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E-raamat: Metaheuristic Applications in Structures and Infrastructures

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Edited by (School of Science and Technology, Middlesex University, UK), Edited by (Dep), Edited by (Department of Civil Engineering, University of Tabriz, Tabriz, Iran.
School of Civil and Environment Engineering, University of New South Wales, Sydney, Australia.)
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  • Ilmumisaeg: 31-Jan-2013
  • Kirjastus: Elsevier Science Publishing Co Inc
  • Keel: eng
  • ISBN-13: 9780123983794
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Metaheuristic Applications in Structures and InfrastructuresSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 31-Jan-2013
  • Kirjastus: Elsevier Science Publishing Co Inc
  • Keel: eng
  • ISBN-13: 9780123983794
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Due to an ever-decreasing supply in raw materials and stringent constraints on conventional energy sources, demand for lightweight, efficient and low-cost structures has become crucially important in modern engineering design. This requires engineers to search for optimal and robust design options to address design problems that are commonly large in scale and highly nonlinear, making finding solutions challenging. In the past two decades, metaheuristic algorithms have shown promising power, efficiency and versatility in solving these difficult optimization problems.

This book examines the latest developments of metaheuristics and their applications in structural engineering, construction engineering and earthquake engineering, offering practical case studies as examples to demonstrate real-world applications. Topics cover a range of areas within engineering, including big bang-big crunch approach, genetic algorithms, genetic programming, harmony search, swarm intelligence and some other metaheuristic methods. Case studies include structural identification, vibration analysis and control, topology optimization, transport infrastructure design, design of reinforced concrete, performance-based design of structures and smart pavement management. With its wide range of everyday problems and solutions, Metaheursitic Applications in Structures and Infrastructures can serve as a supplementary text for design courses and computation in engineering as well as a reference for researchers and engineers in metaheuristics, optimization in civil engineering and computational intelligence.

  • Review of the latest development of metaheuristics in engineering.
  • Detailed algorithm descriptions with focus on practical implementation.
  • Uses practical case studies as examples and applications.

Muu info

Provides a comprehensive summary of metaheuristic methods with applications in structural engineering, construction engineering and earthquake engineering.
List of Contributors
xvii
1 Metaheuristic Algorithms in Modeling and Optimization
1(24)
Amir Hossein Gandomi
Xin-She Yang
Siamak Talatahari
Amir Hossein Alavi
1.1 Introduction
1(1)
1.2 Metaheuristic Algorithms
2(1)
1.2.1 Characteristics of Metaheuristics
2(1)
1.2.2 No Free Lunch Theorems
3(1)
1.3 Metaheuristic Algorithms in Modeling
3(7)
1.3.1 Artificial Neural Networks
4(1)
1.3.2 Genetic Programming
5(3)
1.3.3 Fuzzy Logic
8(1)
1.3.4 Support Vector Machines
9(1)
1.4 Metaheuristic Algorithms in Optimization
