GNSS辅助加速度计频域积分法监测结构动态位移

Xu Liu, Jian Wang, Jie Zhen, Houzeng Han, C. Hancock
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引用次数: 1

摘要

加速度计频域积分法(FDIA)正被积极应用于大型工程结构的动力位移响应计算。然而,这是一个相对的加速度测量,因为初始位置是不可用的。GNSS提供了直接的位移测量,但与其他测量技术相比,其数据频率相对较低。因此,本文提出了一种改进的FDIA,利用GNSS的优势来获得关于初始位置的准确信息。首先通过软件仿真验证了该方法的有效性。验证后,在北京建筑大学南广场使用一台GNSS接收机和一台加速度计进行了一系列不同振动频率(0.5 HZ、1 HZ、1.5 HZ、2 HZ和2.5 HZ)的振动台试验。结果表明,该方法有效避免了初始值的不确定性,提高了结构动态位移的直接测量精度,均方根误差(RMSE)由11.4 mm降至6.8 mm。
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GNSS-aided accelerometer frequency domain integration approach to monitor structural dynamic displacements
ABSTRACT The accelerometer frequency domain integration approach (FDIA) is being actively applied to calculate dynamic displacement responses of large engineering structures. However, it is a relative acceleration measurement as the initial position is unavailable. GNSS offers direct displacement measurements, but has the limitation of relatively low frequency of data compared with alternative measurement techniques. Therefore, this paper proposes an improved FDIA utilising the advantages of GNSS to gain accurate information about the initial position. The performance of the proposed approach is first validated through software simulation. Following the validation, a series of shaking table tests using various vibration frequencies (0.5 HZ, 1 HZ, 1.5 HZ, 2 HZ and 2.5 HZ) are performed at the south square of Beijing University of Civil Engineering and Architecture (BUCEA) using one GNSS receiver and one accelerometer. The results show that the proposed approach can effectively avoid the uncertainty of the initial value and thus enhance the direct measurement accuracy of the dynamic displacements of structures, with root mean square error (RMSE) decreasing from 11.4 mm to 6.8 mm.
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来源期刊
CiteScore
5.00
自引率
0.00%
发文量
10
期刊介绍: International Journal of Image and Data Fusion provides a single source of information for all aspects of image and data fusion methodologies, developments, techniques and applications. Image and data fusion techniques are important for combining the many sources of satellite, airborne and ground based imaging systems, and integrating these with other related data sets for enhanced information extraction and decision making. Image and data fusion aims at the integration of multi-sensor, multi-temporal, multi-resolution and multi-platform image data, together with geospatial data, GIS, in-situ, and other statistical data sets for improved information extraction, as well as to increase the reliability of the information. This leads to more accurate information that provides for robust operational performance, i.e. increased confidence, reduced ambiguity and improved classification enabling evidence based management. The journal welcomes original research papers, review papers, shorter letters, technical articles, book reviews and conference reports in all areas of image and data fusion including, but not limited to, the following aspects and topics: • Automatic registration/geometric aspects of fusing images with different spatial, spectral, temporal resolutions; phase information; or acquired in different modes • Pixel, feature and decision level fusion algorithms and methodologies • Data Assimilation: fusing data with models • Multi-source classification and information extraction • Integration of satellite, airborne and terrestrial sensor systems • Fusing temporal data sets for change detection studies (e.g. for Land Cover/Land Use Change studies) • Image and data mining from multi-platform, multi-source, multi-scale, multi-temporal data sets (e.g. geometric information, topological information, statistical information, etc.).
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