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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 張陸滿(Luh-Maan Chang) | |
| dc.contributor.author | Nadsanti Lertpiriyakamol | en |
| dc.contributor.author | 那桑提 | zh_TW |
| dc.date.accessioned | 2021-06-14T17:01:53Z | - |
| dc.date.available | 2011-08-26 | |
| dc.date.copyright | 2011-08-26 | |
| dc.date.issued | 2011 | |
| dc.date.submitted | 2011-08-11 | |
| dc.identifier.citation | References
[1] Banks, J. (1998). Handbook of Simulation: Principles, Methodology, Advances, Applications, and Practice, Wiley-Interscience. [2] Duan, L., and Chen, W. F. (1999). Bridge Engineering Handbook, CRC Press; 1 edition (November 4, 1999). [3] Elbeltagi, E., Hegazy, T., and Grierson, D. (2005). 'Comparison among five evolutionary-based optimization algorithms.' Advanced Engineering Informatics, 19(1), 43-53. [4] Engelbrecht, A. P. (2007). Computational Intelligence, An Introduction, John Wiley & Sons Ltd. [5] Halpin, D. W., and Martinez, L.-H. (1999). 'Real world applications of construction process simulation.' Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 2, ACM, Phoenix, Arizona, United States, 956-962. [6] Hansen, P., and Mladenovic, N. (2001). 'Variable neighborhood search: Principles and applications.' European Journal of Operational Research, 130(3), 449-467. [7] Hegazy, T., and Kassab, M. (2003). 'Resource Optimization Using Combined Simulation and Genetic Algorithms.' Journal of Construction Engineering and Management, 129(6), 698-705. [8] Kandil, A., El-Rayes, K., and El-Anwar, O. (2010). 'Optimization Research: Enhancing the Robustness of Large-Scale Multiobjective Optimization in Construction.' Journal of Construction Engineering and Management, 136(1), 17-25. [9] Kennedy, J., and Eberhart, R. 'Particle Swarm Optimization.' Proc., IEEE Int'l. Conf. on Neural Networks (Perth, Australia), IEEE Service Center, Piscataway, NJ, IV:1942-1948. [10] Kennedy, J., and Eberhart, R. 'Particle swarm optimization.' Proc., Neural Networks, 1995. Proceedings., IEEE International Conference on, 1942-1948 vol.1944. [11] Lien, L.-C., and Cheng, M.-Y. (2011). 'Particle Bee Algorithm for Construction Site Layout Optimization.' National Taiwan University of Science and Technology. [12] Lutz, J. D., and Hijazi, A. (1993). 'Planning repetitive construction: Current practice.' Construction Management and Economics, 11(2), 99 - 110. [13] Magoulas, G. D., Eldabi, T., and Paul, R. J. 'Global search strategies for simulation optimisation.' Proc., Simulation Conference, 2002. Proceedings of the Winter, 1978-1985 vol.1972. [14] Marler, R. T., and Arora, J. S. (2005). 'Function-transformation methods for multi-objective optimization.' Engineering Optimization, 37(6), 551-570. [15] Marzouk, M., Said, H., and El-Said, M. (2009). 'Framework for Multiobjective Optimization of Launching Girder Bridges.' Journal of Construction Engineering and Management, 135(8), 791-800. [16] Mladenovic, N., and Hansen, P. (2003). 'Handbook of Metaheuristics ', F. W. Glover, and G. A. Kochenberger, eds., Kluwer Academic Publishers. [17] Mladenovic, N., and Hansen, P. (1997). 'Variable neighborhood search.' Computers & Operations Research, 24(11), 1097-1100. [18] Reynolds, C. (1995). 'Boids, Background and Update.' <http://www.red3d.com/cwr/boids/>. (1995). [19] Reynolds, C. W. (1987). 'Flocks, herds and schools: A distributed behavioral model.' SIGGRAPH Comput. Graph., 21(4), 25-34. [20] Shi, Y., and Eberhart, R. 'A modified particle swarm optimizer.' Proc., Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on, 69-73. [21] Shi, Y., and Eberhart, R. (1998). 'Parameter selection in particle swarm optimization.' Evolutionary Programming VII, V. Porto, N. Saravanan, D. Waagen, and A. Eiben, eds., Springer Berlin / Heidelberg, 591-600. [22] Trelea, I. C. (2003). 'The particle swarm optimization algorithm: convergence analysis and parameter selection.' Information Processing Letters, 85(6), 317-325. [23] Yang, X.-S. (2008). Nature-Inspired Metaheuristic Algorithms Luniver Press [24] Zayed, T., Sharifi, M. R., Baciu, S., and Amer, M. (2008). 'Slip-Form Application to Concrete Structures.' Journal of Construction Engineering and Management, 134(3), 157-168. [25] Zhang, H., Tam, C. M., Li, H., and Shi, J. J. (2006). 'Particle Swarm Optimization-Supported Simulation for Construction Operations.' Journal of Construction Engineering and Management, 132(12), 1267-1274. [26] Zhang, H., Tam, C. M., and Shi, J. J. (2002). 'Simulation-based methodology for project scheduling.' Construction Management and Economics, 20(8), 667 - 678. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/40820 | - |
| dc.description.abstract | Construction method using climbing form (sometimes referred to as jump-form, self-climbing or self-lifting) is economical and effective for tall structures such as core walls, lift shaft, stair shafts, silo or bridge piers (or pylons) due to its superior speed and productivity. For cable-stayed bridge construction, the bridge pylon is typically constructed ahead of bridge deck by using climbing form system which comprises the formwork and the working platforms for cleaning, steel fixing, concreting, followed-up or repair works, and self-lifting mechanical system.