10(8)
1.4.1 Evolutionary Algorithms
11(2)
1.4.2 Swarm-Intelligence-Based Algorithms
13(5)
1.5 Challenges in Metaheuristics
18(7)
References
18(7)
2 A Review on Traditional and Modern Structural Optimization: Problems and Techniques
25(24)
Mohammed Ghasem Sahab
Vassili V. Toropov
Amir Hossein Gandomi
2.1 Optimization Problems
25(1)
2.2 Optimization Techniques
26(1)
2.3 Optimization History
26(4)
2.4 Structural Optimization
30(5)
2.4.1 General Concept
30(1)
2.4.2 Major Advances in Structural Optimization
30(2)
2.4.3 OC Methods
32(1)
2.4.4 Reliability-Based Optimization Approach
33(1)
2.4.5 Fuzzy Optimization
34(1)
2.5 Metaheuristic Optimization Techniques
35(14)
2.5.1 Genetic Algorithm
35(1)
2.5.2 Simulated Annealing
36(1)
2.5.3 Tabu Search
37(1)
2.5.4 Ant Colony Optimization
37(2)
2.5.5 Particle Swarm Optimization
39(1)
2.5.6 Harmony Search
39(1)
2.5.7 Big Bang-Big Crunch
39(1)
2.5.8 Firefly Algorithm
40(1)
2.5.9 Cuckoo Search
40(1)
2.5.10 Other Metaheuristics
40(1)
References
41(8)
3 Particle Swarm Optimization in Civil Infrastructure Systems: State-of-the-Art Review
49(28)
Kasthurirangan Gopalakrishnan
3.1 Introduction
49(1)
3.2 Particle Swarm Optimization
50(2)
3.3 Structural Engineering
52(2)
3.3.1 Shape and Size Optimization Problems in Structural Design
52(1)
3.3.2 Structural Condition Assessment and Health Monitoring
53(1)
3.3.3 Structural Material Characterization and Modeling
54(1)
3.3.4 Other PSO Applications in Structural Engineering
54(1)
3.4 Transportation and Traffic Engineering
54(3)
3.4.1 Transportation Network Design
54(1)
3.4.2 Traffic Flow Forecasting
55(1)
3.4.3 Traffic Control
55(1)
3.4.4 Traffic Accident Forecasting
56(1)
3.4.5 Vehicle Routing Problem
56(1)
3.4.6 Other PSO Application in Transportation and Traffic Engineering
56(1)
3.5 Hydraulics and Hydrology
57(3)
3.5.1 River Stage Prediction
57(1)
3.5.2 Design Optimization of Water/Wastewater Distribution Networks
57(1)
3.5.3 Reservoir Operation Problems
58(1)
3.5.4 Parameter Estimation/Calibration of Hydrological Models
59(1)
3.5.5 Other PSO Applications in Hydraulics and Hydrology
59(1)
3.6 Construction Engineering
60(2)
3.6.1 Construction Planning and Management
60(1)
3.6.2 Construction Litigation
61(1)
3.6.3 Construction Cost Estimation and Prediction
61(1)
3.6.4 Other PSO Applications in Construction Engineering
61(1)
3.7 Geotechnical Engineering
62(1)
3.7.1 Inverse Parameter Identification and Geotechnical Model Calibration
62(1)
3.7.2 Slope Stability Analysis
62(1)
3.8 Pavement Engineering
63(1)
3.9 PSO Applications in Other Civil Engineering Fields
63(1)
3.10 Concluding Remarks
63(14)
References
64(13)
Part One Structural Design
77(218)
4 Evolution Strategies-Based Metaheuristics in Structural Design Optimization
79(24)
Chara Ch. Mitropoulou
Yiannis Fourkiotis
Nikos D. Lagaros
Matthew G. Karlaftis
4.1 Introduction
79(1)
4.2 Literature Survey
80(2)
4.3 The Structural Optimization Problem
82(3)
4.3.1 Sizing Optimization
83(1)
4.3.2 Shape Optimization
83(1)
4.3.3 Topology Optimization
84(1)
4.4 Problem Formulations
85(3)
4.4.1 Single-Objective Structural Optimization
86(1)
4.4.2 Multiobjective Structural Optimization
86(2)
4.5 Metaheuristics
88(6)
4.5.1 Solving the Single-Objective Optimization Problems
88(5)
4.5.2 Solving the Multiobjective Optimization Problems
93(1)
4.6 39-Bar Truss---Test Example
94(3)
4.7 Conclusions
97(6)
References
99(4)
