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Features

Imparts a quick and practical understanding of the physical background and inverse analysis tools used in the nondestructive evaluation of solids and structures
Clearly describes the nature and characteristics of inverse problems
Presents effective regularization and optimization techniques developed by the author´s research group and many others
Closely examines and numerically tests a variety of computational techniques with examples of force/source reconstruction, crack detection, flaw identification, and material characterization
Presents broad applications of computational inverse techniques in other important areas, including MEMS, electronic systems, life science, and nano-technology
Summary

Ill-posedness. Regularization. Stability. Uniqueness. To many engineers, the language of inverse analysis projects a mysterious and frightening image, an image made even more intimidating by the highly mathematical nature of most texts on the subject. But the truth is that given a sound experimental strategy, most inverse engineering problems can be well-posed and not difficult to deal with.

Computational Inverse Techniques in Nondestructive Evaluation sets forth in clear, easy-to-understand terms the principles, computational methods, and algorithms of inverse analyses based on elastic waves or the dynamic responses of solids and structures. After describing the features of inverse problems, the authors discuss the regularization methods useful in handling ill-posed problems. The book also presents practical optimization algorithms, including some developed and successfully tested by his research group.

Inverse analyses are fast becoming one of the engineer´s most powerful tools in nondestructive evaluation and testing. With straightforward examples, a wealth of specific applications, and clear exposition written by engineers for engineers, this book offers an outstanding opportunity to overcome any trepidation and begin using inverse analysis in practice.



Table of Contents

INTRODUCTION
Forward and Inverse Problems Encountered in Structural Systems
General Procedures to Solve Inverse Problems
Outline of the Book
FUNDAMENTALS OF INVERSE PROBLEMS
A Simple Example: A Single-Bar
A Slightly Complex Problem: A Composite Bar
Type III Ill-Posedness
Types of Ill-Posed Inverse Problems
Explicit Matrix Systems
Inverse Solution for Systems with Matrix Form
General Inversion by Singular Value Decomposition (SVD)
Systems in Functional Forms: Solution by Optimization
Choice of the Outputs or Effects
Simulated Measurement
Examination of Ill-Posedness
REGULARIZATION FOR ILL-POSED PROBLEMS
Tikhonov Regularization
Regularization by SVD
Iterative Regularization Method
Regularization by Discretization (Projection)
Regularization by Filtering
CONVENTIONAL OPTIMIZATION TECHNIQUES1
The Role of Optimization in Inverse Problems
Optimization Formulations
Direct Search
Gradient-Based Methods
Nonlinear Least Squares Method
Some References for Optimization Methods
GENETIC ALGORITHMS
Introduction
Basic Concept of GAs
Micro-GAs
Intergeneration Project Genetic Algorithm (IP-GA)
Improved IP-GA
IP-GA with Three Parameters (IP3-GA)
GAs with Search Space Reduction (SR-GA)
GA Combined with the Gradient-Based Method
Other Minor Tricks in the Implementation of GAs for Inverse Problems
Some References for GA
NEURAL NETWORKS
General Concepts of Neural Networks
Role of Neural Networks in Solving Inverse Problems
Multilayer Perceptrons
Performance of MLP
A Progressive Learning Neural Network
A Simple Application of NN
References on Neural Networks
INVERSE IDENTIFICATION OF IMPACT LOADS
Introduction
Displacement as System Effects
Identification of Impact Loads on the Surface of Beams
Line Loads on the Surface of Composite Laminates
Point Loads on the Surface of Composite Laminates
Ill-Posedness Analysis
INVERSE IDENTIFICATION OF MATERIAL CONSTANTS OF COMPOSITES
Introduction
Statement of the Problem
Using the Uniform mGA
Using the Real mGA
Using the Combined Optimization Method
Using the Progressive NN for Identifying Elastic Constants
INVERSE IDENTIFICATION OF MATERIAL PROPERTY OF FUNCTIONALLY GRADED MATERIALS
Introduction
Statement of the Problem
Rule-of-Mixture
Use of Gradient-Based Optimization Methods
Use of Uniform mGA
Use of Combined Optimization Method
Use of Progressive NN Model
INVERSE DETECTION OF CRACKS IN BEAMS USING FLEXURAL WAVES
Introduction
Beams with a Horizontal Delamination
Beam Model of Flexural Wave
Beam Model of for Transient Response to an Impact Load
Extensive Experimental Study
Inverse Crack Detection Using Uniform mGA
Inverse Crack Detection Using Progressive NN
INVERSE DETECTION OF DELAMINATIONS IN COMPOSITE LAMINATES
Introduction
Statement of the Problem
Delamination Detection Using Uniform mGA
Delamination Detection Using the IP-GA
Delamination Detection Using the Improved IP-GA
Delamination Detection Using the Combined Optimization Method
Delamination Detection Using the Progressive NN
INVERSE DETECTION OF FLAWS IN STRUCTURES
Introduction
Inverse Identification Formulation
Use of Uniform mGA
Use of Newton´s Root Finding Method
Use of Levenberg -Marquardt Method
OTHER APPLICATIONS
Coefficients Identification for Electronic Cooling System
Identification of the Material Parameters of a PCB
Identification of Material Property of Thin Films
Crack Detection Using Integral Strain Measured by Optic Fibers
Flaw Detection in Truss Structure
Protein Structure Prediction
Fitting of Interatomic Potentials
Parameter Identification in Valve-Less Micropumps
TOTAL SOLUTION FOR ENGINEERING SYSTEMS: A NEW CONCEPT
Introduction
Approach Towards a Total Solution
Inverse Algorithms
Numerical Examples