Fuzzy Hybrid Computing in Construction Engineering and Management: Theory and Applications

Description

This book provides an introduction to fuzzy logic and surveys emerging research trends and the application of state-of-the-art fuzzy hybrid computing techniques in the field of construction engineering and management. Authors cover the theory and implementation of fuzzy hybrid computing methodologies for arithmetic, optimization, machine learning, multi-criteria decision-making, simulation, cognitive maps and data modelling. The practical application of these techniques to solve real-world problems across a wide range of construction engineering and management issues is also demonstrated and discussed. The completion of effectively planned, executed and controlled construction projects is dependent on numerous interacting factors and human activities, both of which introduce vagueness and subjective uncertainty into already complex processes. While expert knowledge is an essential component of effective decision-making, analysis and consideration of expert knowledge expressed in linguistic terms remains a challenging aspect of construction engineering and management. Fuzzy logic, which has applications in many disciplines, has the potential to address certain challenges inherent in construction engineering and management, in part because of its strengths in modelling human reasoning, dealing with subjective uncertainty and computing with linguistic terms. However, fuzzy logic alone has a number of limitations that can only be overcome by its integration with other, complementary methodologies, together leading to advanced and powerful fuzzy hybrid computing techniques. This book is of particular interest to students, researchers and practitioners who want to learn about the latest developments in fuzzy hybrid computing in construction engineering and management. Front Cover ......Page 1 Fuzzy Hybrid Computing in Construction Engineering and Management: Theory and applications......Page 4 Copyright Page ......Page 5 Acknowledgements......Page 8 Contents......Page 10 List of Figures......Page 12 List of Tables......Page 18 About the Editor......Page 22 About the Authors......Page 24 Foreword......Page 28 Introduction......Page 30 Purpose and Structure of the Book......Page 31 Fuzzy Arithmetic Operations: Theory and Applications in Construction Engineering and Management......Page 32 Fuzzy Consensus and Fuzzy Aggregation Processes for Multi-criteria Group Decision-making Problems in Construction Engineering and Management......Page 33 Flexible Management of Essential Construction Tasks Using Fuzzy OLAP Cubes......Page 34 Crane Guidance Gesture Recognition Using Fuzzy Logic and Kalman Filtering......Page 35 Future Directions......Page 36 Part 1: Introduction to Fuzzy Logic and Overview of Fuzzy Hybrid Techniques in Construction Engineering and Management......Page 38 Introduction to Fuzzy Logic in Construction Engineering and Management......Page 40 List of Notations......Page 41 Fuzzy Logic for Handling Uncertainty in Construction Engineering and Management......Page 42 Fuzzy Sets and Membership Functions......Page 44 Representing Membership Functions......Page 46 Characteristics of Membership Functions......Page 48 Fuzzy Variables and Fuzzy Partitions......Page 50 Basic Set Operations on Fuzzy Sets......Page 51 Fuzzy Relations and Fuzzy Composition......Page 52 Defuzzification......Page 53 Non-probabilistic Entropy: Measuring the Degree of Fuzziness......Page 54 Fuzzy Numbers......Page 55 Direct Assignment of Membership Functions: Horizontal and Vertical Methods......Page 57 Pairwise Comparison Using the Analytic Hierarchy Process......Page 58 Statistical Methods......Page 59 Methods Based on Clustering......Page 60 Fuzzy Rule-based Systems......Page 61 Fuzzy Hybrid Modelling in Construction......Page 66 References......Page 68 Overview of Fuzzy