Metaheuristic Applications in Structures and Infrastructures

Front Cover......Page 1
Metaheuristic Applications in Structures and Infrastructures......Page 4
Copyright Page......Page 5
Contents......Page 6
List of Contributors......Page 18
1.1 Introduction......Page 22
1.2.1 Characteristics of Metaheuristics......Page 23
1.3 Metaheuristic Algorithms in Modeling......Page 24
1.3.1 Artificial Neural Networks......Page 25
1.3.2 Genetic Programming......Page 26
1.3.2.1 Linear-Based GP......Page 27
1.3.2.1.2 Gene Expression Programming......Page 28
1.3.3 Fuzzy Logic......Page 29
1.3.4 Support Vector Machines......Page 30
1.4 Metaheuristic Algorithms in Optimization......Page 31
1.4.1.1 Genetic Algorithm......Page 32
1.4.1.3 Harmony Search......Page 33
1.4.2.1 Particle Swarm Optimization......Page 34
1.4.2.2 Ant Colony Optimization......Page 35
1.4.2.4 Firefly Algorithm......Page 36
1.4.2.6 Bat Algorithm......Page 37
1.4.2.8 Krill Herd......Page 38
References......Page 39
2.1 Optimization Problems......Page 46
2.3 Optimization History......Page 47
2.4.2 Major Advances in Structural Optimization......Page 51
2.4.3 OC Methods......Page 53
2.4.4 Reliability-Based Optimization Approach......Page 54
2.4.5 Fuzzy Optimization......Page 55
2.5.1 Genetic Algorithm......Page 56
2.5.2 Simulated Annealing......Page 57
2.5.4 Ant Colony Optimization......Page 58
2.5.7 Big Bang–Big Crunch......Page 60
2.5.10 Other Metaheuristics......Page 61
References......Page 62
3.1 Introduction......Page 70
3.2 Particle Swarm Optimization......Page 71
3.3.1 Shape and Size Optimization Problems in Structural Design......Page 73
3.3.2 Structural Condition Assessment and Health Monitoring......Page 74
3.4.1 Transportation Network Design......Page 75
3.4.3 Traffic Control......Page 76
3.4.6 Other PSO Application in Transportation and Traffic Engineering......Page 77
3.5.2 Design Optimization of Water/Wastewater Distribution Networks......Page 78
3.5.3 Reservoir Operation Problems......Page 79
3.5.5 Other PSO Applications in Hydraulics and Hydrology......Page 80
3.6.1 Construction Planning and Management......Page 81
3.6.4 Other PSO Applications in Construction Engineering......Page 82
3.7.2 Slope Stability Analysis......Page 83
3.10 Concluding Remarks......Page 84
References......Page 85
One: Structural Design......Page 98
4.1 Introduction......Page 100
4.2 Literature Survey......Page 101
4.3 The Structural Optimization Problem......Page 103
4.3.2 Shape Optimization......Page 104
4.3.3 Topology Optimization......Page 105
4.4 Problem Formulations......Page 106
4.4.2.1 Criteria and Conflict......Page 107
4.4.2.2 Formulation of a Multiple Objective Optimization Problem......Page 108
4.5.1.1 Evolution Strategies......Page 109
4.5.1.2.2 New Mean Value Vector......Page 110
4.5.1.2.3 Global Step Size......Page 111
4.5.1.3 Elitist CMA......Page 112
4.5.2.2 Strength Pareto Evolution Strategies......Page 114
4.6 39-Bar Truss—Test Example......Page 115
References......Page 120
5.1 Introduction......Page 124
5.2 Coupled Multidisciplinary System......Page 125
5.3 Classifications of MDO Formulations......Page 126
5.4.1 Multiple-Discipline Feasible......Page 127
5.4.3 Individual-Discipline Feasible......Page 128
5.5.1 Concurrent Subspace Optimization......Page 129
5.5.3 Collaborative Optimization......Page 130
5.6 Optimization Algorithms......Page 131
5.6.1 Direct Search Methods......Page 132
5.6.3 Metaheuristic Optimization Techniques......Page 133
5.7 High-Fidelity MDO Using Metaheuristic Algorithms......Page 134
5.8 Test Problem......Page 135
5.8.1 Conventional Optimization Problem Formulation......Page 137
5.8.2 CO Formulation......Page 138
5.8.3 Discipline-Level Optimization......Page 139
5.8.4 Implementation of Multi-Fidelity Modeling Methodology in CO......Page 140
