Construction scheduling, cost optimization, and management : a new model based on neurocomputing and object technologies / Hojjat Adeli, Asim Karim.

Format:

Book

Author/Creator:

Adeli, Hojjat, 1950-

Contributor:

Karim, Asim Salimul, 1971-

Language:

English

Subjects (All):

Construction industry--Management.

Construction industry.

Production scheduling.

Building--Cost control.

Building.

Physical Description:

xvi, 320 pages : illustrations ; 25 cm

Place of Publication:

London ; New York : Spon Press, 2001.

Summary:

Presents a general mathematical formula for the scheduling of construction projects. Using this formula, repetitive and non-repetitive tasks, work continuity considerations, multiple-crew strategies, and the effects of varying job conditions on the performance of a crew can be modeled. It provides a practical methodology which will be of great benefit to all those involved in construction scheduling and cost optimization.

Contents:

Overview of Neural Networks in Civil Engineering 7

2.2 Construction Engineering 9

2.2.1 Construction Scheduling and Management 9

2.2.2 Construction Cost Estimation 10

2.2.3 Resource Allocation and Scheduling 11

2.2.4 Construction Litigation 12

2.2.5 Other Applications of BP and Other Neural Network Models in Construction Engineering and Management 12

2.3 Structural Engineering 13

2.3.1 Pattern Recognition and Machine Learning in Structural Analysis and Design 13

2.3.2 Design Automation and Optimization 19

2.3.3 Structural System Identification 23

2.3.4 Structural Condition Assessment and Monitoring 23

2.3.5 Structural Control 26

2.3.6 Finite Element Mesh Generation 27

2.3.7 Structural Material Characterization and Modeling 28

2.3.8 Parallel Neural Network Algorithms for Large-Scale Problems 29

2.4 Environmental and Water Resources Engineering 30

2.5 Traffic Engineering 32

2.6 Highway Engineering 34

2.7 Geotechnical Engineering 35

2.8 Shortcomings of the BP Algorithm and Other Recent Approaches 36

2.8.1 Shortcomings of the BP Algorithm 36

2.8.2 Adaptive Conjugate Gradient Neural Network Algorithm 37

2.8.3 Radial Basis Function Neural Networks 37

2.8.4 Other Approaches 38

2.9 Integrating Neural Network With Other Computing Paradigms 39

2.9.1 Genetic Algorithms 39

2.9.2 Fuzzy Logic 40

2.9.3 Wavelets 42

Neural Dynamics Model and its Application to Engineering Design Optimization 45

3.2 Cold-Formed Steel Design Optimization 46

3.3 Minimum Weight Design of Cold-Formed Steel Beams 47

3.3.1 Bending Strength Constraint 50

3.3.2 Shear Strength Constraint 55

3.3.3 Constraint on Combined Bending and Shear Strength 55

3.3.4 Constraint on Web Crippling Strength 56

3.3.5 Constraint on Combined Web Crippling and Bending Strength 57

3.3.6 Deflection Constraint 57

3.3.7 Constraint on Flange Curling 58

3.3.8 Local Buckling Constraints 59

3.4 Neural Dynamics Optimization Model 60

3.5 Neural Dynamics Model for Optimization of Cold-Formed Steel Beams 64

3.6 Application of the Model 69

3.6.1 Example 1 70

Example 2 73

Example 3 76

3.7 Global Optimum Design Curves for Hat-Shaped Beams 79

3.7.1 Parametric Studies and Search for Global Optima 80

3.7.2 Design Curves for Hat-Shapes 83

Project Planning and Management and CPM 95

4.2 What is a Project? 98

4.2.2 Life Cycle 100

4.2.3 Participants 102

4.2.4 Attributes of a Project 103

4.2.5 States of a Project 104

4.2.6 Project Time and Cost 105

4.3 Project Planning and Management 107

4.3.2 Component Models 108

4.4 Elements of Project Scheduling 111

4.4.1 Tasks 112

4.4.2 Work Breakdown Structure 113

4.4.3 Scheduling Constraints 117

4.5 Graphical Display of Schedules 119

4.5.1 The Need 119

4.5.2 Gantt Charts 120

4.5.3 Network Diagrams 121

4.5.4 Linear Planning Chart 124

The Critical Path Method 126

4.6.2 Features 128

4.6.3 Parameter in the CPM Analysis 129

4.6.4 Algorithm 131

A General Mathematical Formulation for Project Scheduling and Cost Optimization 139

5.2 Cost-Duration Relationship of a Project 143

5.3 Formulation of the Scheduling Optimization Problem 145

5.3.1 Breakdown the Work into Tasks, Crews, and Segments 147

5.3.2 Specify the Internal Logic of Repetitive Tasks 147

5.3.3 Specify the External Logic of Repetitive and Non-Repetitive Tasks 149

Neural Dynamics Cost Optimization Model for Construction Projects 155

6.2 Formulation of the Neural Dynamics Construction Cost Optimization Model 155

6.3 Topological Characteristics 159

6.4 Illustrative Example 163

6.4.1 General Description 163

6.4.2 Cost-Duration Relationship 165

6.4.3 Scheduling Logic 165

6.4.4 Solution of the Problem 172

Object-Oriented Information Model for Construction Project Management 177

7.2 Change Order Management 178

7.3 Owner's Role in Construction Project Management 179

7.4 Object-Oriented Methodology and Construction Engineering 181

7.5 An Object-Based Information Model for Construction Scheduling, Cost Optimization, and Change Order Management 185

7.6 Software Reuse Techniques: Components, Design Patterns, and Frameworks 186

7.7 Development Environment 192

7.8 An Application Architecture for the Construction Domain 196

7.9 Brief Description of Classes in Figure 7.6 201

The CONSCOM Framework 205

8.2 The CONSCOM Framework 206

8.2.2 Object Model 208

8.2.3 Model Description 211

8.4 Brief Description of Classes in the CONSCOM Framework (Figures 8.1-8.9) 229

8.5 Brief Description of the Attributes and Operations Shown in Figures 8.3-8.9 232

8.5.1 Attributes 232

8.5.2 Operations 233

A New Generation Software for Construction Scheduling and Management 237

9.2 Integrated Construction Scheduling and Cost Management 237

9.3 Features of CONSCOM 239

9.4 Integrated Management Environment 241

9.5 User Interface Characteristics 244

9.6 Example

Retaining Wall Project 254

Regularization Neural Network Model for Construction Cost Estimation 261

10.2 Estimation, Learning and Noisy Curve Fitting 263

10.3 Regularization Networks 268

10.4 Determination of Weights of Regularization Network 272

10.5 Proper Generalization and Estimation by Cross-Validation 274

10.6 Input and Output Normalization 276

10.7 Application 279.

Notes:

Includes bibliographical references (pages 289-313) and index.

ISBN:

041524417X

OCLC:

45376334