Data-Driven Decision-Making in the Design Optimization of Thin-Walled Steel Perforated Sections: A Case Study
The rack columns have so distinctive characteristics in their design, which have regular perforations to facilitate installation of the rack system that it is more difficult to be analyzed with traditional cold-formed steel structures design theory or standards. The emergence of industrial “big-data...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2018/6326049 |
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| author | Zhi-Jun Lyu Qi Lu YiMing Song Qian Xiang Guanghui Yang |
| author_facet | Zhi-Jun Lyu Qi Lu YiMing Song Qian Xiang Guanghui Yang |
| author_sort | Zhi-Jun Lyu |
| collection | DOAJ |
| description | The rack columns have so distinctive characteristics in their design, which have regular perforations to facilitate installation of the rack system that it is more difficult to be analyzed with traditional cold-formed steel structures design theory or standards. The emergence of industrial “big-data” has created better innovative thinking for those working in various fields including science, engineering, and business. The main contribution of this paper lies in that, with engineering data from finite element simulation and physical test, a novel data-driven model (DDM) using artificial neural network technology is proposed for optimization design of thin-walled steel specific perforated members. The data-driven model based on machine learning is able to provide a more effective help for decision-making of innovative design in steel members. The results of the case study indicate that compared with the traditional finite element simulation and physical test, the DDM for the solving the hard problem of complicated steel perforated column design seems to be very promising. |
| format | Article |
| id | doaj-art-5e9efa60366d4cfc8be4aba2559a7544 |
| institution | OA Journals |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-5e9efa60366d4cfc8be4aba2559a75442025-08-20T02:05:08ZengWileyAdvances in Civil Engineering1687-80861687-80942018-01-01201810.1155/2018/63260496326049Data-Driven Decision-Making in the Design Optimization of Thin-Walled Steel Perforated Sections: A Case StudyZhi-Jun Lyu0Qi Lu1YiMing Song2Qian Xiang3Guanghui Yang4College of Mechanical Engineering, Donghua University, Shanghai 201620, ChinaCollege of Mechanical Engineering, Donghua University, Shanghai 201620, ChinaCollege of Mechanical Engineering, Donghua University, Shanghai 201620, ChinaCollege of Mechanical Engineering, Donghua University, Shanghai 201620, ChinaShanghai Engineering Research Centre of Storage & Logistics Equipment, Shanghai 201611, ChinaThe rack columns have so distinctive characteristics in their design, which have regular perforations to facilitate installation of the rack system that it is more difficult to be analyzed with traditional cold-formed steel structures design theory or standards. The emergence of industrial “big-data” has created better innovative thinking for those working in various fields including science, engineering, and business. The main contribution of this paper lies in that, with engineering data from finite element simulation and physical test, a novel data-driven model (DDM) using artificial neural network technology is proposed for optimization design of thin-walled steel specific perforated members. The data-driven model based on machine learning is able to provide a more effective help for decision-making of innovative design in steel members. The results of the case study indicate that compared with the traditional finite element simulation and physical test, the DDM for the solving the hard problem of complicated steel perforated column design seems to be very promising.http://dx.doi.org/10.1155/2018/6326049 |
| spellingShingle | Zhi-Jun Lyu Qi Lu YiMing Song Qian Xiang Guanghui Yang Data-Driven Decision-Making in the Design Optimization of Thin-Walled Steel Perforated Sections: A Case Study Advances in Civil Engineering |
| title | Data-Driven Decision-Making in the Design Optimization of Thin-Walled Steel Perforated Sections: A Case Study |
| title_full | Data-Driven Decision-Making in the Design Optimization of Thin-Walled Steel Perforated Sections: A Case Study |
| title_fullStr | Data-Driven Decision-Making in the Design Optimization of Thin-Walled Steel Perforated Sections: A Case Study |
| title_full_unstemmed | Data-Driven Decision-Making in the Design Optimization of Thin-Walled Steel Perforated Sections: A Case Study |
| title_short | Data-Driven Decision-Making in the Design Optimization of Thin-Walled Steel Perforated Sections: A Case Study |
| title_sort | data driven decision making in the design optimization of thin walled steel perforated sections a case study |
| url | http://dx.doi.org/10.1155/2018/6326049 |
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