Engineering Informatics Fundamentals of Computer-Aided Engineering

Computers are ubiquitous throughout all life-cycle stages of engineering, from conceptual design to manufacturing maintenance, repair and replacement.  It is essential for all engineers to be aware of the knowledge behind computer-based tools and techniques they are likely to encounter. The computational technology, which allows engineers to carry out design, modelling, visualisation,  manufacturing, construction and management of products and infrastructure is known as Computer-Aided Engineering (CAE).

Engineering Informatics: Fundamentals of Computer-Aided Engineering, 2nd Edition provides the foundation knowledge of computing that is essential for all engineers. This knowledge is independent of hardware and software characteristics and thus, it is expected to remain valid throughout an engineering career. This Second Edition is enhanced with treatment of new areas such as network science and the computational complexity of distributed systems.

Key features: 

• Provides extensive coverage of almost all aspects of Computer-Aided Engineering, outlining general concepts such as fundamental logic, definition of engineering tasks and computational complexity
• Every chapter revised and expanded following more than ten years of experience teaching courses on the basis of the first edition
• Covers numerous representation frameworks and reasoning strategies
• Considers the benefits of increased computational power, parallel computing and cloud computing
• Offers many practical engineering examples and exercises, with lecture notes available for many of the topics/chapters from the ASCE Technical Council on Computing and Information Technology, Global Centre of Excellence in Computing (www.asceglobalcenter.org), providing a valuable resource for lecturers.
• Accompanied by a website hosting updates and solutions

Engineering Informatics: Fundamentals of Computer-Aided Engineering, 2nd Edition provides essential knowledge on computing theory in engineering contexts for students, researchers and practising engineers.

Benny Raphael, National University of Singapore, Singapore


Ian Smith, EPFL, Lausanne, Switzerland


Ian Smith is Professor and Head of the Applied Computing and Mechanics Laboratory within the Civil Engineering Institute in the School of Architecture, Civil and Environmental Engineering at EPFL. He has also been active in consulting related to monitoring structures, applications of information technology, structural design, evaluation and repair of existing structures and accident analysis in Europe, North America and Japan. He was elected to the Swiss Academy of Engineering Sciences in 2004 and received the Computing in Civil Engineering Award from the Amercian Society of Civil Engineers in 2005. HE is Editor of the"Journal of Advanced Engineering Informatics", and Associate Editor of "Journal of Artificial Intelligence for Engineering Design Analysis and Manufacturing".