Due to the study of simulation technique is very limited to the study of earthworks and a few of precast operations. This study aims to apply the simulation to solve the problem in pylon construction project using climbing formwork technique. Different combinations of construction methods and varieties in resource utilizations affect the cost and duration in pylon construction project. This objective of this study is to apply particle swarm optimization in combination with simulation technique to establish the framework for solving multiobjective cost/duration optimization in the pylon construction using climbing form technique. The framework consists of optimization module and simulation module. The main activities for pylon construction process are reviewed. The particle swarm optimization module accounts for optimizing seven optimization variables e.g. reinforcement fabrication method, steel frame fabrication method, stressing method, etc. The hybridized particle swarm optimization using variable neighborhood search algorithm is also included for comparison with original particle swarm optimization. The framework can quickly provide a set of near-optimum solutions of the resource utilization combinations. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-14T17:01:53Z (GMT). No. of bitstreams: 1 ntu-100-R96521715-1.pdf: 2360681 bytes, checksum: a2c2bf8e00d4e2dfe589b48364703bee (MD5) Previous issue date: 2011 | en |
| dc.description.tableofcontents | Table of Contents
口試委員會審定書 i Acknowledgement ii Abstract iii Table of Contents v List of Figures ix List of Tables xi Chapter 1 Introduction 1 1.1 Introduction 1 1.2 Problem Statements 2 1.3 Objectives 4 1.4 Scopes 4 1.5 Research Process Flow 4 Chapter 2 Literature Review 7 2.1 Introduction 7 2.2 Aspects of the Problem 8 2.3 Uncertainties in Construction 9 2.4 Optimization Algorithms 9 2.5 Cable-stayed Bridge 12 2.6 Climbing Formwork 15 Chapter 3 Particle Swarm Optimization 20 3.1 Particle Swarm Optimization (PSO) 20 3.2 Particle Swarm Optimization Algorithm 23 3.2.1 Pseudo code for PSO: 26 3.2.2 Computational Implementation of PSO: 26 3.3 Variable Neighborhood Search (VNS) 33 3.3.1 Rules of Variable Neighborhood Search 33 3.3.2 Step of Basic Variable Neighborhood Search 34 3.4 Multiobjective Optimization 34 Chapter 4 Methodology 36 4.1 Introduction 36 4.2 Data Collection 36 4.3 Model Conceptualization 37 4.3.1 Decision Variables 37 4.3.2 Optimization Objectives 39 4.3.3 Input Parameters 40 4.4 Model Translation 42 Chapter 5 Details of Project 44 5.1 Introduction 44 5.2 Details of Project 44 5.3 Pylon Construction 47 5.4 Process Identification 48 Chapter 6 Data Collection and Analysis 68 6.1 Introduction 68 6.2 Verification of Optimization Variables 68 6.2.1 Strip, Remove and Modify Internal and External Formwork 69 6.2.2 Steel Frame Fabrication 70 6.2.3 Reinforcement Fabrication Method 71 6.2.4 Stressing Method 73 6.2.5 Grouting Method 76 6.2.6 Install Internal and External Formwork 77 6.2.7 Concreting Method 78 6.3 Computer Implementation 84 6.4 Computer Outputs and Analysis 85 Chapter 7 Conclusions and Recommendations 94 7.1 Conclusions 94 7.2 Recommendations 96 References 98 Appendix I: Program Code 100 Appendix II: Program Input 113 Appendix III: Output 115 | |
| dc.language.iso | en | |
| dc.subject | 橋塔工程 | zh_TW |
| dc.subject | 爬模法 | zh_TW |
| dc.subject | 粒子群聚演算法 | zh_TW |
| dc.subject | 最佳化 | zh_TW |
| dc.subject | 模擬 | zh_TW |
| dc.subject | Climbing formwork | en |
| dc.subject | Simulation | en |
| dc.subject | Particle Swarm Optimization | en |
| dc.subject | Pylon | en |
| dc.title | 運用粒子群聚演最佳化法之橋塔工程 | zh_TW |
| dc.title | Using Particle Swarm Optimization for Pylon Construction | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 99-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 郭斯傑(Sy-Jye Guo),陳柏翰(Po-Han Chen) | |
| dc.subject.keyword | 爬模法,粒子群聚演算法,最佳化,模擬,橋塔工程, | zh_TW |
| dc.subject.keyword | Climbing formwork,Particle Swarm Optimization,Simulation,Pylon, | en |
| dc.relation.page | 120 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2011-08-12 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 土木工程學研究所 | zh_TW |
| 顯示於系所單位： | 土木工程學系 | |
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