Construction scheduling, cost optimization and management : a new model based on neurocomputing and object technologies

Construction Scheduling, Cost Optimization and Management 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 modelled. This book presents an entirely new approach to the construction scheduling problem. It provides a practical methodology which will be of great benefit to all those involved in construction scheduling and cost optimization, including construction engineers, highway engineers, transportation engineers, contractors and architects. It will also be useful for researchers, and graduates on courses in construction scheduling and planning.

Author Notes

Hojjat Adeli is Professor of Civil and Environmental Engineering and Geodetic Science at The Ohio State University
Asim Karim is a Research Associate at The Ohio State University

Contents	p. iii
Preface	p. xi
Acknowledgment	p. xiii
About the Authors	p. xiv
Introduction	p. 1
Overview of Neural Networks in Civil Engineering	p. 7
2.1 Introduction	p. 7
2.2 Construction Engineering	p. 9
2.2.1 Construction Scheduling and Management	p. 9
2.2.2 Construction Cost Estimation	p. 10
2.2.3 Resource Allocation and Scheduling	p. 11
2.2.4 Construction Litigation	p. 12
2.2.5 Other Applications of BP and Other Neural Network Models in Construction Engineering and Management	p. 12
2.3 Structural Engineering	p. 13
2.3.1 Pattern Recognition and Machine Learning in Structural Analysis and Design	p. 13
2.3.2 Design Automation and Optimization	p. 19
2.3.3 Structural System Identification	p. 23
2.3.4 Structural Condition Assessment and Monitoring	p. 23
2.3.5 Structural Control	p. 26
2.3.6 Finite Element Mesh Generation	p. 27
2.3.7 Structural Material Characterization and Modeling	p. 28
2.3.8 Parallel Neural Network Algorithms for Large-Scale Problems	p. 29
2.4 Environmental and Water Resources Engineering	p. 30
2.5 Traffic Engineering	p. 32
2.6 Highway Engineering	p. 34
2.7 Geotechnical Engineering	p. 35
2.8 Shortcomings of the BP Algorithm and Other Recent Approaches	p. 36
2.8.1 Shortcomings of the BP Algorithm	p. 36
2.8.2 Adaptive Conjugate Gradient Neural Network Algorithm	p. 37
2.8.3 Radial Basis Function Neural Networks	p. 37
2.8.4 Other Approaches	p. 38
2.9 Integrating Neural Network With Other Computing Paradigms	p. 39
2.9.1 Genetic Algorithms	p. 39
2.9.2 Fuzzy Logic	p. 40
2.9.3 Wavelets	p. 42
Neural Dynamics Model and its Application to Engineering Design Optimization	p. 45
3.1 Introduction	p. 45
3.2 Cold-Formed Steel Design Optimization	p. 46
3.3 Minimum Weight Design of Cold-Formed Steel Beams	p. 47
3.3.1 Bending Strength Constraint	p. 50
3.3.2 Shear Strength Constraint	p. 55
3.3.3 Constraint on Combined Bending and Shear Strength	p. 55
3.3.4 Constraint on Web Crippling Strength	p. 56
3.3.5 Constraint on Combined Web Crippling and Bending Strength	p. 57
3.3.6 Deflection Constraint	p. 57
3.3.7 Constraint on Flange Curling	p. 58
3.3.8 Local Buckling Constraints	p. 59
3.4 Neural Dynamics Optimization Model	p. 60
3.5 Neural Dynamics Model for Optimization of Cold-Formed Steel Beams	p. 64
3.6 Application of the Model	p. 69
3.6.1 Example 1	p. 70
Example 2	p. 73
Example 3	p. 76
3.7 Global Optimum Design Curves for Hat-Shaped Beams	p. 79
3.7.1 Parametric Studies and Search for Global Optima	p. 80
3.7.2 Design Curves for Hat-Shapes	p. 83
3.8 Concluding Remarks	p. 92
Project Planning and Management and CPM	p. 95
4.1 Introduction	p. 95
4.2 What is a Project?	p. 98
4.2.1 Definition	p. 98
4.2.2 Life Cycle	p. 100
