanical Analysis;256
28.4;3 The Computational Model;257
28.4.1;3.1 Combined Modules;257
28.4.2;3.2 Results and Discussion;259
28.5;4 Conclusion;261
28.6;References;262
29;Guided Wave-Based Assessment of Bonded Composite Joints;264
29.1;1 Introduction;264
29.2;2 Fabrication of Composite Coupons;265
29.3;3 Guided Wave-Based Assessment of Kissing Bonds;266
29.3.1;3.1 Experimental Setup;267
29.3.2;3.2 Residual Bond Strength Characterization;268
29.4;4 NDE Results and Comparison with Destructive Tests;271
29.5;5 Conclusions;272
29.6;References;272
30;Modeling Magnetostrictive Transducers for SH Guided Wave Generation and Reception for Structural Health Monitoring;274
30.1;Abstract;274
30.2;1 Introduction;274
30.3;2 Model Setup;275
30.4;3 Modeling Results and Discussion;276
30.4.1;3.1 Mode Control Plot;276
30.4.2;3.2 Lift-Off Distance Effect;277
30.4.3;3.3 Adhesive Layer Effect;278
30.4.4;3.4 Two-Sided Transducer;280
30.5;4 Conclusions;281
30.6;References;282
31;Experimental Identification of Damage in Single Lap Joint;283
31.1;1 Introduction;283
31.2;2 Materials and Methods;285
31.2.1;2.1 Experimental Setup;285
31.2.2;2.2 Analytical Model;286
31.3;3 Results and Discussion;288
31.4;4 Conclusions;291
31.5;References;291
32;Damage Detection in Rods via Use of a Genetic Algorithm and a Deep-Learning Based Surrogate;294
32.1;1 Introduction;294
32.2;2 Cracked Rod Spectral Element Model;295
32.3;3 Deep-Learning-Based Surrogate Model;296
32.4;4 Genetic Algorithm for Damage Detection;296
32.4.1;4.1 Selection;297
32.4.2;4.2 Crossover;298
32.4.3;4.3 Mutation;299
32.5;5 Damage Detection Results;299
32.6;6 Conclusions;300
32.7;References;301
33;Ultrasonic Damage Assessment Using Virtual Time Reversal Indices and the RAPID Method;303
33.1;Abstract;303
33.2;1 Introduction;303
33.3;2 Theoretical Framework;304
33.4;3 Experimental Set-Up;308
33.5;4 Ultrasonic Imaging Results;309
33.6;5 Conclusions;312
33.7;References;312
34;Improved Detection of Localized Damage in Pipe-Like Structures Using Gradient-Index Phononic Crystal Lens;314
34.1;Abstract;314
34.2;1 Introduction;314
34.3;2 Methods and Models;315
34.4;3 Results and Discussion;316
34.5;4 Conclusions;320
34.6;Acknowledgment;320
34.7;References;320
35;Machine Learning and Modelling in Structural Health Monitoring;322
36;Optimized Electromechanical Impedance Spectroscopy Using Minimal Number of Test Frequencies;323
36.1;1 Introduction;323
36.2;2 Materials and Methods;324
36.2.1;2.1 Experimental Setup for Impedance Measurements up to 100 kHz;324
36.2.2;2.2 Measuring Protocol for the Aluminum Plate with Different Size Drilled Holes;325
36.2.3;2.3 Statistical Analysis of the Conductance Data;325
36.3;3 Results;327
36.3.1;3.1 Distance-Based Clustering of the Data;327
36.3.2;3.2 Tree Classifiers for Semi-supervised Learning;328
36.4;4 Discussion;329
36.5;5 Conclusion;330
36.6;References;330
37;A New Unsupervised Learning Approach for CWRU Bearing State Distinction;332
37.1;Abstract;332
37.2;1 Introduction;332
37.3;2 CWRU Bearing Dataset;334
37.4;3 Proposed Approach;335
37.4.1;3.1 Data Selection;336
37.4.2;3.2 Measurement Segmentation;336
37.4.3;3.3 Segment Transformation;337
37.4.4;3.4 Feature Extraction;337
37.4.5;3.5 Feature Clustering;337
37.5;4 Results;338
37.6;5 Conclusion;338
37.7;References;339
38;Machine Learning Based Predictive Modelling of a Steel Railway Bridge for Damage Modelling of Train Passages and Different Usage Scenarios;340
38.1;1 Introduction;340
38.2;2 Methodology;341
38.2.1;2.1 Data Gathering and Processing;341
38.2.2;2.2 Train Passage Data Set;342
38.2.3;2.3 Predictive Model;343
38.3;3 Results;344
38.4;4 Modelling Usage Scenarios;345
38.4.1;4.1 Train Speed Scenario;345
38.4.2;4.2 Train Configuration Scenario;347
38.5;5 Conclusion;348
38.6;References;348
39;Damage Detection in Composites by AI and High-Order Modelling Surface-Strain-Displacement Analysis;350
39.1;1 Introduction;350
39.2;2 Refined 1D Models;351
39.3;3 Damage Formulation;352
39.4;4 CNN for Damage Detection;352
39.4.1;4.1 CNN Architecture;352
39.4.2;4.2 CNN Training Process;353
39.5;5 Numerical Results;353
39.6;6 Conclusions;356
39.7;References;357
40;Adding Autonomy to Robotic Enabled Sensing;358
40.1;Abstract;358
40.2;1 Introduction;358
40.3;2 Autonomous Pose Correction;359
40.3.1;2.1 Sensor Orientation;360
40.3.2;2.2 Sensor Standoff;362
40.4;3 Autonomous Geometry Discovery;362
40.4.1;3.1 On-Line Computation of Next-Best-Pose;362
40.4.2;3.2 Progressive Mesh Growth;363
40.4.3;3.3 Inspection Confinement and Collision Avoidance;364
