Composite Materials and Structures

Name: Composite Materials and Structures | Artificial Intelligence-Based Structural Health Monitoring
Brand: De Gruyter
Price: 99.95 EUR
Availability: OnlineOnly

Artificial Intelligence-Based Structural Health Monitoring

Wael A. Altabey(Author)

De Gruyter (Publisher)

1st Edition

Published on 31. October 2025

X, 360 pages

E-Book

PDF with digital watermarking

System requirements

978-3-11-221309-4 (ISBN)

€99.95incl. 7% vat

System requirements

for PDF with digital watermarking

E-Book Single Licence

Available for download

Description

More details

Other editions

Person

Dr. Wael A. Altabey is a full professor at department of Mechanical Engineering, Alexandria University, Alexandria, Egypt. Before that he was a research associate professor between 2018 to 2024 at International Institute for Urban Systems Engineering (IIUSE), Southeast University, Nanjing, China, and National and Local Joint Engineering Research Center for Basalt Fiber Production and Application Technology, Southeast University, Nanjing, Jiangsu, China, after completing a postdoctoral research fellowship for two years (2016-2018).Since 2016 his researches have focused on the utilization of Artificial Intelligence (AI) based schemes for structural health monitoring (SHM) and Non-Destructive Testing (NDT) for damage classification, detection, diagnosis, prediction, dynamic response analysis, and Reliability evaluation in composite, and steel Structures (such as aircraft, wind turbines, pipes, bridges and industrial machines) at National and Local Joint Engineering Research Center for Basalt Fiber Production and Application Technology, Southeast University, Nanjing, Jiangsu, China. This is the only national R&D platform awarded by the National Development and Reform Commission in this industry with more than 30 national authorized patents. The center's international and national awards indicators have reached international and local leading levels and filling many technical gaps in China.He participated in several research activities, which achieved from NSFC and private sectors. He listed in Stanford List of World's Top 2% Scientists from 2020, until now. He serves on various technical committees in several international conferences and workshops, guest editor of special Issues in several international scientific journals and on the editorial board of several international scientific journals in the field of artificial intelligence, mechanical, materials, and civil engineering. He a peer reviewer of more than 140 international scientific journals. He is an author and co-author of more than 110 high impact journ al papers, 60 scientific conference papers and 50 chapters, 10 academic and research books, patent, and delivered over 40 invited talks.His research interests: Smart and Nanomaterials; Composite Structures; Structural Health Monitoring (SHM); Artificial Intelligence (AI); Non-Destructive Testing (NDT); Digital Twins Model of Structural Behavior, System Identification; Damage Detection: Vibration-Based Techniques; Fiber Optical Sensing Technique, Structural Control; Structural Resilience and Reliability, Hysteretic Systems, Micro/Nano Electro Mechanical Systems (MEMS/ NEMS), and Energy Harvesting Model for Self-Powered Sensors.

