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首页> 外文期刊>Computer-Aided Civil and Infrastructure Engineering >Real-Time Frequency-Domain Decomposition for Structural Health Monitoring Using General-Purpose Graphic Processing Unit
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Real-Time Frequency-Domain Decomposition for Structural Health Monitoring Using General-Purpose Graphic Processing Unit

机译:使用通用图形处理单元进行结构健康监测的实时频域分解

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Frequency-domain decomposition (FDD) is used in civil engineering to identify the modal properties of structures by analyzing the data output of structural health monitoring (SHM) systems. However, because FDD is computationally expensive, it prevents CPUs from achieving real-time performance. A CPU takes seconds to perform FDD of 16 input signals but minutes to perform FDD of hundreds of input signals; and the deployed SHM systems are becoming larger and larger. Instead, a supercomputer can achieve real-time performance but it cannot be installed near a civil structure because it is bulky, expensive, and requires constant maintenance. In this study, FDD is performed using general-purpose graphic processor unit (GPGPU). A GPU is capable of massive parallel computing. The developed parallel FDD algorithm is up to hundreds of times faster than its serial version on CPU. For SHM of civil structures, where natural frequencies are less than 20 Hz parallel FDD on a single GPU achieves real-time performance. The use of GPGPU offers many advantages. The modal properties are tracked in real time. A GPU can be installed inside the base station at a structure site. A GPU is energy efficient and does not require the maintenance of a supercomputer.
机译:频域分解(FDD)用于土木工程中,通过分析结构健康监测(SHM)系统的数据输出来识别结构的模态特性。但是,由于FDD在计算上很昂贵,因此会阻止CPU获得实时性能。 CPU执行16个输入信号的FDD需要几秒钟,而执行数百个输入信号的FDD需要几分钟。并且已部署的SHM系统变得越来越大。相反,超级计算机可以实现实时性能,但由于体积大,价格昂贵且需要不断维护,因此无法安装在民用建筑附近。在这项研究中,FDD使用通用图形处理器单元(GPGPU)执行。 GPU能够进行大规模并行计算。所开发的并行FDD算法比CPU上的串行版本快数百倍。对于土木结构的SHM,自然频率小于20 Hz的单个GPU上的并行FDD可实现实时性能。 GPGPU的使用具有许多优势。模态属性是实时跟踪的。 GPU可以安装在基站的某个结构站点内。 GPU是高能效的,不需要维护超级计算机。

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