40.4.4;3.4 Termination Criteria;365
40.5;4 Simulations;365
40.6;5 Conclusions;367
40.7;Acknowledgements;367
40.8;References;367
41;Anomaly Detection in Vibration Signals for Structural Health Monitoring of an Offshore Wind Turbine;368
41.1;1 Introduction;368
41.2;2 Case Presentation and Data Exploration;369
41.3;3 Methodologies and Results;372
41.3.1;3.1 Evaluation Metric;372
41.3.2;3.2 Baselines Models;373
41.3.3;3.3 Proposed Approach;373
41.4;4 Conclusion;377
41.5;References;377
42;Global Health Assessment of Structures Using NDT and Machine Learning;379
42.1;Abstract;379
42.2;1 Introduction;379
42.3;2 Need for Structural Health Assessment;380
42.4;3 Advances in SHA;381
42.5;4 Case Study: Assessment of Global Behaviour of RCC Building Using NDT and ML;381
42.6;5 Global Behaviour of the Structure Using Traditional Method;382
42.6.1;5.1 Methodology;382
42.6.2;5.2 Visual Inspection;383
42.6.3;5.3 Condition Mapping;384
42.6.4;5.4 Non-Destructive Testing;385
42.6.5;5.5 Numerical Modelling and Analysis;386
42.7;6 Global Behaviour Through Machine Learning;387
42.7.1;6.1 Data Collection;387
42.7.2;6.2 Support Vector Machine (SVM);387
42.7.3;6.3 Results and Discussion;388
42.8;7 Conclusion;389
42.9;References;389
43;A Review on Technological Advancements in the Field of Data Driven Structural Health Monitoring;391
43.1;Abstract;391
43.2;1 Introduction;391
43.3;2 Structural Health Monitoring Using Data Driven;393
43.3.1;2.1 Vibration Based Structural Health Monitoring;393
43.3.2;2.2 Vision Based Structural Health Monitoring;396
43.4;3 Conclusions;398
43.5;References;398
44;Physical Covariance Functions for Dynamic Systems with Time-Dependent Parameters;401
44.1;1 Introduction;401
44.2;2 Deriving Physical Covariance Functions;403
44.2.1;2.1 Uninformed Gaussian Process Priors;403
44.2.2;2.2 Towards Physically Representative Covariance Functions;404
44.2.3;2.3 Handling Parameters with a Time-Dependency;405
44.3;3 A Comparison Between Kernel Performance;406
44.4;4 Concluding Remarks;410
44.5;References;410
45;Damage Assessment of an Aircraft’s Wing Spar Using Gaussian Process Regressors;412
45.1;Abstract;412
45.2;1 Introduction;412
45.3;2 The Finite Element Model;413
45.4;3 Damage Identification Framework and Results;415
45.4.1;3.1 Using Strain to Infer Damage Parameters;415
45.4.2;3.2 Assessing Damage Criticality;417
45.5;4 Conclusions;419
45.6;References;419
46;Experimental Damage Localization and Quantification with a Numerically Trained Convolutional Neural Network;421
46.1;Abstract;421
46.2;1 Introduction;421
46.3;2 Database Computation;422
46.4;3 Networks Architectures;424
46.5;4 Networks Evaluation;426
46.6;5 Conclusion;426
46.7;References;427
47;On the Application of Partial Domain Adaptation for PBSHM;428
47.1;1 Introduction;428
47.2;2 Domain Adaptation;429
47.2.1;2.1 Normal Condition Alignment;429
47.2.2;2.2 Balanced Distribution Adaptation;430
47.2.3;2.3 Instance-Weighting for Partial Domain Adaptation;432
47.3;3 Case Study: Numerical Three-Storey and Four-Storey Population;433
47.3.1;3.1 Simulation;433
47.3.2;3.2 Comparison Procedure;434
47.3.3;3.3 Results;435
47.4;4 Discussion and Conclusions;436
47.5;A Material Properties;437
47.6;References;437
48;Intelligent Health Indicators Based on Semi-supervised Learning Utilizing Acoustic Emission Data;439
48.1;Abstract;439
48.2;1 Introduction;439
48.3;2 Experimental Setup;441
48.4;3 Workflow;442
48.4.1;3.1 Signal Pre-processing;442
48.4.2;3.2 Feature Extraction (FE);442
48.4.3;3.3 Feature Fusion;443
48.5;4 Results and Discussions;446
48.6;5 Conclusions;447
48.7;Funding;448
48.8;References;448
49;Supervised Deep Learning Algorithms for Delaminations Detection on Composites Panels by Wave Propagation Signals Analysis;449
49.1;Abstract;449
49.2;1 Introduction;449
49.3;2 Convolutional Neural Networks;450
49.4;3 SHM Algorithm Implementation;451
49.4.1;3.1 Numerical Model and Signal Analysis;451
49.4.2;3.2 RGB Images Generation;454
49.4.3;3.3 Images Analysis;455
49.4.4;3.4 Confusion Matrix;456
49.4.5;3.5 Classification with 0.072? lessthan ?DI? lessthan ?1.26;457
49.5;4 Conclusions;460
49.6;References;460
50;Site-Specific Defect Detection in Composite Using Solitary Waves Based on Deep Learning;462
50.1;Abstract;462
50.2;1 Introduction;462
50.3;2 Experimental Setup for HNSW Datasets;463
50.4;3 CNN Architectures for Defect Detection in Composites;465
50.5;4 Results and Discussion;467
50.6;5 Conclusion;469
50.7;Acknowledgements;469
50.8;References;470