5.8.5 System-Level Optimization Using MLSM......Page 141
5.8.6 Evaluation of Predictive Capabilities of the Metamodels......Page 142
5.8.7 Optimization Algorithms......Page 143
References......Page 145
6.1 Introduction......Page 150
6.2 Statement of the Problem......Page 152
6.3.1 Slabs......Page 153
6.3.2 Beams......Page 156
6.3.3 Columns......Page 158
6.4 The Optimization Problem......Page 159
6.5 ACO Algorithm for Column Layout Optimization......Page 161
6.5.1 Numerical Example......Page 164
References......Page 166
7.1.1 Heuristic Versus Algorithmic Design Tasks......Page 168
7.1.3 Beam–Slab Layout Design as an Optimization Problem......Page 171
7.2 A Representation of Beam–Slab Layouts......Page 172
7.2.1 A Representation of Beam Locations......Page 173
7.2.2 Elimination of Invalid Beams......Page 176
7.3 A Representative Optimization Problem......Page 180
7.4.1 Problem Formulation for a GA......Page 183
7.4.2 Adaptive Penalty and Elitism......Page 184
7.4.3 Algorithm......Page 185
7.5 Examples......Page 186
7.6 Future Challenges......Page 189
References......Page 191
8.1 Introduction......Page 194
8.2 Statement of the Optimization Design Problem......Page 195
8.2.1 Constraint Conditions for Truss Structures......Page 196
8.2.2 Constraint Conditions for Steel Frames......Page 197
8.3.1 BB–BC Algorithm......Page 198
8.3.2 Particle Swarm Optimization......Page 199
8.3.3 Sub-Optimization Mechanism......Page 201
8.4.1 A Continuous Algorithm......Page 202
8.4.2 A Discrete Algorithm......Page 204
8.5 Design Examples......Page 206
8.5.1 A Square on Diagonal Double-Layer Grid......Page 207
8.5.2 A 26-Story-Tower Spatial Truss......Page 209
8.5.3 A 354-Bar Braced Dome Truss......Page 211
8.5.4 A 582-Bar Tower Truss......Page 214
8.5.5 A 3-Bay 15-Story Frame......Page 216
8.5.6 A 3-Bay 24-Story Frame......Page 218
8.6 Concluding Remarks......Page 220
References......Page 223
9.1 Introduction......Page 228
9.2 Statement of the Weight Minimization Problem for a Truss Structure......Page 230
9.3 Harmony Search......Page 231
9.3.1 Generation, Acceptance/Rejection, and Adjustment of a New Harmony......Page 233
9.3.2 Evaluation of the New Trial Design......Page 236
9.3.3 One-Dimensional SA-Type Probabilistic Search......Page 237
9.4 Big Bang–Big Crunch......Page 238
9.4.2 Evaluation of the Characteristics of the Center of Mass......Page 240
9.4.3 Perturbation of Design Variables......Page 241
9.4.4 Evaluate the Quality of the New Trial Design, Eventually Use Improvement Routines, and Finally Perform a New Explosion......Page 242
9.5 Simulated Annealing......Page 243
9.6 Description of Test Problems......Page 245
9.6.1 Planar 200-Bar Truss Structure Subject to Five Independent Loading Conditions......Page 246
9.6.2 Spatial 3586-Bar Truss Tower......Page 247
9.7 Results of Sensitivity Analysis......Page 249
9.8 Results of the Large-Scale Optimization Problem......Page 253
9.9 Summary and Conclusions......Page 256
References......Page 258
10.1 Introduction......Page 262
10.2.1 Equilibrium Equations......Page 263
10.2.2 Formulation of the Optimization Problem......Page 265
10.2.3 Optimization Methods......Page 267
10.3.1 Basic Terminology......Page 269
10.3.2 Finite Element Representation......Page 270
10.3.3 Weighted Adjacency Matrix......Page 271
10.4.2 Representation......Page 272
10.4.4 Kinematic Stability......Page 273
10.4.5 Evaluation......Page 276
10.4.7 Crossover......Page 277
10.4.9 Replacement......Page 279
10.5.1 Free-Form Tower......Page 280
10.5.2 Bridge Structure......Page 282
10.5.3 Double-Layer Truss Grid......Page 284
References......Page 286
11.1 Introduction......Page 290
11.2 Overview of Topology Optimization Methods......Page 292