5 Multidisciplinary Design and Optimization Methods
103(26)
Parviz Mohammad Zadeh
Mohadeseh Alsadat
Sadat Shirazi
5.1 Introduction
103(1)
5.2 Coupled Multidisciplinary System
104(1)
5.3 Classifications of MDO Formulations
105(1)
5.4 Single-Level Optimization
106(2)
5.4.1 Multiple-Discipline Feasible
106(1)
5.4.2 All-At-Once Method
107(1)
5.4.3 Individual-Discipline Feasible
107(1)
5.4.4 Comparative Characteristics of Single-Level Optimization
108(1)
5.5 Multilevel Optimization
108(2)
5.5.1 Concurrent Subspace Optimization
108(1)
5.5.2 Bilevel Integrated System Synthesis
109(1)
5.5.3 Collaborative Optimization
109(1)
5.6 Optimization Algorithms
110(3)
5.6.1 Direct Search Methods
111(1)
5.6.2 Gradient-Based Optimization Techniques
112(1)
5.6.3 Metaheuristic Optimization Techniques
112(1)
5.7 High-Fidelity MDO Using Metaheuristic Algorithms
113(1)
5.8 Test Problem
114(10)
5.8.1 Conventional Optimization Problem Formulation
116(1)
5.8.2 CO Formulation
117(1)
5.8.3 Discipline-Level Optimization
118(1)
5.8.4 Implementation of Multi-Fidelity Modeling Methodology in CO
119(1)
5.8.5 System-Level Optimization Using MLSM
120(1)
5.8.6 Evaluation of Predictive Capabilities of the Metamodels
121(1)
5.8.7 Optimization Algorithms
122(2)
5.9 Conclusions
124(5)
References
124(5)
6 Cost Optimization of Column Layout Design of Reinforced Concrete Buildings
129(18)
Pejman Sharafi
Muhammad N.S. Hadi
Lip H. Teh
6.1 Introduction
129(2)
6.2 Statement of the Problem
131(1)
6.3 Formulation in a New Space
132(6)
6.3.1 Slabs
132(3)
6.3.2 Beams
135(2)
6.3.3 Columns
137(1)
6.4 The Optimization Problem
138(2)
6.5 ACO Algorithm for Column Layout Optimization
140(5)
6.5.1 Numerical Example
143(2)
6.6 Conclusions
145(2)
References
145(2)
7 Layout Design of Beam---Slab Floors by a Genetic Algorithm
147(26)
Pruettha Nanakorn
Anan Nimtawat
7.1 Introduction
147(4)
7.1.1 Heuristic Versus Algorithmic Design Tasks
147(3)
7.1.2 Conversion of Heuristic to Algorithmic Tasks
150(1)
7.1.3 Beam---Slab Layout Design as an Optimization Problem
150(1)
7.2 A Representation of Beam---Slab Layouts
151(8)
7.2.1 A Representation of Beam Locations
152(3)
7.2.2 Elimination of Invalid Beams
155(4)
7.3 A Representative Optimization Problem
159(3)
7.4 A GA for Beam-Slab Layout Design
162(3)
7.4.1 Problem Formulation for a GA
162(1)
7.4.2 Adaptive Penalty and Elitism
163(1)
7.4.3 Algorithm
164(1)
7.5 Examples
165(3)
7.6 Future Challenges
168(5)
References
170(3)
8 Optimum Design of Skeletal Structures via Big Bang---Big Crunch Algorithm
173(34)
Siamak Talatahari
Ali Kaveh
8.1 Introduction
173(1)
8.2 Statement of the Optimization Design Problem
174(3)
8.2.1 Constraint Conditions for Truss Structures
175(1)
8.2.2 Constraint Conditions for Steel Frames
176(1)
8.2.3 Constraints Handling Approach
177(1)
8.3 Review of the Utilized Methods
177(4)
8.3.1 BB-BC Algorithm
177(1)
8.3.2 Particle Swarm Optimization
178(2)
8.3.3 Sub-Optimization Mechanism
180(1)
8.4 The Proposed Method
181(4)
8.4.1 A Continuous Algorithm
181(2)
8.4.2 A Discrete Algorithm
183(2)
8.5 Design Examples
185(14)
8.5.1 A Square on Diagonal Double-Layer Grid
186(2)
8.5.2 A 26-Story-Tower Spatial Truss
188(2)
8.5.3 A 354-Bar Braced Dome Truss
190(3)
8.5.4 A 582-Bar Tower Truss
193(2)
8.5.5 A 3-Bay 15-Story Frame
195(2)
8.5.6 A 3-Bay 24-Story Frame
197(2)
8.6 Concluding Remarks
199(8)
References
202(5)
9 Truss Weight Minimization Using Hybrid Harmony Search and Big Bang---Big Crunch Algorithms