51;Machine Learning Based Classification of Guided Wave Signals in the Context of Inter-specimen Variabilities;472
51.1;1 Introduction;472
51.2;2 Experimental Setup;474
51.3;3 Signal Processing and Data Preparation;475
51.3.1;3.1 Labeling Process: Path Identification;475
51.3.2;3.2 Disassociating Coupon Connection Between Pristine and Damaged States;476
51.4;4 Defect Detection Methodology;477
51.4.1;4.1 Feature Extraction Using Autoregression;477
51.4.2;4.2 Classification;478
51.5;5 Experimental Validation Results;479
51.5.1;5.1 Data Requirement Study;479
51.5.2;5.2 Performance Comparison;479
51.6;6 Conclusion and Perspective;480
51.7;References;480
52;Bayesian Changepoint Modelling for Reference-Free Damage Detection with Acoustic Emission Data;482
52.1;1 Introduction;482
52.2;2 Bayesian Online Change Point Detection;484
52.2.1;2.1 Bayesian Identification;484
52.2.2;2.2 Initialising the Model;486
52.3;3 Data Collection;486
52.4;4 Results and Discussion;487
52.5;5 Concluding Remarks and Future Work;490
52.6;References;490
53;Integrating Physical Knowledge into Gaussian Process Regression Models for Probabilistic Fatigue Assessment;492
53.1;1 Introduction;492
53.2;2 Methodology;494
53.2.1;2.1 Gaussian Process Regression;494
53.2.2;2.2 Description of SDOF Covariance Function;495
53.3;3 Interpretation of Model Variance;495
53.3.1;3.1 Interpreting the Confidence Intervals from Physics Derived Kernels;495
53.3.2;3.2 Combining Covariance Functions;496
53.4;4 Case Study;496
53.5;5 Discussion and Conclusions;499
53.6;References;501
54;Hybrid Training of Supervised Machine Learning Algorithms for Damage Identification in Bridges;502
54.1;Abstract;502
54.2;1 Introduction;502
54.3;2 Z-24 Bridge and Monitoring Data;503
54.4;3 Finite Element Model and Hybrid Training Matrix;504
54.5;4 Supervised Algorithms Training and Damage Identification;507
54.6;5 Conclusions;511
54.7;References;511
55;Imbalanced Multi-class Classification of Structural Damage in a Wind Turbine Foundation;512
55.1;1 Introduction;513
55.2;2 Theoretical Background;514
55.2.1;2.1 Mean Centered Unitary Group Scaling (MCUGS);514
55.2.2;2.2 Principal Component Analysis (PCA);514
55.2.3;2.3 Extreme Gradient Boosting (XGBoost) Classifier;514
55.3;3 Dataset for Validation;515
55.4;4 Damage Classification Methodology;516
55.5;5 Results and Discussion;517
55.6;6 Conclusions;519
55.7;References;519
56;Dynamic Behavior Analysis of a Rotor-Bearing-Squeeze Film Damper Coupling System with Bearing Outer Race Localized Defect;521
56.1;Abstract;521
56.2;1 Introduction;522
56.3;2 Modeling of the RBSC System;522
56.3.1;2.1 Bearing Force Model Considering Centrifugal Load;523
56.3.2;2.2 SFD Nonlinear Oil-Film Force Model;526
56.3.3;2.3 Governing Equations of Motion;527
56.4;3 Simulation and Discussion;528
56.5;4 Conclusions;530
56.6;Acknowledgment;530
56.7;References;530
57;Wave Propagation Modeling via Neural Networks for Emulating a Wave Response Signal;532
57.1;1 Introduction;533
57.2;2 Dataset Generation;533
57.3;3 Neural Network Framework;534
57.3.1;3.1 Deep Convolutional Autoencoder (DCAE);534
57.3.2;3.2 Feed-Forward Neural Network (FFNN);536
57.3.3;3.3 Training of the Neural Network Framework;536
57.4;4 Results;536
57.5;5 Conclusions and Future Work;539
57.6;References;539
58;Delamination Identification Using Global Convolution Networks;541
58.1;1 Introduction;541
58.2;2 Methodology;543
58.2.1;2.1 Dataset;543
58.2.2;2.2 Data Preprocessing;543
58.3;3 Semantic Segmentation Models;544
58.3.1;3.1 Global Convolutional Network;544
58.4;4 Results and Discussions;545
58.4.1;4.1 Numerical Cases;546
58.4.2;4.2 Experimental Case;548
58.5;References;548
59;A Spatial Autoregressive Approach for Wake Field Prediction Across a Wind Farm;550
59.1;1 Introduction;550
59.2;2 A Small Simulated Wind Farm;552
59.3;3 Spatial Autoregressive Models;554
59.3.1;3.1 Linear SPARX Model;554
59.3.2;3.2 Nonlinear SPARX Model;555
59.3.3;3.3 GP-SPARX Model;555
59.4;4 Result and Comparison;555
59.5;5 Conclusions;558
59.6;References;559
60;ConvLSTM Based Approach for Delamination Identification Using Sequences of Lamb Waves;561
60.1;1 Introduction;561
60.2;2 Dataset Preparation;563
60.3;3 A Brief Introduction to RNN, LSTM and ConvLSTM;564
60.4;4 The Proposed Model;565
60.5;5 Results and Discussions;566
60.6;6 Conclusions;568
60.7;References;568
61;Structural Assessment and Health Monitoring of the Builtup Environment with Satellite Radar Interferometry: Methodologies and Applications;571
62;The Use DInSAR Technique for the Study of Land Subsidence Associated with Illegal Mining Activities in Zaruma – Ecuador, a Cultural Heritage Cite;572