4.2.3 Participants	p. 102
4.2.4 Attributes of a Project	p. 103
4.2.5 States of a Project	p. 104
4.2.6 Project Time and Cost	p. 105
4.3 Project Planning and Management	p. 107
4.3.1 Introduction	p. 107
4.3.2 Component Models	p. 108
4.4 Elements of Project Scheduling	p. 111
4.4.1 Tasks	p. 112
4.4.2 Work Breakdown Structure	p. 113
4.4.3 Scheduling Constraints	p. 117
4.5 Graphical Display of Schedules	p. 119
4.5.1 The Need	p. 119
4.5.2 Gantt Charts	p. 120
4.5.3 Network Diagrams	p. 121
4.5.4 Linear Planning Chart	p. 124
The Critical Path Method	p. 126
4.6.1 Introduction	p. 126
4.6.2 Features	p. 128
4.6.3 Parameter in the CPM Analysis	p. 129
4.6.4 Algorithm	p. 131
4.6.5 Example	p. 134
A General Mathematical Formulation for Project Scheduling and Cost Optimization	p. 139
5.1 Introduction	p. 139
5.2 Cost-Duration Relationship of a Project	p. 143
5.3 Formulation of the Scheduling Optimization Problem	p. 145
5.3.1 Breakdown the Work into Tasks, Crews, and Segments	p. 147
5.3.2 Specify the Internal Logic of Repetitive Tasks	p. 147
5.3.3 Specify the External Logic of Repetitive and Non-Repetitive Tasks	p. 149
5.4 Conclusion	p. 153
Neural Dynamics Cost Optimization Model for Construction Projects	p. 155
6.1 Introduction	p. 155
6.2 Formulation of the Neural Dynamics Construction Cost Optimization Model	p. 155
6.3 Topological Characteristics	p. 159
6.4 Illustrative Example	p. 163
6.4.1 General Description	p. 163
6.4.2 Cost-Duration Relationship	p. 165
6.4.3 Scheduling Logic	p. 165
6.4.4 Solution of the Problem	p. 172
6.5 Conclusion	p. 174
Object-Oriented Information Model for Construction Project Management	p. 177
7.1 Introduction	p. 177
7.2 Change Order Management	p. 178
7.3 Owner's Role in Construction Project Management	p. 179
7.4 Object-Oriented Methodology and Construction Engineering	p. 181
7.5 An Object-Based Information Model for Construction Scheduling, Cost Optimization, and Change Order Management	p. 185
7.6 Software Reuse Techniques: Components, Design Patterns, and Frameworks	p. 186
7.7 Development Environment	p. 192
7.8 An Application Architecture for the Construction Domain	p. 196
7.9 Brief Description of Classes in Figure 7.6	p. 201
The CONSCOM Framework	p. 205
8.1 Introduction	p. 205
8.2 The CONSCOM Framework	p. 206
8.2.1 Introduction	p. 206
8.2.2 Object Model	p. 208
8.2.3 Model Description	p. 211
8.3 Conclusion	p. 227
8.4 Brief Description of Classes in the CONSCOM Framework (Figures 8.1-8.9)	p. 229
8.5 Brief Description of the Attributes and Operations Shown in Figures 8.3-8.9	p. 232
8.5.1 Attributes	p. 232
8.5.2 Operations	p. 233
A New Generation Software for Construction Scheduling and Management	p. 237
9.1 Introduction	p. 237
9.2 Integrated Construction Scheduling and Cost Management	p. 237
9.3 Features of CONSCOM	p. 239
9.4 Integrated Management Environment	p. 241
9.5 User Interface Characteristics	p. 244
9.6 Example--Retaining Wall Project	p. 254
9.7 Concluding Remarks	p. 259
Regularization Neural Network Model for Construction Cost Estimation	p. 261
10.1 Introduction	p. 261
10.2 Estimation, Learning and Noisy Curve Fitting	p. 263
10.3 Regularization Networks	p. 268
10.4 Determination of Weights of Regularization Network	p. 272
10.5 Proper Generalization and Estimation by Cross-Validation	p. 274
10.6 Input and Output Normalization	p. 276
10.7 Application	p. 279
10.7.1 Example 1	p. 280
10.7.2 Example 2	p. 284
10.8 Conclusion	p. 284
Bibliography	p. 289
Subject Index	p. 315

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Table of Contents