[雙語(yǔ)翻譯]--外文翻譯--關(guān)于長(zhǎng)電纜支持橋梁健康監(jiān)測(cè)系統(tǒng)的監(jiān)測(cè)數(shù)據(jù)的多尺度特征的統(tǒng)計(jì)分析（英文）

1、Available online at www.sciencedirect.comProcedia Engineering 01 (2009) 000–000 Procedia Engineering www.elsevier.com/locate/procediaMesomechanics 2009 Statistical analyses on multi-scale features of monitoring data fr

2、om health monitoring system in long cable supported bridges Z. X. Li*, B. J. Wu, Y. Wang School of Civil Engineering, Southeast University, Nanjing 210096, China Received 3 March 2009; revised 30 April 2009; accepted 8

3、May 2009 Abstract Strain-time histories and other data acquired from a structural health monitoring system (SHMS) installed on a bridge reflect the real-time structural response of the bridge under actual service and en

4、vironmental loading. It is necessary to understand the inherent features of the data if we want to have confidence in using them to assess the health state or detect potential damage in the structure. This paper aims a

5、t exploring the inherent features of strain-time histories data from SHMS in order to find out their behavior in multiple temporal scales and to obtain reliable, clean and normalized data at the dominant scale of stress

6、es inducing fatigue. Firstly, the strain history data from SHMS installed on Runyang Yangtze Bridges (RYB) were analyzed within three typical temporal scales to explore their different characteristics and their own cut

7、-off frequency which span different orders of magnitude. Then, based on the description of the multi-scale features of the monitored data, a further investigation of the dominant scale controlling fatigue failures was

8、carried out. The result shows that, the strain data corresponding to the typical temporal scales of 106, 103 and 100 sec are caused by temperature change, with cut-off frequency fc,1 in the 10-2Hz range, by train load,

9、 with fc,2 in the 10-1Hz range and by truck load, with fc,3 in the 100Hz range. Noise shows significant coupling effect when coarse scale strain data are used for the evaluation, which may lead to significant error ev

10、en it is in small level acceptable in engineering analyses. © 2009 Elsevier B.V. All rights reserved Keywords: Multi-scale features; monitoring data; fatigue assessment; strain history; fatigue accumulation; 1. In

11、troduction With the development of structural health monitoring technique in recent years, stress spectrum based on real- time strain history recorded by the health monitoring system installed on a bridge is a valid alte

12、rnative to the acquisition of stress spectrum formerly based on experience and statistics. Compared with stress spectrum in accordance with criterion or statistics, fatigue stress spectrum based on real-time strain his

13、tory could re-create realistic and accurate structural working conditions therefore making great strides in realizing the exact evaluation of fatigue life, and in ensuring accuracy and reliability of fatigue assessment

14、 [1,2]. How to make full use of online monitoring data recorded by health monitoring system to acquire fatigue stress characteristics and the health state of monitored bridge has become an important issue nowadays [3-5

15、]. * Corresponding author. Tel.: +86-25-83790268; fax: +86-25-83792247. E-mail address: zhxli@seu.edu.cn. Procedia Engineering 1 (2009) 123–127www.elsevier.com/locate/procediadoi:10.1016/j.proeng.2009.06.029Li Z.X. et

16、al. / Procedia Engineering 01 (2009) 000–000 Strain time curves in Fig. 3 reveal that strain has different characteristics over three typical time scales, each having their own cut-off frequency of different orders of m

17、agnitude. The characteristics of the aforementioned monitoring data lead to the use of wavelet and multi-resolution analysis methodology for separation and extraction. Wavelet coefficient thresholds were developed and

18、adopted during the strain information extraction process using the method of controlling probability (p-method) and controlling noise (n-method). The results show that wavelet methodology can separate different types o

19、f strain information effectively, and that the similarity and dissimilarity between different type of bridges and different type of strain gauges can be clearly detected after separation [7]. Strain (10-6) 100 120 0 0.

20、24 0.48 0.72 0.96 1.2 Time/103 sec 80 Strain (10-6) 0 50 100 0.0144 0.0288 0.0432 0.0576 0.0720 0.0864 Time/106 sec 0.0 150 Strain (10-6) 100 0 1 2 3 4 6 Time/100 sec 140 60 80 5 120 Fig. 3. The strain

21、history recorded by “YBH4-13” installed on RYSB, displayed in the temporal scale of (a) 106 sec, (b) 103 sec and (c) 100 sec respectively (a) (b) (c) Fig. 2. Strain history from 18:00 to 19:00 on Aug. 10-16 recorded b