62.1;Abstract;572
62.2;1 Introduction;573
62.3;2 Study Area;574
62.3.1;2.1 Geological Setting;574
62.4;3 Data Set and Methods;575
62.4.1;3.1 Geotechnical Analysis;575
62.4.2;3.2 DInSAR Analysis;576
62.5;4 Results;577
62.5.1;4.1 Geotechnical Results;577
62.5.2;4.2 Interferometric Results;578
62.6;5 Conclusions;580
62.7;References;580
63;Structural Monitoring of a Masonry Hydraulic Infrastructure in Rome: GIS Integration of SAR Data, Geological Investigation and Historical Surveys;582
63.1;Abstract;582
63.2;1 Introduction;583
63.3;2 Methodology;583
63.4;3 Application;583
63.4.1;3.1 3D Model;585
63.4.2;3.2 Preliminary Structural Monitoring and Damage Assessment;586
63.5;4 Conclusions;588
63.6;References;588
64;Integration of Multi-source Data to Infer Effects of Gradual Natural Phenomena on Structures;591
64.1;Abstract;591
64.2;1 Introduction;591
64.3;2 Materials and Method;593
64.3.1;2.1 Selected Case Studies;593
64.3.2;2.2 Urban Analyses: Historic Center of Central Archaeological Area;595
64.3.3;2.3 Localized Analysis: The Colosseum;596
64.4;3 Results and Discussion;598
64.5;References;599
65;An Application of the DInSAR Technique for the Structural Monitoring of the “Vittorino da Feltre” School Building in Rome;601
65.1;Abstract;601
65.2;1 Introduction;601
65.3;2 Procedure;602
65.4;3 The “Vittorino da Feltre” School Building in Rome;603
65.5;4 SAR Data;605
65.6;5 Structural Damage Survey;609
65.7;6 Conclusion;609
65.8;References;610
66;Techniques for Structural Assessment Based on MT-DInSAR Data, Applied to the San Michele Complex in Rome;612
66.1;Abstract;612
66.2;1 Introduction;613
66.3;2 Proposed Comprehensive Approach;613
66.4;3 SBAS-DInSAR Methodology Applied to the Area of Rome, Italy;614
66.5;4 The San Michele Complex Case Study;614
66.6;5 Conclusions;621
66.7;References;621
67;Satellite Interferometric Data and Perturbation Characteristics for Civil Structures at Nanohertz;623
67.1;Abstract;623
67.2;1 Introduction;623
67.3;2 Materials and Method;624
67.3.1;2.1 Time Domain Analysis;624
67.3.2;2.2 Frequency Domain Analysis;625
67.3.3;2.3 Entropy Analysis;625
67.4;3 Results and Discussion;626
67.4.1;3.1 Time Domain Analysis;626
67.4.2;3.2 Frequency Domain Analysis;627
67.4.3;3.3 Entropy Analysis;628
67.5;4 Conclusions;630
67.6;References;630
68;Standardization and Guidelines on SHM and NDT: Needs and Ongoing Activities;632
69;Offline Algorithm Selection of CMA-ES Variants in Bayesian Optimal Sensor Placement: Application to Buildings and Recommendations to the Philippine Instrumentation Practice;633
69.1;Abstract;633
69.2;1 Introduction;633
69.3;2 Related Work on OSP in SHM;634
69.4;3 Problem Statement and Objectives;635
69.5;4 Algorithm Selection in OSP;635
69.6;5 Single-Sensor Investigation on a Steel Column;636
69.7;6 Application to the ASCE Benchmark Structure;637
69.7.1;6.1 Undamaged Case;637
69.7.2;6.2 Damaged Case;638
69.8;7 Comments on the Philippine SHM Practice;639
69.9;8 Conclusions and Recommendations;640
69.10;Acknowledgments;641
69.11;References;641
70;Small Punch Test Method for SHM;643
70.1;Abstract;643
70.2;1 Introduction;643
70.3;2 Small Punch Test;644
70.3.1;2.1 Material Sampling;644
70.3.2;2.2 SPT Test Set;645
70.3.3;2.3 SPT Test Principles;646
70.4;3 Case Study;646
70.4.1;3.1 Small Punch Test vs Uniaxial Tensile Test;647
70.4.2;3.2 Ductile-Brittle Transition Temperature;648
70.4.3;3.3 Results Discussion;650
70.5;4 Conclusion;651
70.6;References;651
71;A Preliminary Qualification Approach for Structural Health Monitoring Systems;653
71.1;Abstract;653
71.2;1 Introduction;653
71.3;2 The European Framework for Qualification of Construction Products;654
71.4;3 A Proposal for Qualification of Civil SHM Systems;656
71.5;4 Case Study and Discussion;658
71.6;5 Conclusions and Future Developments;661
71.7;Acknowledgments;661
71.8;References;661
72;Smart Self-Sensory Concrete Based Structures and Infrastructures;663
73;A Review on Non-destructive Evaluation of Civil Structures Using Magnetic Sensors;664
73.1;Abstract;664
73.2;1 Introduction;664
73.3;2 Fundamentals;665
73.3.1;2.1 Magnetic Flux Leakage;665
73.3.2;2.2 Eddy Currents;665
73.3.3;2.3 Hall Effect Sensors;666
73.3.4;2.4 Magnetoresistive Sensors;667
73.4;3 Applications;667
73.4.1;3.1 Hall Effect Sensors;667
73.4.2;3.2 Magnetoresistive Sensors;669
73.5;4 Discussion and Conclusions;670
73.6;References;672
74;Realization and Testing of Hybrid Textile Reinforced Concrete Prototype Modules Sensorized with Distributed Fiber Optic Sensors;674