Preface to the First Edition xvii

Preface to the Second Edition xxi

1 Fundamental Logic and the Definition of Engineering Tasks 1

1.1 Three Types of Inference 1

1.2 Engineering Tasks 3

1.3 A Model of Information and Tasks 5

1.4 Another Task Definition 8

1.5 The Five Orders of Ignorance 9

1.6 Summary 9

Exercises 10

References 10

2 Algorithms and Complexity 11

2.1 Algorithms and Execution Time of Programs 12

2.1.1 Program Execution Time versus Task Size 12

2.2 ‘Big Oh’ Notation 14

2.2.1 Definition of the Big Oh Notation 15

2.2.2 Big Oh and Tightness of Bound 16

2.2.3 Classification of Functions 20

2.2.4 Examples 21

2.2.5 Tractability and Algorithm Optimality 30

2.3 Practical Methods for Determining the Complexity of Algorithms 30

2.4 P, NP and NP-Completeness 34

2.4.1 Zero–One Integer Programming (ZOIP) Problem 35

2.4.2 Classes of NP-Complete Problems 36

2.5 Summary 37

Exercises 37

Reference 40

Further Reading 40

3 Data Structures 41

3.1 Introduction 41

3.2 Definitions 42

3.3 Derived Data Types 42

3.3.1 Examples of Derived Data Types 43

3.3.2 User-Defined Data Types 45

3.4 Abstract Data Types 46

3.4.1 Linked Lists 47

3.4.2 Graphs 50

3.4.3 Trees 52

3.4.4 Stacks 56

3.4.5 Queues 60

3.5 An Example: Conceptual Structural Design of Buildings 63

3.6 Network Science 70

3.6.1 Types of Networks 71

3.7 Hashing 73

3.8 Summary 74

Exercises 74

Further Reading 79

4 Object Representation and Reasoning 81

4.1 Introduction 81

4.2 Grouping Data and Methods 82

4.3 Definitions and Basic Concepts 83

4.3.1 Classes and Objects 83

4.3.2 Object-Oriented Programming (OOP) 84

4.3.3 Messages 84

4.4 Important Characteristics of Objects 84

4.4.1 Encapsulation of Data and Methods 84

4.4.2 Message-Passing Mechanism 85

4.4.3 Abstraction Hierarchy 86

4.4.4 Secondary Features of Object Representation 88

4.4.5 Decomposition versus Abstraction 89

4.5 Applications Outside Programming 90

4.5.1 Knowledge Representation 91

4.5.2 User Interfaces 91

4.5.3 Off-the-Shelf Components 91

4.5.4 Product Models 91

4.6 An Object-Oriented Design Methodology 93

4.6.1 Single versus Multiple Inheritance 93

4.6.2 Message-Passing Architecture 94

4.7 Summary 95

Exercises 95

References 101

Further Reading 101

5 Database Concepts 103

5.1 Introduction 103

5.2 Basic Concepts 104

5.2.1 Initial Definitions 104

5.2.2 Evolution of Types of Databases 104

5.2.3 The Three-Level Architecture 106

5.3 Relational Database Systems 106

5.3.1 The Relational Model 107

5.3.2 Limitations of Relational Databases 111

5.3.3 Accessing Data in Relational Databases 112

5.4 Relational Database Design 114

5.4.1 First Normal Form 114

5.4.2 Second Normal Form 115

5.4.3 Third Normal Form 118

5.4.4 Boyce-Codd and Higher Normal Forms 119

5.4.5 Importance of Database Design 120

5.5 Transaction Processing 120

5.5.1 Definition of Transaction 121

5.5.2 Implementing Transactions 122

5.5.3 Properties of Transactions 124

5.6 Other Types of Database 124

5.6.1 Object-Oriented Databases 124

5.6.2 Geographical Databases 124

5.6.3 Multimedia Database Systems 125

5.6.4 Distributed Databases 125

5.7 Summary 126

Exercises 127

Transaction A 131

Transaction B 131

Reference 131

Further Reading 131

6 Computational Mechanics 133

6.1 Introduction 133

6.1.1 Challenges of Computational Mechanics 134

6.2 From Physical Principles to Practical Systems 135

6.3 Methods for Finding Solutions 137

6.3.1 Galerkin Method 137

6.3.2 Remarks 139

6.4 Issues in Computer-Aided Engineering 139

6.4.1 Accuracy 140

6.4.2 Speed 141

6.4.3 User Interaction 142

6.5 Summary 142

References 142

Further Reading 142

7 Constraint-Based Reasoning 143

7.1 Introduction 143

7.2 Terminology 145

7.3 Constraint-Solving Methods 146

7.3.1 Levels of Consistency for Label Propagation 147

7.3.2 Global Consistency in Label Propagation 148

7.3.3 Constraint Propagation 149

7.4 Reasoning with Constraints on Discrete Variables 149

7.4.1 CSP Complexity for Discrete Variables 151

7.5 Reasoning with Constraints on Continuous Variables 151

7.5.1 Constraint-Based Support for Collaborative Work 152

7.6 Summary 156

References 156

8 Optimization and Search 157

8.1 Introduction 157

8.2 Basic Concepts 158

8.2.1 Types of Optimization Problem 160

8.2.2 Formulating Optimization Tasks 161

8.2.3 Representing Search Spaces 163

8.2.4 Representing Constraints 164

8.2.5 Some Optimization Problems 165

8.3 Classification of Methods 167

8.4 Deterministic Optimization and Search 169

8.4.1 Special Cases 169

8.4.2 Deterministic Methods 174

8.5 Stochastic Methods 179

8.5.1 Pure Global Random Search 182

8.5.2 Local Search with Multiple Random Starts 182

8.5.3 Simulated Annealing 182

8.5.4 Genetic Algorithms 184

8.5.5 Controlled Random Search 184

8.5.6 PGSL 185

8.6 A Closer Look at Genetic Algorithms 188

8.6.1 Representation: Genetic Encoding 188

8.6.2 Evaluating an Individual 189

