Title Page	2
	Preface	6
	Contents	8
	Introduction	12
	Background	12
	Lamb-wave-based Damage Identification	14
	About this Book	22
	References	23
	Fundamentals and Analysis of Lamb Waves	26
	Retrospect	26
	Fundamentals and Theory	26
	Theory of Lamb Waves	27
	Lamb Waves in Plate of Multiple Layers	33
	Shear Horizontal Waves and Love Waves	33
	Cylindrical Lamb Waves	35
	Propagation Velocity – Phase vs. Group Velocities	36
	Slowness	37
	Dispersion	38
	Numerical and Semi-analytical Study	42
	Transfer Matrix Method	44
	Global Matrix Method	45
	Finite Element Modelling and Simulation	46
	Modelling Lamb Waves	46
	Modelling Structural Damage	47
	Attenuation of Lamb Waves	53
	Influence of Temperature	58
	Influence of Damage Orientation and Size	59
	Summary	62
	References	64
	Activating and Receiving Lamb Waves	70
	Introduction	70
	Transducers of Lamb Waves	70
	Ultrasonic Probes	70
	Piezoelectric Wafers and Piezocomposite Transducers	72
	Laser-based Ultrasonics	73
	Interdigital Transducers	73
	Fibre-optic Sensors – Reception Only	75
	Activation of Desired Diagnostic Lamb Waves	75
	Selection of Appropriate Wave Mode	75
	Optimal Design of Waveform	80
	Mechanistic Models of Piezoelectric Transducers	83
	Various Models	83
	Influence of Transducer Shape	84
	Case Study: Activating and Receiving Lamb Waves (Both the S$_{0}$ and A$_{0}$ Modes) in Delaminated Composite Laminates with Surface-bonded PZT Wafers	87
	Modelling Coupled PZT Actuator	87
	Modelling Coupled PZT Sensor	95
	Validation in FEM Simulation	96
	Summary	99
	References	99
	Sensors and Sensor Networks	110
	Introduction	110
	Piezoelectric Transducer	112
	Design of Piezoelectric Actuator and Sensor	113
	Surface-mounting vs. Embedding	117
	Fibre-optic Sensor	120
	Optical Fibre and Fibre-optic Sensor	120
	Fibre Bragg Grating Sensor	121
	FBG Sensor for Lamb Wave Collection	123
	Surface-mounting vs. Embedding	127
	Directivity	128
	Hybrid Piezoelectric Actuator-optic Sensor Unit	130
	Sensor Network	132
	Arrangement and Optimisation of Sensor Network	134
	Standardised Sensor Network	137
	Commercial Sensor Network Techniques	139
	Recent Developments	141
	Large-scale Sensor Network	141
	Wireless Sensor	142
	Summary	144
	References	145
	Processing of Lamb Wave Signals	154
	Introduction	154
	Digital Signal Processing	155
	Time Domain Analysis	155
	Frequency Domain Analysis	163
	Joint Time-frequency Domain Analysis	166
	Wavelet Transform	169
	Continuous Wavelet Transform	170
	Discrete Wavelet Transform	172
	Selection of Wavelet Function	175
	Extracting Signal Features Using Wavelet Transform	175
	Processing of Lamb Wave Signals	179
	Averaging and Normalisation	179
	De-noising	181
	Feature Extraction and Damage Index	182
	Compression	189
	A Signal Processing Approach for Lamb Waves: Digital Damage Fingerprints	192
	Summary	195
	References	197
	Algorithms for Damage Identification ? Fusion of Signal Features	205
	Introduction	205
	Data Fusion and Damage Identification Algorithms	206
	Damage Index	209
	Time-of-flight	209
	Time Reversal – for Identifying Damage	219
	Migration Technique	219
	Lamb Wave Tomography	223
	Probability-based Diagnostic Imaging	229
	Phased-array Beamforming	238
	Artificial Intelligence	244
	Architecture and Scheme of Data Fusion	251
	Fusion Architecture	251
	Fusion Scheme	253
	Summary	256
	References	258
	Application of Algorithms for Identifying Structural Damage ? Case Studies	265
	Identifying a Notch in a Structural Alloy Beam Using Damage Index	265
	Establishment of DI	266
	Assessing Changes in Notch Size Using DI	268
	Locating Delamination in a Composite Panel Using Time-of-flight	271
	Hierarchically Locating Multiple Delamination in a Composite Panel Using Time-of-flight	273
	Rationale	274
	Experimental Validation	275
	Evaluating Multiple Delamination in a Composite Panel Using Probability-based Diagnostic Imaging	278
	Establishment of Prior Perceptions from a Sensing Path in Terms of ToF	278
	Establishment of Probability of Presence of Damage	278
	Fusion of Probabilities for Diagnostic Imaging	281
	Quantitatively Predicting Delamination in Composite Beams Using an Artificial Neural Network	283
	Training Data Preparation	283
	ANN Training and Experimental Validation	286
	Discussion	286
	Quantitatively Estimating Through-thickness Hole and Delamination in Composite Panels Using an Artificial Neural Network	287
	Training Data Preparation	287
	Parameterised Modelling	290
	ANN Training and Experimental Validation	291
	Discussion: Influential Issues	293
	Identifying Structural Damage in a Composite Laminate Using Bayesian Inference	302
	Summary	303
	References	303
	Systems and Engineering Applications	308
	Introduction	308
	System for Implementation	308
	Signal Generation Subsystem	310
	Data Acquisition Subsystem	310
	Central Control/Analysis Unit	311
	Calibration of System	311
	Application in Engineering Structures	314
	Detection of Corrosion in Pipelines	314
	Identification of Damage in Aerospace Structures	322
	Evaluation of Integrity of Civil Infrastructure	329
	Summary	333
	References	334
	Looking Forward	338
	State of the Art	338
	Prospects	340
	References	348
	Index	350