207(34)
Luciano Lamberti
Carmine Pappalettere
9.1 Introduction
207(2)
9.2 Statement of the Weight Minimization Problem for a Truss Structure
209(1)
9.3 Harmony Search
210(7)
9.3.1 Generation, Acceptance/Rejection, and Adjustment of a New Harmony
212(3)
9.3.2 Evaluation of the New Trial Design
215(1)
9.3.3 One-Dimensional SA-Type Probabilistic Search
216(1)
9.3.4 Update of the Harmony Memory
217(1)
9.3.5 Termination Criterion
217(1)
9.4 Big Bang-Big Crunch
217(5)
9.4.1 Generation of the Initial Population and Determination of the Center of Mass
219(1)
9.4.2 Evaluation of the Characteristics of the Center of Mass
219(1)
9.4.3 Perturbation of Design Variables
220(1)
9.4.4 Evaluate the Quality of the New Trial Design, Eventually Use Improvement Routines, and Finally Perform a New Explosion
221(1)
9.5 Simulated Annealing
222(2)
9.6 Description of Test Problems
224(4)
9.6.1 Planar 200-Bar Truss Structure Subject to Five Independent Loading Conditions
225(1)
9.6.2 Spatial 3586-Bar Truss Tower
226(2)
9.6.3 Implementation Details
228(1)
9.7 Results of Sensitivity Analysis
228(4)
9.8 Results of the Large-Scale Optimization Problem
232(3)
9.9 Summary and Conclusions
235(6)
References
237(4)
10 Graph Theory in Evolutionary Truss Design Optimization
241(28)
Benoit Descamps
Rajan Filomeno Coelho
10.1 Introduction
241(1)
10.2 Truss Design
242(6)
10.2.1 Equilibrium Equations
242(2)
10.2.2 Formulation of the Optimization Problem
244(2)
10.2.3 Optimization Methods
246(2)
10.3 Graph Theory
248(3)
10.3.1 Basic Terminology
248(1)
10.3.2 Finite Element Representation
249(1)
10.3.3 Weighted Adjacency Matrix
250(1)
10.4 Evolutionary Algorithm
251(8)
10.4.1 Outline
251(1)
10.4.2 Representation
251(1)
10.4.3 Initial Population
252(1)
10.4.4 Kinematic Stability
252(3)
10.4.5 Evaluation
255(1)
10.4.6 Selection
256(1)
10.4.7 Crossover
256(2)
10.4.8 Mutation
258(1)
10.4.9 Replacement
258(1)
10.5 Application
259(6)
10.5.1 Free-Form Tower
259(2)
10.5.2 Bridge Structure
261(2)
10.5.3 Double-Layer Truss Grid
263(2)
10.6 Conclusions
265(4)
References
265(4)
11 Element Exchange Method for Stochastic Topology Optimization
269(26)
Mohammad Rouhi
Masoud Rais-Rohani
11.1 Introduction
269(2)
11.2 Overview of Topology Optimization Methods
271(2)
11.3 Element Exchange Method
273(8)
11.3.1 EEM Algorithm
274(3)
11.3.2 Element Exchange
277(1)
11.3.3 Checkerboard Control
278(1)
11.3.4 Random Shuffle
279(1)
11.3.5 Passive Elements
280(1)
11.3.6 Convergence Criteria
280(1)
11.4 EEM Application
281(8)
11.5 Influence of EEM Operations and Parameters on Optimization Results
289(3)
11.6 Conclusion
292(3)
References
292(3)
Part Two Structural Control and Identification
295(182)
12 Evolutionary Path-Dependent Damper Optimization for Variable Building Stiffness Distributions
297(22)
Izuru Takewaki
Kohei Fujita
12.1 Introduction
297(1)
12.2 Concept of Adaptive Sensitivity
298(1)
12.3 Structural Model with Passive Dampers
299(3)
12.4 Critical Excitation for Variable Design
302(1)
12.5 Optimal Design Problem
303(1)
12.5.1 Performance-Based Optimal Design with Multiple Design Parameters
303(1)
12.5.2 Performance-Based Optimal Damper Placement for Given Supporting Members
304(1)
12.6 Optimality Conditions
304(1)
12.7 Solution Procedure of Optimal Design Problem
305(5)
12.7.1 Solution Procedure for the Performance-Based Optimal Design with Multiple Design Parameters