Hybrid Techniques in Construction Engineering and Management......Page 74 Introduction......Page 75 Systematic Literature Review Methodology......Page 76 Fuzzy Hybrid Optimization......Page 78 Fuzzy Hybrid Evolutionary Models......Page 80 Fuzzy Hybrid Particle Swarm Optimization Models......Page 87 Hybridisation of Fuzzy Logic with the Artificial Neural Network Technique......Page 89 Fuzzy Clustering Techniques......Page 103 Fuzzy AHP......Page 107 Fuzzy TOPSIS......Page 117 Fuzzy VIKOR......Page 119 Fuzzy Simulation......Page 120 Fuzzy Monte Carlo Simulation......Page 121 Fuzzy Discrete Event Simulation......Page 125 Fuzzy System Dynamics......Page 127 Fuzzy Agent-based Modelling......Page 128 Fuzzy Hybrid Optimization......Page 129 Fuzzy Multi-criteria Decision-making......Page 130 Future Research Directions......Page 131 References......Page 133 Part 2: Theoretical Approaches of Fuzzy Hybrid Computing in Construction Engineering and Management......Page 146 Fuzzy Arithmetic Operations: Theory and Applications in Construction Engineering and Management......Page 148 Fuzzy Arithmetic Operations: Exact Mathematical Methods......Page 149 Exact Mathematical Method for Implementation of Standard Fuzzy Arithmetic......Page 150 Exact Mathematical Method for Implementation of Extended Fuzzy Arithmetic......Page 153 Fuzzy Arithmetic Operations: Computational Methods......Page 156 Computational Method for the Implementation of Standard Fuzzy Arithmetic......Page 158 Computational Method for the Implementation of Extended Fuzzy Arithmetic......Page 160 Extended Fuzzy Addition Using the Algebraic Product t-norm......Page 161 Extended Fuzzy Multiplication Using the Algebraic Product t-norm......Page 163 Extended Fuzzy Arithmetic Using the Bounded Difference t-norm......Page 168 Extended Fuzzy Multiplication Using the Bounded Difference t-norm......Page 169 Extended Fuzzy Multiplication Using the Drastic Product t-norm.......Page 174 Fuzzy Arithmetic Operations in Construction Applications......Page 178 Conclusions and Future Work......Page 182 References......Page 183 Fuzzy Simulation Techniques in Construction Engineering and Management......Page 186 Introduction......Page 187 Discrete Event Simulation......Page 188 System Dynamics......Page 189 Agent-based Modelling......Page 191 Limitations of Simulation Techniques......Page 192 Fuzzy Discrete Event Simulation......Page 194 Fuzzy System Dynamics......Page 196 Fuzzy Agent-based Modelling......Page 199 Step 1. Determining the Architecture of the Agent-based Model.......Page 200 Step 2. Developing the Basic Structure of Agents: Agents, Agent Attributes and Agent Behaviours.......Page 201 Step 3. Defining Protocols Governing Interactions among Agents and Determining Agent Decision-making Rules.......Page 202 Step 4. Incorporating Fuzzy Logic into the Agent-based Model.......Page 203 The Appropriate Choice of Fuzzy Simulation Techniques for Construction Modelling......Page 204 Applications of Fuzzy System Dynamics: An FSD Model of Quality Management Practice in Construction......Page 205 Fuzzy Agent-based Modelling Applications: A Fuzzy ABM Model of Construction Crew Motivation and Performance......Page 207 Conclusions and Future Work......Page 208 References......Page 210 Fuzzy Set Theory and Extensions for Multi-criteria Decision-making in Construction Management......Page 216 List of Notations......Page 217 Introduction......Page 222 MCDM Process and Methods in Construction Management......Page 223 Weighted Sum Method......Page 225 Analytic Hierarchy Process......Page 226 Technique for Order of Preference by Similarity to Ideal Solution......Page 227 Elimination and Choice Expressing Reality......Page 228 Fuzzy Set Theory and Typical Extensions......Page 229 Intuitionistic Fuzzy Sets......Page 230 Hesitant Fuzzy Sets......Page 231 Type-2 Fuzzy Sets......Page 232 Fuzzy Sets-based Weighted Sum Method......Page 