Abstract
The safety of prestressed steel structures in service has been studied widely. However, traditional safety assessment methods for prestressed steel structures involve few sample points, do not provide accurate predictions, and consume substantial human and material resources. The digital twin technology can be used to monitor the structural behavior, state, and activity of a steel structure throughout its life cycle, which is equivalent to performing a safety assessment of the structure. The purpose of this study is to establish a digital twin multidimensional model of prestressed steel structures. Based on this model, the support vector machine and prediction model are trained using the relevant structural history data, and the safety risk level of the structure is then predicted based on the measured data. Finally, a proportional reduction model of the wheel‐spoke cable truss structure is used to verify the feasibility of the proposed method. The results show that digital twin technology can achieve real‐time monitoring of prestressed steel structures in use and can provide timely predictions of the safety level. This represents a new method for the safety risk assessment of prestressed steel structures.
Highlights
The longer a structure is in service, the more it is affected by various uncertain factors, such as component failure, temperature effects, loss of prestress, and bar construction error; this adversely affects the structural safety; the mechanical performance and reliability of prestressed structures are very important
The existing research methods can achieve safety assessment to a certain extent, analysis software such as ABAQUS and ANSYS are usually used for analysis and calculation, but it is difficult to determine the boundary conditions of the study, and in service, it is even more complicated, with fewer sample points and lack of prediction; it may not be ideal for safety assessment of the prestressed steel structures in service
Corresponding maintenance measures can be applied to the physical structure based on the assessment results of the virtual structure to ensure the safe service of the building, which is in line with the basic concept of analysis, evaluation, and application of the data during the safety assessment process of the prestressed steel structure during service
Summary
Zhansheng Liu , Wenyan Bai, Xiuli Du, Anshan Zhang, Zezhong Xing, and Antong Jiang e Key Laboratory of Urban Security and Disaster Engineering of Ministry of Education, Beijing University of Technology, Beijing 100124, China. E safety of prestressed steel structures in service has been studied widely. E purpose of this study is to establish a digital twin multidimensional model of prestressed steel structures. Traditional safety assessment methods for prestressed steel structures involve few sample points, do not provide accurate predictions, and consume substantial human and material resources. Based on this model, the support vector machine and prediction model are trained using the relevant structural history data, and the safety risk level of the structure is predicted based on the measured data. E results show that digital twin technology can achieve real-time monitoring of prestressed steel structures in use and can provide timely predictions of the safety level. The existing research methods can achieve safety assessment to a certain extent, analysis software such as ABAQUS and ANSYS are usually used for analysis and calculation, but it is difficult to determine the boundary conditions of the study, and in service, it is even more complicated, with fewer sample points and lack of prediction; it may not be ideal for safety assessment of the prestressed steel structures in service
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callSimilar Papers
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
