臺灣博碩士論文加值系統

Steel bridge structures are the fundamental of transport infrastructure. In nature, steel bridge structures’ performance will decrease over time by many reasons. Bridge maintenance is essential requirement. The life extension of the bridges not only makes the large economic profit but also relieves the financial issue on asset management. Bridge maintenance is carried out to extend the life of the structure and to ensure that it functions as designed. One of challenges in bridge maintenance comes from uncertainty factors which make it difficult to get an accurate prediction of bridge behavior and performance of bridge structures. Therefore, the time-variant reliability model in bridge maintenance is needed. There are several techniques to penetrate the time-variant reliability such as iterative procedure; sampling techniques or Monte Carlo Simulation… This research presents another method called probability density evolution method (PDEM) to solve this issue. PDEM has been researched and developed recently, started with the idea of physical stochastic systems. In general, time-variant reliability model could be treated as a stochastic system. From application of PDEM in generic stochastic system, the probability density function of time-variant reliability in reliability model will be captured. A case study is considered to apply PDEM. Lastly, to fulfillment the problem Stochastic Optimization (PDEM combines with Particle Swarm Optimization algorithm) with uncertainties is used to optimize the maintenance cost by using the result from time-variant reliability as a constraint.

Steel bridge structures are the fundamental of transport infrastructure. In nature, steel bridge structures’ performance will decrease over time by many reasons. Bridge maintenance is essential requirement. The life extension of the bridges not only makes the large economic profit but also relieves the financial issue on asset management. Bridge maintenance is carried out to extend the life of the structure and to ensure that it functions as designed. One of challenges in bridge maintenance comes from uncertainty factors which make it difficult to get an accurate prediction of bridge behavior and performance of bridge structures. Therefore, the time-variant reliability model in bridge maintenance is needed. There are several techniques to penetrate the time-variant reliability such as iterative procedure; sampling techniques or Monte Carlo Simulation… This research presents another method called probability density evolution method (PDEM) to solve this issue. PDEM has been researched and developed recently, started with the idea of physical stochastic systems. In general, time-variant reliability model could be treated as a stochastic system. From application of PDEM in generic stochastic system, the probability density function of time-variant reliability in reliability model will be captured. A case study is considered to apply PDEM. Lastly, to fulfillment the problem Stochastic Optimization (PDEM combines with Particle Swarm Optimization algorithm) with uncertainties is used to optimize the maintenance cost by using the result from time-variant reliability as a constraint.

ACKNOWLEDGEMENTS i
ABSTRACT iii
TABLES OF CONTENTS iv
LIST OF FIGURES viii
LIST OF TABLES xi
CHAPTER 1 INTRODUCTION 1
1.1 Research background 1
1.2 Research scope, objectives and assumptions 2
1.2.1 Research scope 2
1.2.2 Research motivation and objective 4
1.2.3 Research assumptions 5
1.3 Research outline 5
CHAPTER 2 LITERATURE REVIEW 8
2.1 Steel Bridge Maintenance 8
2.1.1 Structural Reliability Model of Steel Bridge 9
2.1.2 Time-variant reliability model of bridge structure 10
2.2 Probability Density Evolution Method (PDEM) 13
2.3 Stochastic optimization under uncertainty 16
CHAPTER 3 THE STEEL BRIDGE FAILURE AND MAINTENANCE 20
3.1 Steel Bridge Failure 20
3.1.1 Limit state functions of steel bridge 20
3.1.2 Reliability Techniques 22
3.1.3 Reliability Model for Steel Girders of Bridge 25
3.1.4 Determine main uncertainty factors 27
3.1.4.1 Traffic Load 27
3.1.4.2 Corrosion 31
3.2 Steel bridge maintenance 37
3.3 Case Study of Steel Bridge 39
3.4 Calculation Process 41
3.4.1 Traffic Live Load Calculation Procedure 41
3.4.2 Plastic Modulus Z and Area of the web induced by corrosion process 44
3.4.3 Steel girders-limit state functions of case study 49
CHAPTER 4 PROBABILITY DENSITY EVOLUTION METHOD 51
4.1 Overview of PDEM 51
4.1.1 Probability density evolution theory 51
4.1.2 Numerical Solving Flow 55
4.2 Applying PDEM into reliability model of steel bridge 57
4.2.1 Construction of a stochastic process 57
4.2.2 Differential Equation 58
4.2.3 Numerical Method 59
4.2.4 Generate representative points set 61
4.2.5 PDEM Calculation Process for case study of steel bridge 63
4.3 Evaluation of PDEM 68
4.3.1 Validation of PDEM 68
4.3.2 Sensitivity Analysis of PDEM 72
CHAPTER 5 STOCHASTIC OPTIMIZATION: PDEM-PSO IN STEEL BRIDGE 75
5.1 Stochastic Optimization under uncertainty – Stochastic Programming 75
5.1.1 Stochastic programming 75
5.1.2 Calculation process of stochastic programming 76
5.1.3 Disadvantages of stochastic programming 76
5.2 PDEM-PSO Model in steel bridge maintenance 77
5.2.1 Problem formulation with uncertainties in steel bridge maintenance 77
5.2.2 General PSO Algorithm 77
5.2.3 PDEM-PSO 79
5.2.4 Case study for PDEM-PSO Stochastic Optimization 81
5.2.5 Result 83
5.3 Evaluation of PDEM-PSO 86
CHAPTER 6 CONCLUSION 87
6.1 Conclusion 87
6.2 Future Research Direction 88
REFERENCES 89
APPENDIX A: Maximum moments and shear live load in Exterior Girders 91
APPENDIX B: Plastic Modulus of Exterior Girder against time 92
APPENDIX C: Area of the web of Ex-interior Girder and Exterior 93
APPENDIX D: PDEM result for moment and shear of Interior girders 94
APPENDIX E: PDEM result for moment and shear of Ex-interior girders 95
APPENDIX F: PDEM result for moment and shear of Exterior girders 96

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