Application of evolutionary deep learning algorithm in construction engineering management system
The application of construction project management system is crucial for improving work efficiency and operational convenience. However, in order to further improve the application level and efficiency of the system, this study aims to optimize deep learning algorithms and apply them to the construc...
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| Vydané v: | Systems and soft computing Ročník 7; s. 200317 |
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| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier B.V
01.12.2025
Elsevier |
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| ISSN: | 2772-9419, 2772-9419 |
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| Abstract | The application of construction project management system is crucial for improving work efficiency and operational convenience. However, in order to further improve the application level and efficiency of the system, this study aims to optimize deep learning algorithms and apply them to the construction project management system. The goal of this work is to develop a construction project management platform based on optimized evolutionary deep learning algorithms. Evolutionary computation strategy has been introduced in learning network models. Compared with other classic deep learning models, the optimized evolutionary deep learning algorithm model has significantly higher classification training accuracy and testing accuracy. The optimized evolutionary deep learning algorithm model can be applied to the field of construction project management. Based on this model, the development platform can be regarded as the "neurons" in the neural network structure, and through the collaboration of various components, a personalized management information system that meets user needs can be formed. Designed the structure, logical structure, and functional modules of the management system. The design process can provide experience for the design of similar management systems. This study optimized deep learning algorithms and introduced evolutionary computation strategies. By comparing with other classic deep learning models, we found that the optimized evolutionary deep learning algorithm model significantly improved the accuracy of classification training and testing. Through the collaboration of various components, a personalized management information system that meets user needs can be formed. We have designed the structure, logical structure, and functional modules of the management system, and verified its effectiveness through actual testing. |
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| AbstractList | The application of construction project management system is crucial for improving work efficiency and operational convenience. However, in order to further improve the application level and efficiency of the system, this study aims to optimize deep learning algorithms and apply them to the construction project management system. The goal of this work is to develop a construction project management platform based on optimized evolutionary deep learning algorithms. Evolutionary computation strategy has been introduced in learning network models. Compared with other classic deep learning models, the optimized evolutionary deep learning algorithm model has significantly higher classification training accuracy and testing accuracy. The optimized evolutionary deep learning algorithm model can be applied to the field of construction project management. Based on this model, the development platform can be regarded as the "neurons" in the neural network structure, and through the collaboration of various components, a personalized management information system that meets user needs can be formed. Designed the structure, logical structure, and functional modules of the management system. The design process can provide experience for the design of similar management systems. This study optimized deep learning algorithms and introduced evolutionary computation strategies. By comparing with other classic deep learning models, we found that the optimized evolutionary deep learning algorithm model significantly improved the accuracy of classification training and testing. Through the collaboration of various components, a personalized management information system that meets user needs can be formed. We have designed the structure, logical structure, and functional modules of the management system, and verified its effectiveness through actual testing. |
| ArticleNumber | 200317 |
| Author | Yang, Zhe |
| Author_xml | – sequence: 1 givenname: Zhe surname: Yang fullname: Yang, Zhe email: Zhe123Yangyz@outlook.com organization: Five-year College Department, Zhoukou Vocational and Technical College, Zhoukou, Henan 466001, PR China |
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| Cites_doi | 10.1061/(ASCE)CO.1943-7862.0001906 10.1061/(ASCE)CO.1943-7862.0002045 10.1016/j.autcon.2020.103517 10.1061/(ASCE)CO.1943-7862.0001852 10.1109/JSTSP.2019.2908700 10.1016/j.ssci.2021.105402 10.1145/3603704 10.1007/s12205-018-1528-y 10.1061/(ASCE)CO.1943-7862.0001854 10.1016/j.ress.2020.106806 10.1007/s42107-023-00698-y 10.1007/s42524-019-0073-9 10.1016/j.compag.2018.02.016 10.1038/s41566-020-0685-y 10.1007/s13042-020-01096-5 10.1108/ECAM-08-2018-0350 10.1016/j.neucom.2021.03.091 10.3390/buildings12071063 10.1016/j.neunet.2020.07.025 10.1007/s11831-019-09344-w 10.1016/j.eswa.2024.123503 10.1007/s12525-021-00475-2 |
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| Keywords | Office Automation Management System Evolutionary Construction engineering Deep learning algorithm |
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