Content

Intro
Preface
Contents
Chapter 1 Introduction to composite materials
1.1 Introduction to materials
1.1.1 Classification of materials
1.2 Introduction to composite materials
1.2.1 What are composites?
1.2.2 History of composite materials
1.2.3 Advantages and disadvantages
1.2.3.1 Advantages of composite materials
1.2.3.2 Disadvantages of composite materials
1.2.4 Applications of composite materials
1.2.4.1 Space applications
1.2.4.2 Automotive applications
1.2.4.3 Sporting industry applications
1.2.4.4 Marine applications
1.2.4.5 What can be made using composite materials?
1.2.4.6 What is the world market for composites?
1.2.5 Classification of composite materials
1.2.5.1 Particulate composites
1.2.5.2 Flake composites
1.2.5.3 Fiber composites
1.2.5.4 Polymer matrix composites
1.2.5.5 Metal matrix composites
1.2.5.6 Ceramic matrix composites
1.2.5.7 Carbon-carbon composites
Chapter 2 Basic concepts and terminology
2.1 Fibers and matrix
2.1.1 Matrix materials
2.1.2 Functions of a matrix
2.1.3 Properties of a matrix
2.1.4 Factors considered for the selection of matrix
2.1.5 General types of matrix materials
2.1.5.1 Thermosetting matrices (resin)
2.1.5.1.1 Comparison of common thermosetting resins
2.1.5.2 Thermoplastic matrices (resin)
2.1.6 Fiber materials
2.1.7 Functions of a fibers
2.1.7.1 Glass fibers
2.1.7.2 Carbon fibers
2.1.7.3 Aramid fibers
2.1.7.4 Silicon carbide
2.1.7.5 Organic fibers
2.2 Styles of reinforcement
Chapter 3 Design concepts for composite materials/structures
3.1 Introduction
3.1.1 Design considerations
3.1.2 Designing the laminate
3.1.3 Establishing property data
3.1.4 Designing for the environment
3.1.5 Designing for joints and assemblies
3.1.6 Designing for robustness and through-life performance
3.1.7 Designing for manufacture
3.1.8 Designing for cost
3.2 The need for design management
3.3 The design process
Chapter 4 Composite manufacturing, fabrication, and processing
4.1 Introduction
4.2 Classification of manufacturing processes
4.2.1 Open mold process
4.2.1.1 Wet layup/hand layup
4.2.1.2 Spray layup
4.2.1.3 Filament winding
4.2.1.4 Sheet molding compound
4.2.1.5 Expansion tool molding
4.2.1.6 Contact molding
4.2.2 Closed mold process
4.2.2.1 Compression molding
4.2.2.2 Vacuum bag processing
4.2.2.3 Pressure bag molding
4.2.2.4 Injection molding
4.2.2.5 Cold press molding
4.2.2.6 Resin transfer molding
4.2.2.7 Autoclave molding
4.2.3 Continuous processes
4.2.3.1 Pultrusion
4.2.3.2 Continuous laminating processes
4.2.3.3 Braiding
4.3 Defects in manufactured polymeric composites
Chapter 5 The mechanical behavior of composite materials
5.1 Introduction
5.1.1 Isotropic material
5.1.2 Orthotropic material
5.1.3 Anisotropic material
5.2 Lamina and laminates
5.3 Failure modes of composite materials
5.3.1 Microcracking of the matrix
5.3.2 Fiber pull-out and debonding (separation of fibers and matrix)
5.3.3 Delamination
5.3.4 Breaking of fibers
5.4 Failure mechanisms of composite materials
5.5 Fatigue behavior of composite materials
5.6 Factors affecting the fatigue behavior of composite materials
5.7 Failure criteria of fatigue loading
Chapter 6 Structural health monitoring of composite structures
6.1 Introduction
6.1.1 Capability of nondestructive testing techniques for structural health monitoring
6.1.2 Characteristics of piezoelectric sensors for structural health monitoring
6.2 Lamb wave technique-based structural health monitoring of composite structures
6.2.1 History and literature behind the Lamb wave technique
6.2.2 Lamb wave modeling and simulation
6.2.3 Lamb wave application in structural health monitoring
6.2.4 Optimal number and configuration of piezoelectric transducers
6.2.5 Lamb wave-based damage identification principle
6.3 Electrical capacitance sensor technique-based structural health monitoring of composite structures
6.3.1 History and literature behind the electrical capacitance sensor technique
6.3.2 Electrical capacitance sensor modeling and simulation
6.3.3 Electrical capacitance sensor electrode excitation strategy
6.3.4 3D electrical capacitance sensor governing equation
6.3.5 Factors affecting the electrical capacitance sensor technique
6.3.6 Effect of the number of electrodes on the performance of electrical capacitance sensors
6.3.7 The life of electrical capacitance sensor
6.4 Fiber-optic sensor technique-based structural health monitoring of composite structures
6.4.1 History and literature behind the fiber-optic technique
6.4.2 Common types of fiber-optic sensors
6.4.3 Description of fiber Bragg grating sensors
6.4.4 Working principle of fiber Bragg grating sensors
6.4.5 Improve the design of the fiber Bragg grating for large-strain sensor
6.5 Artificial intelligence
6.5.1 Machine learning
6.5.2 Deep learning
6.5.3 Artificial neural networks
6.5.4 Damage identification with artificial neural networks in composite structures
