Tower crane lifting
Abstract Tower crane lifting is a common procedure deployed in constructions. In order to optimized the use of a tower crane and come up with a cost effective way and optimized the use of a tower crane, a Master–Slave Parallel Genetic Algorithm and implement the algorithm on Graphics Processing Un...
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sg-ntu-dr.10356-722472023-03-04T18:47:28Z Tower crane lifting Loh, Weisong Cai Yiyu School of Mechanical and Aerospace Engineering DRNTU::Engineering::Mechanical engineering Abstract Tower crane lifting is a common procedure deployed in constructions. In order to optimized the use of a tower crane and come up with a cost effective way and optimized the use of a tower crane, a Master–Slave Parallel Genetic Algorithm and implement the algorithm on Graphics Processing Units using CUDA programming is developed to calculate a lifting path plan. Currently this algorithm is under testing phase and it is necessary to do a physical trial with a scaled down tower crane model to test the integration of both software algorithm calculation and match to the physical crane lifting. All this obstacle needs to be cleared before this algorithm can introduced to crane industry. Due to heavy and complex works of the construction sites, safety is utmost importance and a safe collision avoidance pathway system is necessary for safe operation of a tower crane lifting. In this project, a scaled down fully functional tower tower crane based on Terex SK 415-20 is designed and builded. Master–Slave Parallel Genetic Algorithm will be used for calculating the optimized lifting path which will be executed by the tower crane model. The tower crane moves accordingly in the designated 3 degree-of-freedom space according to the data and performs the lifting task. In the final part, the tower crane model is tested and reviewed. Suggestions are given for future study on crane lifting to improve on the movement precision and related studies. Bachelor of Engineering (Mechanical Engineering) 2017-05-30T09:00:28Z 2017-05-30T09:00:28Z 2017 Final Year Project (FYP) http://hdl.handle.net/10356/72247 en Nanyang Technological University 45 p. application/pdf |
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DRNTU::Engineering::Mechanical engineering Loh, Weisong Tower crane lifting |
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Abstract
Tower crane lifting is a common procedure deployed in constructions. In order to optimized the use of a tower crane and come up with a cost effective way and optimized the use of a tower crane, a Master–Slave Parallel Genetic Algorithm and implement the algorithm on Graphics Processing Units using CUDA programming is developed to calculate a lifting path plan. Currently this algorithm is under testing phase and it is necessary to do a physical trial with a scaled down tower crane model to test the integration of both software algorithm calculation and match to the physical crane lifting. All this obstacle needs to be cleared before this algorithm can introduced to crane industry. Due to heavy and complex works of the construction sites, safety is utmost importance and a safe collision avoidance pathway system is necessary for safe operation of a tower crane lifting.
In this project, a scaled down fully functional tower tower crane based on Terex SK 415-20 is designed and builded. Master–Slave Parallel Genetic Algorithm will be used for calculating the optimized lifting path which will be executed by the tower crane model. The tower crane moves accordingly in the designated 3 degree-of-freedom space according to the data and performs the lifting task. In the final part, the tower crane model is tested and reviewed. Suggestions are given for future study on crane lifting to improve on the movement precision and related studies. |
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Cai Yiyu |
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Cai Yiyu Loh, Weisong |
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Final Year Project |
author |
Loh, Weisong |
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Loh, Weisong |
title |
Tower crane lifting |
title_short |
Tower crane lifting |
title_full |
Tower crane lifting |
title_fullStr |
Tower crane lifting |
title_full_unstemmed |
Tower crane lifting |
title_sort |
tower crane lifting |
publishDate |
2017 |
url |
http://hdl.handle.net/10356/72247 |
_version_ |
1759854052569513984 |