234 Fuzzy Sets-based Analytic Hierarchy Process......Page 235 Fuzzy Sets-based Technique for Order of Preference by Similarity to Ideal Solution......Page 237 Fuzzy Sets-based Elimination and Choice Expressing Reality......Page 239 F-MCDM Method Applications in Construction Management......Page 240 Intuitionistic Fuzzy Sets-based Analytic Hierarchy Process......Page 242 Intuitionistic Fuzzy Sets-based Technique for Order of Preference by Similarity to Ideal Solution......Page 243 Intuitionistic Fuzzy Sets-based Elimination and Choice Expressing Reality......Page 244 Intuitionistic Fuzzy Sets-based Preference Ranking Organisation Method Enrichment Evaluation......Page 246 Hesitant Fuzzy Sets-based Analytic Hierarchy Process......Page 247 Hesitant Fuzzy Sets-based Technique for Order of Preference by Similarity to Ideal Solution......Page 249 Hesitant Fuzzy Sets-based Elimination and Choice Expressing Reality......Page 250 Type-2 Fuzzy Sets-based Weighted Sum Method......Page 251 Type-2 Fuzzy Sets-based Analytic Hierarchy Process......Page 252 Type-2 Fuzzy Sets-based Technique for Order of Preference by Similarity to Ideal Solution......Page 253 Type-2 Fuzzy Sets-based Elimination and Choice Expressing Reality......Page 255 T2FS-MCDM Method Applications in Construction Management......Page 256 Conclusions......Page 257 References......Page 259 Fuzzy Consensus and Fuzzy Aggregation Processes for Multi-criteria Group Decision-making Problems in Construction Engineering and Management......Page 266 List of Notations......Page 267 Introduction......Page 268 Fuzzy Consensus-reaching Process......Page 270 Importance Degree of Experts......Page 271 Preference Representation Formats......Page 272 Fuzzy Consensus-reaching Process for Multi-criteria Group Decision-making Problems......Page 273 Fuzzy Aggregation Processes for Constructing Collective Opinions......Page 279 Classification of Fuzzy Aggregation Operators and Their Properties......Page 280 Fuzzy Weighted Average......Page 282 Linguistic Ordered Weighted Averaging......Page 283 Fuzzy Number Induced Ordered Weighted Averaging......Page 285 Fuzzy Prioritised Weighted Aggregation Operators......Page 288 The Fuzzy TOPSIS-based Approach for Prioritised Aggregation......Page 294 Fuzzy Consensus Reaching and Aggregation in Construction Industry Applications......Page 300 Building Design Applications......Page 301 Risk Analysis and Hazard Assessment Applications......Page 302 Construction Procurement and Project Delivery Applications......Page 303 Construction Bidding Applications......Page 305 Construction Productivity Applications......Page 306 Conclusions and Recommendations for Future Work......Page 307 References......Page 308 Fuzzy AHP with Applications in Evaluating Construction Project Complexity......Page 314 List of Notations......Page 315 Introduction......Page 316 Fuzzy Extensions of the Analytical Hierarchy Process......Page 317 Fuzzy Pairwise Comparisons......Page 318 Consistency of Fuzzy Pairwise Comparisons......Page 321 Fuzzy Weights and Defuzzification......Page 322 The Hierarchical Structure of Project Complexity......Page 325 Fuzzy Pairwise Comparison Matrices......Page 326 Local and Global Weights of the Criteria and Sub-criteria......Page 327 Project Complexity and Performance......Page 330 Conclusions......Page 332 References......Page 334 Part 3: Applications of Fuzzy Hybrid Computing in Construction Engineering and Management......Page 338 The Fuzzy Analytic Hierarchy Process in the Investment Appraisal of Drilling Methods......Page 340 Introduction......Page 341 Risk Analysis in Projects......Page 342 Fuzzy Logic......Page 343 Fuzzy AHP in Risk Analysis......Page 345 Application of Fuzzy AHP Techniques in Oil Drilling......Page 347 Case Study......Page 348 Subsea Solution, Tied Back to Harald through a 7 km Long Pipe......Page 349 Economic Screening......Page 350 Dry-hole Risk for