11.3 Element Exchange Method......Page 294
11.3.1 EEM Algorithm......Page 295
11.3.2 Element Exchange......Page 298
11.3.3 Checkerboard Control......Page 299
11.3.4 Random Shuffle......Page 300
11.3.6 Convergence Criteria......Page 301
11.4 EEM Application......Page 302
11.5 Influence of EEM Operations and Parameters on Optimization Results......Page 310
References......Page 313
Two: Structural Control and Identification......Page 316
12.1 Introduction......Page 318
12.2 Concept of Adaptive Sensitivity......Page 319
12.3 Structural Model with Passive Dampers......Page 320
12.4 Critical Excitation for Variable Design......Page 323
12.5.1 Performance-Based Optimal Design with Multiple Design Parameters......Page 324
12.6 Optimality Conditions......Page 325
12.7 Solution Procedure of Optimal Design Problem......Page 326
12.7.1 Solution Procedure for the Performance-Based Optimal Design with Multiple Design Parameters......Page 327
12.7.2 Solution Procedure for the Optimal Design Problem with Given Supporting Members......Page 329
12.8 Numerical Examples......Page 331
12.8.1 Adaptive Sensitivity of the Optimal Damper Placement with Multiple Design Parameters......Page 332
12.8.2 Optimal Damper Placement for Given Supporting Members......Page 336
12.9 Conclusions......Page 337
References......Page 338
13.1.1 The Role of Dynamic Analysis in Performance-Based Earthquake Engineering......Page 340
13.1.2 Selection of Ground Motion Records as an Important Challenge in PBEE......Page 344
13.2 A Snapshot of the Genetic Algorithm as One of the Popular Metaheuristics......Page 345
13.3 Code-Conforming Ground Motion Selection......Page 346
13.3.1 Code-Based Target Spectrum......Page 347
13.3.4 GA Solution......Page 348
13.4.1 Definition of the Subject......Page 353
13.4.2 PIDA Methodology......Page 354
13.4.3 Precedence List of Ground Motion Records......Page 355
13.4.4 Example......Page 357
13.4.4.1 The Test Structure and Selected Ground Motion Records......Page 358
13.4.4.2 IDA Analysis for a Simple Mathematical Model......Page 359
13.4.4.3 The Precedence List of Ground Motion Records......Page 360
13.4.4.4 IDA Analysis for the MDOF Model......Page 361
13.5 Conclusions......Page 362
References......Page 363
14.1 Introduction......Page 366
14.2 A Passive Structural Control Device: Tuned Mass Damper......Page 367
14.2.1 A Brief Review of Studies on Parameter Estimation of TMDs......Page 369
14.2.2 Equations of Motion for Structure with TMD......Page 372
14.3 Optimization of TMDs with HS......Page 377
14.4.1 Example 1......Page 379
14.4.2 Example 2......Page 382
14.5 Conclusion......Page 386
References......Page 390
15.1 Introduction......Page 394
15.2.1 Mechanical Model......Page 395
15.2.2 Problem Formulation......Page 396
15.3.1 Mutation Operator......Page 397
15.4.1 General Model......Page 398
15.4.2 Inertia Weight and Acceleration Factors......Page 400
15.4.3 Chaotic Particle Swarm Optimization......Page 401
15.5.1 Experimental Setup......Page 402
15.5.3 Results......Page 404
References......Page 407
16.1 Introduction......Page 410
16.2 Formulation of a Structural Optimization Problem with Frequency Constraints......Page 413
16.3 Formulation of Optimization Problem of an Arch Dam with Frequency Constraints......Page 415
16.4 Metaheuristics......Page 417
16.4.2 Particle Swarm Optimization......Page 418
16.4.3 HS Algorithm......Page 419
16.5.2 BP Model......Page 420
16.5.3 ANFIS Model......Page 421
16.6 Numerical Examples......Page 422
16.6.1 First Example: 10-Bar Truss......Page 423
16.6.3 Third Example: 37-Bar Truss......Page 425
16.6.5 Fifth Example: Arch Dam......Page 428
16.7 Conclusions......Page 434
References......Page 436
17.1 Introduction......Page 440
17.2 A Brief Review of Metaheuristic Algorithms......Page 441
