Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China.
The Key Laboratory of Urban Security and Disaster Engineering of the Ministry of Education, Beijing University of Technology, Beijing 100124, China.
Sensors (Basel). 2022 Feb 20;22(4):1647. doi: 10.3390/s22041647.
Civil infrastructure O&M requires intelligent monitoring techniques and control methods to ensure safety. Unfortunately, tedious modeling efforts and the rigorous computing requirements of large-scale civil infrastructure have hindered the development of structural research. This study proposes a method for impact response prediction of prestressed steel structures driven by digital twins (DTs) and machine learning (ML). The high-fidelity DTs of a prestressed steel structure were constructed from the perspective of both a physical entity and virtual entity. A prediction of the impact response of prestressed steel structure's key parts was established based on ML, and a structure response prediction of the parts driven by data was realized. To validate the effectiveness of the proposed prediction method, the authors carried out a case study in an experiment of a prestressed steel structure. This study provides a reference for fusion applications with DTs and ML in impact response prediction and analysis of prestressed steel structures.
民用基础设施的运维需要智能监测技术和控制方法来确保安全。然而，大型民用基础设施繁琐的建模工作和严格的计算要求阻碍了结构研究的发展。本研究提出了一种基于数字孪生（DT）和机器学习（ML）的预应力钢结构冲击响应预测方法。从物理实体和虚拟实体两个角度构建了预应力钢结构的高保真 DT。基于 ML 对预应力钢结构关键部位的冲击响应进行预测，实现了数据驱动的结构部位响应预测。为了验证所提出的预测方法的有效性，作者在预应力钢结构的实验中进行了案例研究。本研究为 DT 和 ML 在预应力钢结构冲击响应预测和分析中的融合应用提供了参考。
