Please use this identifier to cite or link to this item:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/40820
Title: | 運用粒子群聚演最佳化法之橋塔工程 Using Particle Swarm Optimization for Pylon Construction |
Authors: | Nadsanti Lertpiriyakamol 那桑提 |
Advisor: | 張陸滿(Luh-Maan Chang) |
Keyword: | 爬模法,粒子群聚演算法,最佳化,模擬,橋塔工程, Climbing formwork,Particle Swarm Optimization,Simulation,Pylon, |
Publication Year : | 2011 |
Degree: | 碩士 |
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. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/40820 |
Fulltext Rights: | 有償授權 |
Appears in Collections: | 土木工程學系 |
Files in This Item:
File | Size | Format | |
---|---|---|---|
ntu-100-1.pdf Restricted Access | 2.31 MB | Adobe PDF |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.