17.3 Statement of Seismic Design of Frames......Page 442
17.4 Pushover Analysis for Performance-Based Design......Page 444
17.5.1 Genetic Algorithms......Page 447
17.5.2 Ant Colony Optimization......Page 449
17.5.4 PSACO Algorithm......Page 451
17.6 Design Examples......Page 452
17.6.1 Four-Bay Three-Story Steel Frame......Page 453
17.6.2 Five-Bay Nine-Story Steel Frame......Page 454
17.7 Concluding Remarks......Page 455
References......Page 456
18.1 Introduction......Page 460
18.2.1 Expression Programming......Page 461
18.3.1 Review of State of the Art......Page 463
18.3.2 Numerical Experiments......Page 464
18.3.4.1 A Case Study of Concrete Structures: Shear Strength of RC Columns......Page 466
18.3.4.1.1 Model Construction and Analysis......Page 467
18.3.4.2 A Case Study of Steel Structures: Hysteretic Energy Demand in Steel Moment Frames......Page 468
18.3.4.2.2 GEP- and MEP-Based Formulations of Hysteretic Energy......Page 470
18.4 Model Validity......Page 471
18.5 Conclusions......Page 472
References......Page 473
19.1 Introduction......Page 478
19.2 Recent Studies on Sub-SI......Page 479
19.3.1 A Simple GA......Page 480
19.3.2 A Multifeature GA......Page 481
19.4 Divide-and-Conquer-Based Structural Identification......Page 483
19.5 Numerical Study......Page 487
19.6 Applications to Local Damage Detection......Page 489
19.7 Experimental Verification......Page 491
19.8 Conclusions......Page 495
References......Page 496
Three: Construction Management and Maintenance......Page 498
20.1 Introduction......Page 500
20.2.1 Memetic Algorithms......Page 501
20.2.2 Particle Swarm Optimization......Page 502
20.2.3 Ant Colony Optimization......Page 503
20.2.4 MSFL Algorithm......Page 504
20.3.1 Project TCT Problem......Page 505
20.3.2 Bridge-Deck Repair-Strategy Problem......Page 511
20.4 Conclusions......Page 514
References......Page 515
21.1 Introduction......Page 518
21.2 Problem Description......Page 519
21.3 Ant Colony Optimization......Page 521
21.3.2 Rank-Based Ant System......Page 522
21.3.3 Ant Colony System......Page 523
21.3.4.2 Guidance to Feasible Solutions......Page 524
21.3.4.3 Reinterpretation of η......Page 525
21.4 Genetic Algorithms......Page 527
21.4.1 GAs for the Infrastructure Management Problem......Page 528
21.4.2 Repair Functions......Page 530
21.4.3 Additional Ways of Handling Infeasibility......Page 532
21.5 Test Case......Page 533
21.6 Test Results......Page 534
References......Page 537
22.2.1 Maintenance Optimization Formulation......Page 540
22.2.2 Deterministic Versus Stochastic Problem......Page 541
22.2.3 Single-Facility Versus Multi-Facility Problem......Page 542
22.2.4 Interdependency Issues for the Multi-Facility Problem (Network Level)......Page 544
22.3.1 Genetic Algorithms......Page 546
22.3.2 Ant Colony Optimization......Page 548
22.3.3 Shuffled Frog Leaping......Page 549
22.3.4 Hybridization of Metaheuristics......Page 551
22.4 Case Studies......Page 552
22.5 Summary......Page 555
References......Page 556
23.2 Deterioration Modeling......Page 560
23.2.1.1 Routine Maintenance (Deterministic Approach)......Page 561
23.2.1.2 Reactive Maintenance (Stochastic Approach)......Page 562
23.3.1 A Numerical Example......Page 563
23.3.2 Results and Discussion......Page 565
23.4 Experimental Procedure......Page 567
23.5 Evaluation of FRP Composite Materials in Bridge Applications......Page 568
23.5.1 State-of-the-Art and State-of-the-Practice......Page 569
23.5.2 Advantages and Challenges of AFRP Composite Materials......Page 570
23.6 Application of AFRP Bars in a Full-Scale Bridge Deck Slab......Page 571
23.6.1 Bridge Deck Specimen Layout and Test Setup......Page 572
23.7 Experimental Results......Page 573
Acknowledgments......Page 575
References......Page 576