8.6.3 Creating the Initial Population 189

8.6.4 The Fitness Function 190

8.6.5 Reproduction 190

8.6.6 Mutation 192

8.7 Summary of Methods 192

Exercises 193

References 198

Further Reading 198

9 Knowledge Systems for Decision Support 199

9.1 Introduction 199

9.2 Important Characteristics of Knowledge Systems 200

9.3 Representation of Knowledge 202

9.3.1 Representation of Knowledge in Knowledge Systems 204

9.4 Reasoning with Knowledge 205

9.4.1 Rule Selection and Conflict Resolution 207

9.5 Importance of the User Interface 207

9.6 Maintenance of Knowledge 208

9.7 Model-based Reasoning 209

9.8 Case-Based Reasoning 209

9.8.1 Stages of Case-Based Reasoning 210

9.9 Summary 215

Reference 215

Further Reading 215

10 Machine Learning 217

10.1 Introduction 217

10.2 Improving Performance with Experience 218

10.3 Formalizing the Learning Task 220

10.3.1 Searching Hypothesis Spaces 224

10.4 Learning Algorithms 224

10.4.1 Rote Learning 225

10.4.2 Statistical Learning Techniques 226

10.4.3 Deductive Learning 230

10.4.4 Exploration and Discovery 231

10.5 A Closer Look at Artificial Neural Networks 231

10.5.1 Types of Neural Network 235

10.5.2 Learning in Neural Networks 236

10.5.3 Summary of Neural Networks 237

10.6 Support Vector Machines 237

10.6.1 Support Vector Classification 237

10.6.2 Support Vector Regression 240

10.7 Summary 240

Exercises 241

References 242

Further Reading 242

11 Geometric Modelling 243

11.1 Introduction 243

11.2 Engineering Applications 244

11.2.1 Criteria for Evaluating Representations 244

11.3 Mathematical Models for Representing Geometry 245

11.3.1 Two-Dimensional Representation of Simple Shapes 245

11.3.2 Curves Without Simple Mathematical Representations 247

11.3.3 B´ezier Curves 248

11.3.4 Mathematical Representation of Simple Surfaces 249

11.3.5 B´ezier Patches 250

11.3.6 Mathematical Representation of Regular-Shaped Solids 251

11.4 Representing Complex Solids 252

11.4.1 Primitive Instancing 252

11.4.2 Mesh Representations 253

11.4.3 Sweep Representations 255

11.4.4 Boundary Representations 257

11.4.5 Decomposition Models 258

11.4.6 Constructive Solid Geometry (CSG) 260

11.5 Applications 263

11.5.1 Estimation of Volume 263

11.5.2 Finite Element Mesh for a Spread Footing 264

11.5.3 3D Graphical View of a Structure 266

11.6 Summary 267

Further Reading 267

12 Computer Graphics 269

12.1 Introduction 269

12.2 Tasks of Computer Graphics 270

12.3 Display Devices 270

12.3.1 Types of Display Device 271

12.3.2 From Geometric Representations to Graphical Displays 272

12.4 Representing Graphics 272

12.4.1 Representing Colours 273

12.4.2 Coordinate System 273

12.4.3 Bitmap Representations 274

12.4.4 Higher-Level Representations 275

12.5 The Graphics Pipeline 276

12.5.1 Modelling Transformations 276

12.5.2 Viewing Transformations 280

12.5.3 Scan Conversion 285

12.6 Interactive Graphics 287

12.7 Graphical User Interfaces (GUI) and Human–Computer Interaction (HCI) 288

12.7.1 Engineer–Computer Interaction 288

12.8 Applications 289

12.8.1 4D Simulations 289

12.8.2 Navigating Multidimensional Solution Spaces 289

12.8.3 Computer Vision and Image Processing 290

12.8.4 Laser Scanning 290

12.9 Summary 292

References 292

Further Reading 292

13 Distributed Applications and the Web 293

13.1 Introduction 293

13.1.1 A Simple Example of a Client–Server System 294

13.1.2 Definitions 295

13.1.3 Trends Driving C/S Architecture 296

13.2 Examples of Client–Server Applications 297

13.2.1 File Servers 297

13.2.2 FTP Servers 298

13.2.3 Database Servers 298

13.2.4 Groupware Servers 298

13.2.5 Object Servers 298

13.2.6 Operating System Servers 299

13.2.7 Display Servers 299

13.2.8 Web Servers 300

13.2.9 Application Servers 300

13.3 Distinctive Features of C/S Systems 300

13.3.1 Asymmetrical Protocol 300

13.3.2 Message-Based Mechanism 301

13.3.3 Why are Protocols Important? 304

13.4 Client–server System Design 304

13.4.1 Three-Tier Architecture 305

13.4.2 Application Partitioning 306

13.5 Advantages of Client–Server Systems 307

13.6 Developing Client–Server Applications 307

13.6.1 TCP/IP Sockets 308

13.6.2 Other Middleware Options 309

13.7 The World Wide Web 309

13.7.1 Limitations of Exchanging Only Static Information 310

13.7.2 Common Gateway Interface 310

13.7.3 Engineering Applications on the Web 311

13.7.4 Other Models for Dynamic Information Exchange 311

13.8 Peer-to-Peer Networks 312

13.8.1 Information Interchange Through P2P Networks 314

13.8.2 P2P Networks for Engineering Applications 314

13.8.3 Advantages of Peer-to-Peer Networks 315

13.8.4 Issues and Challenges 315

13.9 Agent Technology 316

13.9.1 Issues in Multi-Agent Systems 317

13.10 Cloud Computing 318

13.11 Complexity 319

13.12 Summary 319

Reference 320

Further Reading 320

Index 321

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