306(2)
12.7.2 Solution Procedure for the Optimal Design Problem with Given Supporting Members
308(2)
12.8 Numerical Examples
310(6)
12.8.1 Adaptive Sensitivity of the Optimal Damper Placement with Multiple Design Parameters
311(4)
12.8.2 Optimal Damper Placement for Given Supporting Members
315(1)
12.9 Conclusions
316(3)
Acknowledgments
317(1)
References
317(2)
13 Application of Genetic Algorithms in Ground Motion Selection for Seismic Analysis
319(26)
Alireza Azarbakht
Mehdi Mousavi
13.1 An Introduction to Structural Nonlinear Response-History Analysis
319(5)
13.1.1 The Role of Dynamic Analysis in Performance-Based Earthquake Engineering
319(4)
13.1.2 Selection of Ground Motion Records as an Important Challenge in PBEE
323(1)
13.2 A Snapshot of the Genetic Algorithm as One of the Popular Metaheuristics
324(1)
13.3 Code-Conforming Ground Motion Selection
325(7)
13.3.1 Code-Based Target Spectrum
326(1)
13.3.2 Instructions for Ground Motion Selection
327(1)
13.3.3 Definition of the Problem
327(1)
13.3.4 GA Solution
327(5)
13.4 Ground Motion Record Selection in PBEE
332(9)
13.4.1 Definition of the Subject
332(1)
13.4.2 PIDA Methodology
333(1)
13.4.3 Precedence List of Ground Motion Records
334(2)
13.4.4 Example
336(5)
13.5 Conclusions
341(4)
References
342(3)
14 Optimization of Tuned Mass Damper with Harmony Search
345(28)
Gebrail Bekdas
Sinan Melih Nigdeli
14.1 Introduction
345(1)
14.2 A Passive Structural Control Device: Tuned Mass Damper
346(10)
14.2.1 A Brief Review of Studies on Parameter Estimation of TMDs
348(3)
14.2.2 Equations of Motion for Structure with TMD
351(5)
14.3 Optimization of TMDs with HS
356(2)
14.4 Numerical Examples
358(7)
14.4.1 Example 1
358(3)
14.4.2 Example 2
361(4)
14.5 Conclusion
365(8)
Acknowledgments
369(1)
References
369(4)
15 Identification of Passive Devices for Vibration Control by Evolutionary Algorithms
373(16)
Giuseppe Carlo Marano
Giuseppe Quaranta
Jennifer Avakian
Alessandro Palmeri
15.1 Introduction
373(1)
15.2 Parametric Identification of Fluid Viscous Dampers
374(2)
15.2.1 Mechanical Model
374(1)
15.2.2 Problem Formulation
375(1)
15.3 Differential Evolution Algorithms
376(1)
15.3.1 Mutation Operator
376(1)
15.3.2 Crossover Operator
377(1)
15.3.3 Selection Operator
377(1)
15.4 Particle Swarm Optimization Algorithms
377(4)
15.4.1 General Model
377(2)
15.4.2 Inertia Weight and Acceleration Factors
379(1)
15.4.3 Chaotic Particle Swarm Optimization
380(1)
15.4.4 Passive Congregation
381(1)
15.5 Viscous Damper Identification Using Experimental Data
381(5)
15.5.1 Experimental Setup
381(2)
15.5.2 Nonclassical Parametric Identification Methods
383(1)
15.5.3 Results
383(3)
15.6 Conclusions
386(3)
Acknowledgment
386(1)
References
386(3)
16 Structural Optimization for Frequency Constraints
389(30)
Saeed Gholizadeh
16.1 Introduction
389(3)
16.2 Formulation of a Structural Optimization Problem with Frequency Constraints
392(2)
16.3 Formulation of Optimization Problem of an Arch Dam with Frequency Constraints
394(2)
16.4 Metaheuristics
396(3)
16.4.1 Genetic Algorithm
397(1)
16.4.2 Particle Swarm Optimization
397(1)
16.4.3 HS Algorithm
398(1)
16.5 Neural Networks
399(2)
16.5.1 RBF Model
399(1)
16.5.2 BP Model
399(1)
16.5.3 ANFIS Model
400(1)
16.6 Numerical Examples
401(12)
16.6.1 First Example: 10-Bar Truss
402(2)
16.6.2 Second Example: 72-Bar Truss
404(1)
16.6.3 Third Example: 37-Bar Truss
404(3)
16.6.4 Fourth Example: 52-Bar Truss