74.1;Abstract;674
74.2;1 Introduction;674
74.3;2 Prototypes Preparation;675
74.4;3 Testing;678
74.4.1;3.1 Optical Testing;678
74.4.2;3.2 Testing of the Measuring Behaviour;679
74.5;4 Conclusions;682
74.6;Acknowledgements;682
74.7;References;682
75;Condition Monitoring of Ageing Bridges and Infrastructure;683
76;Fatigue Analysis on Four Months of Data on a Steel Railway Bridge: Event Detection and Train Features’ Effect on Fatigue Damage;684
76.1;Abstract;684
76.2;1 Introduction;684
76.3;2 Case Study and Measurement Campaign;686
76.4;3 Event-Based Methodology;687
76.4.1;3.1 Part I (Event Detection);687
76.4.2;3.2 Part II (Processing the Events);688
76.5;4 Results and Discussion;691
76.5.1;4.1 Effect of Train Type and Axle Number;691
76.5.2;4.2 Effect of Speed and Direction;692
76.6;5 Conclusion;693
76.7;Acknowledgments;693
76.8;References;693
77;Effect of Aging of Bearings on the Behavior of Single-Span Railway Bridges;695
77.1;1 Introduction;695
77.2;2 Numerical Model and Analysis;696
77.3;3 Concluding Remarks;702
77.4;References;703
78;Long-Term Structural Monitoring of a Skewed Masonry Arch Railway Bridge Using Fibre Bragg Gratings;704
78.1;Abstract;704
78.2;1 Introduction;704
78.2.1;1.1 Global Context;704
78.2.2;1.2 CFM-5: the ‘Barkston Road’ Bridge;705
78.3;2 The Monitoring Installation at CFM-5;707
78.3.1;2.1 The FBG Sensing Installation;707
78.3.2;2.2 Autonomous, Remote FBG Monitoring;708
78.4;3 Results and Discussion;709
78.4.1;3.1 Sensitivity of Bridge Response;709
78.5;4 Conclusions;713
78.6;References;713
79;A Novel Procedure for Damping Ratio Identification from Free Vibration Tests with Application to Existing Bridge Decks;714
79.1;Abstract;714
79.2;1 Introduction;714
79.3;2 Modal Component Extraction;715
79.3.1;2.1 Selection of the Signal;715
79.3.2;2.2 Extraction of the Modal Components;716
79.3.3;2.3 Evaluation of Damping Ratios;718
79.4;3 Numerical Applications;719
79.4.1;3.1 Theoretical Signal;720
79.4.2;3.2 Real Signal;721
79.5;4 Conclusions;723
79.6;References;723
80;A Review on Bridge Instrumentation in the United States;724
80.1;Abstract;724
80.2;1 Introduction;724
80.3;2 Structural Health Monitoring;725
80.4;3 Bridge Instrumentation in the U.S.;725
80.4.1;3.1 Golden Gate Bridge;725
80.4.2;3.2 Fred Hartman Bridge;726
80.4.3;3.3 Sunshine Skyway Bridge;726
80.4.4;3.4 Quincy Bayview Bridge;726
80.4.5;3.5 New Benicia-Martinez Bridge;726
80.4.6;3.6 North Halawa Valley Viaduct;727
80.4.7;3.7 Manhattan Bridge;727
80.4.8;3.8 Vincent Thomas Bridge;728
80.4.9;3.9 Mackinac Bridge;728
80.4.10;3.10 Carroll Lee Cropper Bridge;728
80.4.11;3.11 Huey P. Long Bridge;728
80.4.12;3.12 Neville Island Bridge;729
80.4.13;3.13 Birmingham Bridge;729
80.4.14;3.14 Columbia River I-5 Bridge;730
80.5;4 Conclusion;730
80.6;Acknowledgement;731
80.7;References;731
81;How to Make a Self-sensing House with Distributed Fiber Optic Sensing;733
81.1;Abstract;733
81.2;1 Introduction;733
81.3;2 DFOS Installation and Documentation;734
81.4;3 Temperature and Strain Measurements of the Ceiling;736
81.5;4 Seasonal Temperature Changes Within the Ground Plate;738
81.6;5 Movement of People Within the House;739
81.7;6 Conclusion and Outlook;740
81.8;References;741
82;Automation in Documentation of Ageing Masonry Infrastructure Through Image-Based Techniques and Machine Learning;742
82.1;Abstract;742
82.2;1 Introduction;742
82.3;2 Block Segmentation;743
82.4;3 Case Study;746
82.5;4 Conclusions;748
82.6;References;749
83;Health Monitoring Methodologies and Technologies for Aerospace Actuation and Drive Systems;751
84;Condition Monitoring of a Gear Box by Acoustic Camera and Machine Learning Techniques;752
84.1;Abstract;752
84.2;1 Introduction;752
84.2.1;1.1 Statement of the Problem;752
84.3;2 Experimental Data Analysis;753
84.3.1;2.1 Experimental Test Rig and Setup;753
84.3.2;2.2 Analysis Procedure;755
84.4;3 Analysis of the Results;758
84.5;4 Conclusions and Future Applications;760
84.6;References;761
85;Health Monitoring Methodologies for Aerospace Electromechanical Actuation Systems;762
85.1;1 Introduction;762
85.2;2 Aviation Trends;763
85.2.1;2.1 Advantages and Drawbacks;763
85.2.2;2.2 EMA Requirements;764
85.2.3;2.3 Failure Modes;766
85.3;3 Condition Monitoring;767
85.4;4 Potential Future Steps;769
85.5;5 Concluding Remarks;770
85.6;References;771
86;Ultrasonic and Electromagnetic Waves for Diagnosis, Monitoring and Control;773
87;Monitoring of Pipelines Using Microwave Structural Health Monitoring;774
87.1;1 Introduction;774
87.2;2 Theoretical Background;775