6.5.4.1 Gray-box model
Chapter 7 Case studies on structural health monitoring of composite structures
7.1 Structural health monitoring of composite pipelines
7.1.1 Case study (1): predicting water absorption in composite pipes using electrical capacitance sensors integrated with deep learning approach
7.1.2 Methodology
7.1.2.1 The geometric model
7.1.2.2 Numerical work
7.1.2.2.1 System assumptions
7.1.2.3 The structural-thermal-electrostatic (multi-physics) coupled field modeling
7.1.2.3.1 The static model analysis
7.1.2.3.2 Electrostatic field results
7.1.2.4 The mass of water absorption (M%) monitoring
7.1.2.5 Deep neural networks
7.1.2.5.1 Architecture of the developed deep neural network
7.1.2.5.2 Deep neural network training and test sets
7.1.2.5.3 The training and testing algorithm
7.1.2.5.4 The developed deep neural network predicted data of M%
7.1.2.6 Validity of the proposed technique
7.1.2.6.1 Theoretical validation
7.1.2.6.2 Experimental validation
7.1.2.6.3 Evaluation of present algorithm accuracy and reliability
7.1.2.7 Summary
7.1.3 Case study (2): predicting long-term creep thermomechanical fatigue behavior monitoring in composite pipelines using electrical capacitance sensors integrated with deep learning approach
7.1.3.1 Methodology
7.1.3.2 The geometric model
7.1.3.3 Numerical work
7.1.3.3.1 The structural-thermal-electrostatic modeling analysis
7.1.3.3.2 S-N curve of fatigue behavior for basalt fiber-reinforced polymer composite pipeline
7.1.3.3.3 D-N curve of fatigue damage model for basalt fiber-reinforced polymer composite pipeline
7.1.3.4 Deep neural networks
7.1.3.4.1 Deep neural network configuration for LTCTMF behavior in basalt fiber-reinforced polymer composite pipeline
7.1.3.4.2 Deep neural network training and testing
7.1.3.4.3 The training and testing algorithms
7.1.3.5 Electrical capacitance sensor results for LTCTMF behavior in basalt fiber-reinforced polymer composite pipelines
7.1.3.6 Validity of the proposed technique
7.1.3.6.1 Theoretical validation
7.1.3.6.2 Experimental validation
7.1.3.7 The deep neural network predicted data of Sf(t)
7.1.3.8 Utilizing the trained deep neural network for predicting nonfinite element model data
7.1.3.9 Summary
7.1.4 Case study (3): structural health monitoring of composite pipelines utilizing fiber optic sensors and machine learning approach
7.1.4.1 Methodology
7.1.4.2 The geometric model
7.1.4.2.1 Damaged pipeline system modeling
7.1.4.2.2 Modal analysis of the pipeline
7.1.4.2.3 Stress-strain analysis in a stressed thick-walled pipe
7.1.4.3 Pipeline monitoring-based fiber Bragg grating sensor technology
7.1.4.4 Design theory of fiber Bragg grating strain sensor array
7.1.4.5 The basalt fiber-reinforced polymer pipeline damage identification model
7.1.4.5.1 A k-nearest neighbor algorithm
7.1.4.5.2 The convolutional neural network modeling
7.1.4.6 The displacement response identification
7.1.4.7 Experimental validation of the proposed method
7.1.4.8 Hybrid convolutional neural network + k-nearest neighbor (ECNN) architecture as a surrogate model
7.1.4.9 The displacement response prediction based on ECNN
7.1.4.10 Summary
7.2 Structural health monitoring of composite ducts
7.2.1 Case Study (4): structural health monitoring of composite dual-chamber muffler using deep learning algorithm for predicting acoustic behavior
7.2.1.1 Methodology
7.2.1.2 Materials and methods
7.2.1.2.1 The geometric model
7.2.1.2.2 Basic acoustic equations of the dual-chamber muffler
7.2.1.2.3 Acoustic properties of composite laminated muffler
7.2.1.2.4 Acoustic transmission loss
7.2.1.3 Artificial neural networks
7.2.1.3.1 Convolutional neural network
7.2.1.3.2 Recurrent neural network with long short-term memory (RNN-LSTM) blocks
7.2.1.3.3 Bayesian genetic algorithm optimization
7.2.1.4 The work description
7.2.1.5 Validation of the proposed method
7.2.1.6 Data collection
7.2.1.7 The established algorithm
7.2.1.8 Development of artificial neural network models
7.2.1.9 Performance evaluation of artificial neural network models
7.2.1.10 Acoustic DCLCM geometry design optimization
7.2.1.11 Summary
7.3 Structural health monitoring of composite plates
7.3.1 Case Study (5): structural health monitoring of composite plates using electrical capacitance sensors and system control theory
7.3.1.1 Methodology
7.3.1.2 The geometric model
7.3.1.3 Damage monitoring of basalt fiber-reinforced polymer composite plate using electrical capacitance sensors
7.3.1.3.1 Voltage signal analysis in the frequency domain
7.3.1.3.2 Frequency domain analysis methods
7.3.1.4 Summary
Notations
References
Index

System requirements

Save as PDF Copy link into clipboard

Schweitzer Fachinformationen

Composite Materials and Structures

Description

More details

Other editions

Additional editions

Person

Content

System requirements