Digital twin method for intelligent lifting of cable structures under multi-collaborative mode

• Published in: Thin-walled structures Vol. 205; p. 112380
• Main Authors: Shi, Guoliang, Liu, Zhansheng, Lu, Dechun, Zhang, Qingwen, Zhang, Yupeng, Wang, Zeqiang
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2024
• Subjects: Cable structure; Digital twin; Improved long short-term memory; Intelligent lifting; Multi-collaborative mode; Intelligent lifting; Improved long short-term memory; Digital twin; Cable structure; Multi-collaborative mode
• ISSN: 0263-8231
• DOI: 10.1016/j.tws.2024.112380

•A construction and operation methods of the digital twin logic model are formed, which defines the relationship between the transmission and iteration of construction information in the collaborative process.•The coupling relationship between construction step and structural elevation is defined.•An automatic time window selection and adaptive moment estimation method are built to improve long short-term memory neural network.•A digital twin collaborative construction platform is established and used in a cable truss structure test model. In order to ensure the high efficiency and quality consistency of cable structure in each construction stage, this study proposes a digital twin method for intelligent lifting of cable structure under multi-collaborative mode. Firstly, a construction and operation methods of the digital twin logic model are formed. In the logical model, a relationship between the transmission and iteration of construction information in the collaborative process is defined. In order to realize the adaptability of working conditions, the coupling relationship between construction step and structural elevation is established. A cable structure lifting analysis model driven by an improved long short-term memory neural network is proposed, which effectively solves the problem of poor convergence of the traditional finite element method. An automatic time window selection method is formed for the neural network. An adaptive moment estimation method is used for neural network parameter optimization, which improves the efficiency and accuracy of the analysis. Taking the lifting process of a cable truss structure test model as an example, a digital twin collaborative construction platform is established to verify the effectiveness and feasibility of the research method. The results show that the digital twin logic model realizes the process collaboration, scene collaboration and data collaboration, and significantly improves the stability of construction quality and the linkage of construction process. Through the improved long short-term memory neural network, the coupling relationship between the construction step and the structural elevation is established, which accurately guides the whole construction process.