87.2.1;2.1 Detection Concept;775
87.3;3 Experimental Setup;776
87.3.1;3.1 Experimental Measurements;777
87.4;4 Results;778
87.4.1;4.1 Characterization of the Antenna;778
87.4.2;4.2 Damage Detection;779
87.5;5 Conclusion and Outlook;781
87.6;References;782
88;Analysis and Compensation of Relative Humidity and Ice Formation Effects for Radar-Based SHM Systems Embedded in Wind Turbine Blades;783
88.1;1 Introduction;783
88.2;2 Theroretical Background;784
88.2.1;2.1 Wave Excitation;784
88.3;3 Experimental Setup;784
88.3.1;3.1 Radar Sensor;784
88.3.2;3.2 Climate Chamber;785
88.4;4 Results;786
88.4.1;4.1 Humidity Influence;786
88.4.2;4.2 Environmetal Influence Compensation;787
88.4.3;4.3 Ice Detection;788
88.5;5 Summary and Future Work;791
88.6;References;792
89;In-Process Monitoring of Surface Roughness of Internal Channels Using;793
89.1;Abstract;793
89.2;1 Introduction;793
89.3;2 Methodology;794
89.3.1;2.1 Phase-Screen Approximation;794
89.3.2;2.2 Correction of Beam Spreading Effect;795
89.4;3 Numerical Simulation;796
89.4.1;3.1 Finite Element Modeling;796
89.4.2;3.2 Simulation Results;796
89.5;4 Experimental Validation;798
89.5.1;4.1 Experimental Setup;798
89.5.2;4.2 Experiment Results;799
89.6;5 Conclusions;799
89.7;References;799
90;Simultaneous Monitoring of Component Thickness and Internal Temperature Gradient Using Ultrasound;801
90.1;1 Introduction;801
90.2;2 Measurement Principles;802
90.2.1;2.1 Ultrasonic Thickness Measurement;802
90.2.2;2.2 Ultrasonic Temperature Sensing;803
90.2.3;2.3 The Dual-Wave Correction Method;803
90.2.4;2.4 Calculation Process;804
90.3;3 Numerical Simulation;805
90.3.1;3.1 Data Generation;805
90.3.2;3.2 Simulation Results;806
90.4;4 Experimental Verification;807
90.4.1;4.1 Experimental Procedures;807
90.4.2;4.2 Results and Discussion;808
90.5;5 Conclusion;809
90.6;References;809
91;The Use of a Magnetic Probe Coupler to Aid the Reliability of Manual Ultrasonic Testing (MUT) on Carbon Steel Components;810
91.1;Abstract;810
91.2;1 Introduction;810
91.3;2 The Prevalence of MUT in Industry;811
91.3.1;2.1 Magnetic Probe Coupler;812
91.3.2;2.2 Future Work;814
91.4;3 Conclusion;815
91.5;References;816
92;An Adaptive Impedance Matching Network for Ultrasonic De-icing;817
92.1;Abstract;817
92.2;1 Introduction;817
92.3;2 Experimental Setup;818
92.3.1;2.1 Impedance Measuring System;819
92.3.2;2.2 Adaptive Impedance Matching Network;819
92.4;3 Results;820
92.5;4 Conclusions;822
92.6;Acknowledgments;822
92.7;References;822
93;Practical Experiences to Know Making Acoustic Emission-Based SHM Successful;823
93.1;1 Introduction;823
93.2;2 Experiments;825
93.3;3 Methods;826
93.4;4 Results and Discussion;828
93.5;5 Summary and Conclusions;830
93.6;References;830
94;General Session;831
95;Structural Health Monitoring of Adhesively Bonded Skin-Stiffener Composite Joint Using Distributed Fibre Optic Sensor;832
95.1;Abstract;832
95.2;1 Introduction;832
95.3;2 Experimental Setup;833
95.4;3 Results and Discussions;835
95.5;4 Conclusion;838
95.6;References;839
96;Towards Video-Based System Identification and Finite Element Model Updating of Civil Structures and Infrastructures;840
96.1;Abstract;840
96.2;1 Introduction;840
96.2.1;1.1 The Rationale for Computer Vision-Based SHM;841
96.3;2 Materials and Methods;842
96.3.1;2.1 Phase-Based Motion Magnification (PBMM);842
96.3.2;2.2 Finite Element Model Updating (FEMU);843
96.4;3 Case Study #1: SHM of High Rise Buildings;845
96.5;4 Case Study #2: Bridge Monitoring;846
96.6;5 Conclusions;847
96.7;References;847
97;Monitoring of Hydraulic Structure: Problem and Approach;849
97.1;Abstract;849
97.2;1 Problem Statement;849
97.3;2 Structural Health Monitoring;851
97.4;3 Requirements for the SHM System;852
97.5;4 Data-Based Model;854
97.6;5 Long-Term Stability;855
97.7;6 Conclusion and Outlook;856
97.8;References;857
98;Evaluating the Usefulness of Audible Acoustics as a Damage Detection Method in Large Composite Structures;858
98.1;Abstract;858
98.2;1 Introduction;858
98.3;2 Experimental Set-Up;860
98.3.1;2.1 Material Specification;860
98.3.2;2.2 Acoustic Camera Set-Up;861
98.3.3;2.3 Strain Gauge Set-Up;862
98.4;3 Results;862
98.5;4 Conclusion;869
98.6;References;870
99;Low-Power Actuation Methods for Highly Nonlinear Solitary Wave Transducers Used to Assess Human Eyes;871
99.1;1 Introduction;871
99.2;2 Design and Development of the Transducer;873
99.2.1;2.1 Particle Size;873
99.2.2;2.2 L-Shape Container;873
99.2.3;2.3 Sensor Placement;875
99.2.4;2.4 Sensor Type;875
