Intelligent Prediction Method for Operation and Maintenance Safety of Prestressed Steel Structure Based on Digital Twin Technology
The operation and maintenance stage of the long-span prestressed steel structure is the core link of the whole life cycle. At present, there are few studies on the change law of safety risk in the whole process of operation and maintenance, especially the research on the analysis and prediction of t...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/6640198 |
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| Summary: | The operation and maintenance stage of the long-span prestressed steel structure is the core link of the whole life cycle. At present, there are few studies on the change law of safety risk in the whole process of operation and maintenance, especially the research on the analysis and prediction of the change law of safety risk in the whole process of structural operation and maintenance by effectively using the abundant monitoring data and relevant safety risk information in the operation and maintenance stage, which also affects the prestressed steel, which also affects the efficiency of judgment and control decision-making of operation and maintenance safety state of prestressed steel structure. Taking the spoke-type cable truss as an example, this paper proposes a new concept of integrating the digital twin model (DTM) with steel structure operation and maintenance safety. Through the combination of real physical space dimensions and digital virtual space dimensions, it is based on a hypothetical analysis model. In the above, a theoretical framework is proposed, and a case analysis of a prestressed steel structure is carried out from big data, and the feasibility of applying this method in the prestress loss and uneven rain and snow load conditions is evaluated. This method can provide guidance for operation and maintenance management and formulate strategies in time. |
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| ISSN: | 1687-8086 1687-8094 |