407(1)
16.6.5 Fifth Example: Arch Dam
407(6)
16.7 Conclusions
413(6)
References
415(4)
17 Optimum Performance-Based Seismic Design of Frames Using Metaheuristic Optimization Algorithms
419(20)
Siamak Talatahari
17.1 Introduction
419(1)
17.2 A Brief Review of Metaheuristic Algorithm
420(1)
17.3 Statement of Seismic Design of Frames
421(2)
17.4 Pushover Analysis for Performance-Based Design
423(3)
17.5 Utilized Metaheuristic Algorithms
426(5)
17.5.1 Genetic Algorithms
426(2)
17.5.2 Ant Colony Optimization
428(2)
17.5.3 Particle Swarm Optimization
430(1)
17.5.4 PSACO Algorithm
430(1)
17.6 Design Examples
431(3)
17.6.1 Four-Bay Three-Story Steel Frame
432(1)
17.6.2 Five-Bay Nine-Story Steel Frame
433(1)
17.7 Concluding Remarks
434(5)
References
435(4)
18 Expression Programming Techniques for Formulation of Structural Engineering Systems
439(18)
Amir Hossein Gandomi
Amir Hossein Alavi
18.1 Introduction
439(1)
18.2 Genetic Programming
440(2)
18.2.1 Expression Programming
440(2)
18.3 Application to Structural Engineering Problems
442(8)
18.3.1 Review of State of the Art
442(1)
18.3.2 Numerical Experiments
443(2)
18.3.3 Model Selection
445(1)
18.3.4 Prediction Problems
445(5)
18.4 Model Validity
450(1)
18.5 Conclusions
451(6)
References
452(5)
19 An Evolutionary Divide-and-Conquer Strategy for Structural Identification
457(20)
Chan Ghee Koh
Thanh N. Trinh
19.1 Introduction
457(1)
19.2 Recent Studies on Sub-SI
458(1)
19.3 Multifeature GA
459(3)
19.3.1 A Simple GA
459(1)
19.3.2 A Multifeature GA
460(2)
19.4 Divide-and-Conquer-Based Structural Identification
462(4)
19.5 Numerical Study
466(2)
19.6 Applications to Local Damage Detection
468(2)
19.7 Experimental Verification
470(4)
19.8 Conclusions
474(3)
References
475(2)
Part Three Construction Management and Maintenance
477
20 Swarm Intelligence for Large-Scale Optimization in Construction Management
479(18)
Emad E. Elbeltagi
20.1 Introduction
479(1)
20.2 SI-Based Optimization Algorithms
480(4)
20.2.1 Memetic Algorithms
480(1)
20.2.2 Particle Swarm Optimization
481(1)
20.2.3 Ant Colony Optimization
482(1)
20.2.4 MSFL Algorithm
483(1)
20.3 Experiments and Discussion
484(9)
20.3.1 Project TCT Problem
484(6)
20.3.2 Bridge-Deck Repair-Strategy Problem
490(3)
20.4 Conclusions
493(4)
References
494(3)
21 Network-Level Infrastructure Management Based on Metaheuristics
497(22)
Katharina C. Lukas
Andre Borrmann
21.1 Introduction
497(1)
21.2 Problem Description
498(2)
21.3 Ant Colony Optimization
500(6)
21.3.1 The ACO Algorithm in General
501(1)
21.3.2 Rank-Based Ant System
501(1)
21.3.3 Ant Colony System
502(1)
21.3.4 ACO for the Infrastructure Management Problem
503(3)
21.4 Genetic Algorithms
506(6)
21.4.1 GAs for the Infrastructure Management Problem
507(2)
21.4.2 Repair Functions
509(2)
21.4.3 Additional Ways of Handling Infeasibility
511(1)
21.5 Test Case
512(1)
21.6 Test Results
513(3)
21.7 Summary and Outlook
516(3)
Acknowledgments
516(1)
References
516(3)
22 Large-Scale Maintenance Optimization Problems for Civil Infrastructure Systems
519(20)
Sehyun Tak
Sunghoon Kim
Hwasoo Yeo
22.1 Introduction
519(1)
22.2 Large-Scale Maintenance Optimization Problem
519(6)
22.2.1 Maintenance Optimization Formulation
519(1)
22.2.2 Deterministic Versus Stochastic Problem
520(1)
22.2.3 Single-Facility Versus Multi-Facility Problem
521(2)
22.2.4 Interdependency Issues for the Multi-Facility Problem (Network Level)
523(2)