99.2.5;2.5 Solenoid Driver Selection;875
99.3;3 Testing Background and Results;876
99.4;4 Conclusion;879
99.5;References;880
100;Numerical and Experimental Study of Acoustic Emission Source Signal Reconstruction in Fibre-Reinforced Composite Panels;881
100.1;Abstract;881
100.2;1 Introduction;881
100.3;2 Mathematical Procedure;882
100.3.1;2.1 Dispersion Compensation;882
100.3.2;2.2 Waveform Similarity;883
100.4;3 Simulation Using 3D SEM;884
100.5;4 Experimental Procedure;885
100.6;5 Numerical and Experimental Results and Analysis;887
100.6.1;5.1 Simulation Results of GFRP Panel;887
100.6.2;5.2 Experiment on GFRP Panel;888
100.7;6 Conclusion;889
100.8;Acknowledgments;890
100.9;References;890
101;Damage Detection and Identification in Composites by Acoustic Emission, Ultrasonic Inspection and Computer Tomography;892
101.1;Abstract;892
101.2;1 Introduction;892
101.3;2 Sample Preparation;893
101.3.1;2.1 Samples with Impact;893
101.3.2;2.2 Samples with Porosity;893
101.4;3 Test Matrix;894
101.5;4 Experimental Results;895
101.5.1;4.1 Ultrasonic Inspection and CT Investigation;895
101.5.2;4.2 Acoustic Emission Monitoring;896
101.6;5 Conclusion;899
101.7;Acknowledgement;900
101.8;References;900
102;Condition Assessment of Low-Speed Slew Bearings in Offshore Applications Using Acoustic Emission Monitoring;901
102.1;Abstract;901
102.2;1 Introduction;901
102.3;2 Methodology;902
102.3.1;2.1 Experimental Set-Up;902
102.3.2;2.2 Experimental Procedures;903
102.3.3;2.3 Data Processing;904
102.4;3 Results;905
102.5;4 Conclusions;908
102.6;Acknowledgements;908
102.7;References;908
103;Influence of Environmental Conditions and Damage on Closely Spaced Modes;911
103.1;1 Introduction;911
103.2;2 Theoretical Background;912
103.2.1;2.1 Bayesian Operational Modal Analysis;912
103.2.2;2.2 Uncertainty of the MAC and S2MAC;913
103.3;3 Application;914
103.3.1;3.1 Influence of Environmental Conditions on Natural Frequencies and Mode Shapes;915
103.3.2;3.2 Damage Detection;917
103.4;4 Conclusion;919
103.5;References;919
104;Thin Membrane with “Human Touch” Sensitivity: Body Pressure and Temperature Measurements with Optical Fiber Sensors;921
104.1;Abstract;921
104.2;1 Introduction;921
104.3;2 Design and Development of the Thin Membrane;922
104.3.1;2.1 Working Principles;922
104.3.2;2.2 Technological Development of the Thin Membrane;923
104.4;3 Calibration of the Thin Elastomeric Membrane;924
104.4.1;3.1 Set-Up;924
104.4.2;3.2 Calibration Results;925
104.5;4 Validation Tests;926
104.6;5 Conclusions;928
104.7;References;929
105;Enabling FO-Based HUMS Applications Through an Innovative Integration Technique: Application to a Rotor Blade Mockup;930
105.1;Abstract;930
105.2;1 Introduction;930
105.3;2 Smart-Veil Manufacturing;933
105.4;3 The Case Study: A Sensorized Helicopter Blade Mockup;936
105.4.1;3.1 Strain Measurement;936
105.4.2;3.2 Manufacturing;937
105.5;4 Calibration and Validation;939
105.6;5 Conclusions;940
105.7;Bibliography;941
106;An Integrated Fiber Optic Based SHM System for Structural Composite Components: Application to a Racing Motorbike Fork;942
106.1;Abstract;942
106.2;1 Introduction;942
106.3;2 Telescopic Up-Side Down Front Fork Design;943
106.4;3 Material and Methods;944
106.5;4 Structural Monitoring System Design;945
106.5.1;4.1 Finite Element Models;945
106.5.2;4.2 Fiber Bragg Grating Sensors;945
106.6;5 Production of the Fork Tubes;947
106.7;6 Validation and Load Tests;948
106.7.1;6.1 Motostudent Safety Checks;948
106.7.2;6.2 Stiffness Comparison;948
106.7.3;6.3 Bending Test Different Azimuth (Sensor Location);949
106.8;7 Conclusion;951
106.9;References;951
107;Estimation of Local Failure in Large Tensegrity Structures via Substructuring Using Interacting Particle-Ensemble Kalman Filter;952
107.1;1 Introduction;952
107.2;2 System Model;954
107.3;3 Substructuring Approach;954
107.4;4 Output Injection Approach;956
107.5;5 Numerical Experiment;958
107.6;6 Conclusion;959
107.7;References;960
108;Spherical Inclusions Based Defect Modes in a Phononic Crystal for Piezoelectric Energy Harvesting;961
108.1;Abstract;961
108.2;1 Introduction;961
108.3;2 Simulation Methods;962
108.3.1;2.1 Band Structure Analysis;963
108.3.2;2.2 Transmission Analysis;964
108.3.3;2.3 Energy Harvester Voltage Response Analysis;965
108.4;3 Results and Discussion;966
108.4.1;3.1 Perfect PnC Band Structure and Transmission Analysis;966
108.4.2;3.2 Defect PnC Analysis;967
108.4.3;3.3 Energy Harvester Performance Analysis;968
108.5;4 Conclusion;970
108.6;References;970