the Subsea Option......Page 351 Production Risk for the Subsea Option......Page 352 Fuzzy AHP Technique......Page 355 Fuzzy AHP Investment Appraisal for Oil Drilling Methods......Page 360 Fuzzy AHP Computation in the Subsea Drilling Option......Page 361 Discussion......Page 368 Conclusion and Recommendations......Page 369 References......Page 370 Modelling Risk Allocation Decisions in Public–Private Partnership Contracts using the Fuzzy Set Approach......Page 374 Introduction......Page 375 Previous Studies on Risk Allocation in PPPs......Page 376 Decision Criteria for Defining RM Capability......Page 378 Fuzzy Synthetic Evaluation and Risk Allocation......Page 379 Case Study......Page 380 Round Three of Delphi Survey for Risk Allocation......Page 381 Step 1: Establish Decision Criteria and Their Weightings to Assess RM Capability......Page 382 Representation of Fuzzy Set and Membership Functions......Page 383 Membership Functions of Decision Criteria and Relational Matrix......Page 384 Step 5: Determine the Fuzzy Evaluation Vector of RM Capability Using the Weighted Mean Model......Page 385 Step 7: Risk Allocation Decision......Page 387 Practitioners’ Feedback on the Methodology......Page 388 References......Page 389 Flexible Management of Essential Construction Tasks Using Fuzzy OLAP Cubes......Page 394 Introduction......Page 395 A General Idea About the Multi-dimensional Structure......Page 396 Operations......Page 397 The Fuzzy Multi-dimensional Model......Page 399 Structure......Page 400 Cost Management......Page 401 Safety Analysis......Page 402 Planning Analysis......Page 403 The Proposed Fuzzy Multi-dimensional Structure......Page 404 Time......Page 406 Project......Page 407 Type of Construction......Page 408 Task (WBS)......Page 409 Company......Page 411 Location......Page 412 Worker......Page 414 Injury......Page 415 Example of Queries Resolution......Page 416 Conclusions......Page 419 References......Page 421 Using an Adaptive Neuro-fuzzy Inference System for Tender Price Index Forecasting: A Univariate Approach......Page 426 Introduction......Page 427 The Need for the Application of Univariate Modelling Techniques in Tender Price Index Forecasting Research......Page 428 Box–Jenkins Model......Page 430 Adaptive Neuro-fuzzy Inference System......Page 431 Data......Page 432 Input Selection and Prediction Modelling......Page 433 Forecast Evaluation......Page 434 Box–Jenkins......Page 435 ANFIS Model......Page 436 SVM Model......Page 437 Forecast Accuracy of the Developed Models......Page 438 Discussion......Page 439 Practical Implications......Page 440 Limitations, Directions for Future Studies and Potential Applications of the Proposed Techniques......Page 441 Conclusion......Page 442 References......Page 443 Appendix: R-Code for Adaptive Neuro-fuzzy Inference System......Page 447 Modelling Construction Management Problems with Fuzzy Cognitive Maps......Page 450 Construction Management Benefits......Page 451 Enhancing Construction Management Tools and Practices with FCM......Page 454 Fuzzy Cognitive Map Modelling......Page 456 Construction Engineering and Project Management......Page 460 FCM Example 1: Simple Cause-and-effect Analysis......Page 461 FCM Example 2: Compound Cause-and-effect Analysis......Page 470 FCM Example 3: Complex Cause-and-effect Example......Page 471 Conclusions......Page 473 References......Page 484 List of Notations......Page 488 Introduction......Page 489 Dynamic Modelling of Arm Gestures......Page 490 Nonlinear System Dynamics......Page 492 Nonlinear Kalman Filtering-based Gesture Tracking and Sensor Fusion......Page 493 Extended Kalman Filter......Page 494 Unscented Kalman Filter......Page 495 Sugeno-type Fuzzy Inference System......Page 497 Motion Capture with Kinect Camera and Myo Armband Sensors......Page 500 Experimental Results......Page 503 Conclusion......Page 508 References......Page 509 Index......Page 512