Computational Inverse Techniques in Nondestructive Evaluation: 1st Edition (Hardback) book cover

Computational Inverse Techniques in Nondestructive Evaluation

1st Edition

By G.R. Liu, X. Han

CRC Press

592 pages | 284 B/W Illus.

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pub: 2003-06-27
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Description

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

Subject Categories

BISAC Subject Codes/Headings:
TEC009020
TECHNOLOGY & ENGINEERING / Civil / General
TEC009070
TECHNOLOGY & ENGINEERING / Mechanical
TEC021000
TECHNOLOGY & ENGINEERING / Material Science