109;Low Flow Rate Measurement and Leak Detection for Health Monitoring of Water Equipment;972
109.1;Abstract;972
109.2;1 Introduction;972
109.3;2 Materials and Methods;973
109.3.1;2.1 Flow Meter Geometry;973
109.3.2;2.2 Simulation;973
109.3.3;2.3 Sensor Synthesis;974
109.3.4;2.4 Testing;975
109.4;3 Results and Discussion;976
109.4.1;3.1 Factorial Analysis;976
109.4.2;3.2 Sensor Performance;976
109.4.3;3.3 Pressure-Drop;977
109.5;4 Conclusion;977
109.6;Acknowledgements;978
109.7;References;978
110;Frequency Domain System Identification of Error–in–Variables Systems for Vibration–Based Monitoring;981
110.1;1 Introduction;981
110.2;2 Frequency Domain Identification of AR+Noise Models in the Frisch Scheme;982
110.2.1;2.1 Problem Statement;982
110.2.2;2.2 A Frequency Domain Setup in the Frisch Scheme;983
110.3;3 Model Order Estimation;985
110.4;4 Experimental Validation;986
110.4.1;4.1 Methods;987
110.4.2;4.2 Results;987
110.5;5 Conclusions;989
110.6;References;990
111;Real-Time Remote Monitoring of Steam Turbine Blades Based on High Cycle Fatigue Module and Cloud Computing;991
111.1;Abstract;991
111.2;1 Introduction;991
111.3;2 FEM Stress Limit Calculation;992
111.4;3 HCF Analysis Method;993
111.5;4 Remote Monitoring and Cloud Computing;995
111.6;5 Conclusion;997
111.7;Acknowledgement;998
111.8;References;998
112;Numerical Modeling of a Pyroshock Test Plate for Qualification of Space Equipment;999
112.1;Abstract;999
112.2;1 Introduction;999
112.3;2 Methodology;1002
112.4;3 Results;1005
112.5;4 Conclusions;1007
112.6;References;1008
113;A Novel Smart Sensor Node with Embedded Signal Processing Functionalities Addressing Vibration–Based Monitoring;1009
113.1;1 Introduction;1010
113.2;2 Output–Only SysId Models for Vibration Analysis;1010
113.3;3 Sensor Node Description;1011
113.4;4 Experimental Validation;1013
113.4.1;4.1 Materials and Methods;1013
113.4.2;4.2 Results;1014
113.5;5 Conclusions;1017
113.6;References;1017
114;Collective Mobile 3D Printing: An Active Sensing Approach for Improved Autonomy;1018
114.1;1 Introduction;1018
114.2;2 Problem Formulation;1020
114.3;3 Shape Reconstruction;1021
114.4;4 Simulation Results;1022
114.5;5 Conclusions;1023
114.6;References;1023
115;Inferring the Size of Stochastic Systems from Partial Measurements;1025
115.1;1 Introduction;1025
115.2;2 Theory;1026
115.3;3 Numerics;1028
115.4;4 Conclusions;1030
115.5;References;1031
116;Wire Break Detection in Bridge Tendons Using Low-Frequency Acoustic Emissions;1033
116.1;1 Introduction;1033
116.2;2 Experiments;1034
116.2.1;2.1 Wire Break Experiments at Demolished Bridge Girders;1035
116.2.2;2.2 In-situ Wire Break Recordings on Real Bridge Construction;1035
116.2.3;2.3 Operational Recordings in Hagen, Germany;1036
116.3;3 Methodology;1037
116.3.1;3.1 Extraction of Impulse-Like Signals;1037
116.3.2;3.2 Time-Frequency Domain Features;1038
116.3.3;3.3 Wire Break Detection Using Support Vector Machines;1039
116.4;4 Results;1039
116.4.1;4.1 Comparison with Amplitude Based Hitdetection;1040
116.5;5 Conclusion;1040
116.6;References;1041
117;App4SHM – Smartphone Application for Structural Health Monitoring;1043
117.1;Abstract;1043
117.2;1 Introduction;1044
117.3;2 Software Architecture and Details;1045
117.3.1;2.1 Step 1 – Structure Identification;1045
117.3.2;2.2 Step 2 – Data Acquisition;1046
117.3.3;2.3 Step 3 – Feature Extraction;1046
117.3.4;2.4 Step 4 – Damage Detection;1047
117.4;3 Case Study: Twin Bridges Over the Itacaiúnas River;1047
117.5;4 Conclusions;1051
117.6;References;1052
118;Structural Damage Detection of Offshore Structures Using Kalman Filtering;1053
118.1;Abstract;1053
118.2;1 Introduction;1053
118.3;2 Mathematical Formulation and Kalman Filter;1054
118.3.1;2.1 State Space Formulation for Linear Systems;1055
118.3.2;2.2 Linear Approximation of Nonlinear Systems;1055
118.3.3;2.3 Augmented Kalman Filter;1056
118.4;3 Damage Detection Framework;1056
118.5;4 Numerical Example;1057
118.5.1;4.1 FE Model of the 2D Steel Jacket Structure;1058
118.5.2;4.2 Sensors, and Measurement Noise and External Loading;1059
118.5.3;4.3 Definition of Covariance Matrices;1059
118.6;5 Results;1060
118.7;6 Conclusions;1062
118.8;Acknowledgments;1062
118.9;References;1062
119;Experimental Fatigue Evaluation of a Gusset-Less Truss Connection;1064
119.1;Abstract;1064
119.2;1 Introduction;1064
119.3;2 Project Overview;1065
119.4;3 Experimental Test Setup;1066
119.5;4 Instrumentation;1068
119.6;5 Test Monitoring;1069
119.7;6 Finite Element Modeling;1070
119.8;7 Results;1071
119.9;References;1073
120;Author Index;1074