22.3 Metaheuristic Solution Approaches
525(6)
22.3.1 Genetic Algorithms
525(2)
22.3.2 Ant Colony Optimization
527(1)
22.3.3 Shuffled Frog Leaping
528(2)
22.3.4 Hybridization of Metaheuristics
530(1)
22.3.5 Summary
531(1)
22.4 Case Studies
531(3)
22.5 Summary
534(5)
References
535(4)
23 Metaheuristic Applications in Bridge Infrastructure Maintenance Scheduling Considering Stochastic Aspects of Deterioration
539
Manoj K. Jha
Monique Head
Shobeir Pirayeh Gar
23.1 Introduction
539(1)
23.2 Deterioration Modeling
539(3)
23.2.1 Model Formulation
540(2)
23.3 Solution Algorithm
542(4)
23.3.1 A Numerical Example
542(2)
23.3.2 Results and Discussion
544(2)
23.4 Experimental Procedure
546(1)
23.5 Evaluation of FRP Composite Materials in Bridge Applications
547(3)
23.5.1 State-of-the-Art and State-of-the-Practice
548(1)
23.5.2 Advantages and Challenges of AFRP Composite Materials
549(1)
23.6 Application of AFRP Bars in a Full-Scale Bridge Deck Slab
550(2)
23.6.1 Bridge Deck Specimen Layout and Test Setup
551(1)
23.6.2 Material Properties
552(1)
23.6.3 AASHTO LRFD Criteria
552(1)
23.7 Experimental Results
552(2)
23.8 Discussion
554
Acknowledgments
554(1)
References
555
Xin-She Yang obtained his DPhil in Applied Mathematics from the University of Oxford. He then worked at Cambridge University and National Physical Laboratory (UK) as a Senior Research Scientist. He is currently a Reader in Modelling and Simulation at Middlesex University London, Fellow of the Institute of Mathematics and its Application (IMA) and a Book Series Co-Editor of the Springer Tracts in Nature-Inspired Computing. He has published more than 25 books and more than 400 peer-reviewed research publications with over 82000 citations, and he has been on the prestigious list of highly cited researchers (Web of Sciences) for seven consecutive years (2016-2022). Dr. Siamak Talatahari received his Ph.D degree in Structural Engineering from University of Tabriz, Iran. After graduation, he joined the University of Tabriz where he is presently Professor of Structural Engineering. He is the author of more than 100 papers published in international journals, 30 papers presented at international conferences and 8 international book chapters. Dr. Talatahari has been recognized as Distinguished Scientist in the Ministry of Science and Technology and as Distinguished Professor at the University of Tabriz. He also teaches at the Yakin Dogu University, Nicosia, Cyprus. In addition, he is a co-author with our author Xin-She Yang of Swarm Intelligence and Bio-Inspired Computation: Structural Optimization Using Krill Herd Algorithm; Metaheuristics in Water, Geotechnical and Transport Engineering, and Metaheuristic Applications in Structures and Infrastructures, all published by as Insights by Elsevier. Amir Hossein Alavi is an Assistant Professor in the Department of Civil and Environmental Engineering, and holds courtesy appointments in the Department of Bioengineering and Department of Mechanical Engineering and Materials Science, at the University of Pittsburgh, United States. His multidisciplinary scientific studies are organized around three research thrusts: 1) mechanics and electronics of multifunctional materials and structures, 2) embedded self-powered sensing systems, and 3) data-driven characterization, design and discovery of engineering systems.
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