基于数字孪生装配式钢节点混凝土框架结构建造技术研究
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河北工业大学

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TU741

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河北省自然科学基金


Research on construction technology of prefabricated concrete frame with steel connector based on digital twin
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Hebei University of Technology

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    摘要:

    新型装配式钢节点混凝土框架结构相较于传统现浇框架结构建造更快速,但新型框架钢连接节点处存在应力突变,因此有必要采用基于数字孪生的手段监测该实际工程结构的施工过程,获得钢节点对结构性能的影响。论文基于BIM、有限元和传感器等技术搭建了基于数字孪生的新型装配式钢节点混凝土框架结构的智能建造框架,从物理数据收集、虚拟模型建立、模型信息交互三个方面提出基于数字孪生的建筑结构智能建造实现方法。在新型装配式钢节点混凝土框架实际工程的智能建造阶段,采用传感器技术实现了工程中关键点数据的实时监测,并将其与BIM和有限元数据进行比较,进一步调整与修正物理模型中的框架结构受力情况,最终实现了装配式钢节点混凝土框架的数字孪生模型的建立和应用。研究表明该数字孪生模型能够有效对新型装配式钢节点混凝土框架结构进行实时监测,实现基于传感器网络和时空参数分析的危险点预测,有效减小资源消耗,为后续该结构的应用提供有效数据信息。

    Abstract:

    Compared with the traditional cast-in-place frame structure, the construction of the new prefabricated steel joints concrete frame structure is faster, but there is a sudden change of stress at the joints of the new frame steel joints, so it is necessary to adopt the digital twin method to monitor the construction process of the actual engineering structure to obtain the influence of steel joints on the structural performance. Based on building information modeling BIM, finite element, and sensor technologies, an intelligent building framework of the new type of prefabricated concrete frame structure with steel connector based on digital twin was established in this study. An intelligent construction method based on digital twin was proposed in terms of physical data collection, virtual model establishment and model information interaction. In the engineering intelligent construction stage, real-time monitoring of data of the new prefabricated concrete frame with steel connector was realized via sensor technology, and sensor data was compared with BIM and finite element data. The frame structure stress in the physical model would be further adjusted and modified. Finally, the digital twin model of the prefabricated concrete frame with steel connector was established and applied. The results show that the digital dual model can effectively monitor prefabricated concrete frame with steel connector in real-time, predict the hazard position based on sensor network and spatiotemporal parameter analysis, reduce the resource consumption, and provide sufficient data information for subsequent applications.

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  • 收稿日期:2021-12-31
  • 最后修改日期:2022-05-23
  • 录用日期:2022-06-22
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