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Field Monitoring and Analysis of the Vibration of Stay Cables Under Typhoon Conditions

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Field Monitoring and Analysis of the Vibration of Stay Cables Under Typhoon Conditions sensors Article Field Monitoring and Analysis of the Vibration of Stay Cables under Typhoon Conditions Jian Guo * and Xujiang Zhu Institute of bridge Engineering, Zhejiang University of Technology, Hangzhou 310023, China; [email protected] * Correspondence: [email protected]; Tel.: +86-137-3801-7518 Received: 15 July 2020; Accepted: 9 August 2020; Published: 12 August 2020 Abstract: Structural health monitoring systems provide many advantages for full-scale measurements in bridge monitoring. In this study, a strong landing typhoon event recorded at the Jintang Bridge (Zhejiang Province, China) in 2019 was selected to study the nonstationary wind and cable vibration characteristics. To study the characteristics of the recorded typhoon, the time-varying mean wind was extracted based on the adaptive method of the wavelet-matrix transform. The nonstationary characteristics of Typhoon Lekima, including the turbulence intensity, gust factor, and fluctuating wind power spectral density, were analyzed and compared with the stationary model characteristics of a typhoon, and the typical characteristics and parameters were obtained. In addition, the measured vibration response of the cables was analyzed. The vibration characteristics of the cables and the energy distribution of the wind speed wavelet packet were investigated. The vibrations at different positions were compared. A power spectrum analysis and a wavelet packet energy analysis of the cable were performed. The results of this study can be used as a basis for wind-resistant design and performance evaluation of bridges under similar operational conditions. Keywords: sea-crossing bridge; field measurement; stay cable; wavelet-matrix transform; wavelet packet; wind characteristics; nonstationary 1. Introduction With the demand for economic development, China has built many long-span bridges, seven of which are among the ten longest main-span, cable-stayed bridges in the world [1]. However, with the increase in bridge span, the structure has become flexible, the basic natural vibration period has reached >10 s, and the damping has decreased, making such bridges more sensitive to wind-induced vibration [2]. Especially for a cable-stayed bridge, the stay cables are vulnerable components, and the buffeting effects of the stay cables are more serious. Consequently, the wind load has become the most important control factor affecting structural safety [3]. Therefore, field monitoring of the wind environment [4–6] and structure dynamic response [7] has become an important topic. Recording wind environment measurements and performing a buffeting statistical analysis of the measured data constitute the most effective way to understand the wind characteristics of a region. Previous studies of the wind characteristics of typhoons have focused on a stationary wind model hypothesis. Early investigations were conducted by Davenport [8], Kaimal et al. [9], Panofsky and Singer [10], Harris [11], and Shiotani and Iwatani [12]. These researchers have done considerable work in the field of wind characteristics measurement and have developed their own wind field models, and their results have been widely used in wind engineering specifications in various countries [13–15]. Because typhoons have obvious nonstationary characteristics that differ from those of stationary wind models, researchers have turned their attention to the study of nonstationary random wind fields under extreme weather conditions [16–19]. Based on the above premise, various nonstationary characteristics methods have been devised and their feasibility has been demonstrated. Proposed methodologies Sensors 2020, 20, 4520; doi:10.3390/s20164520 www.mdpi.com/journal/sensors Sensors 2020, 20, 4520 2 of 20 based on empirical mode decomposition and the discrete wavelet transform have also been closely followed and further investigated in many more recent studies, including those by Xu and Chen [20], Tao et al. [21], and Huang and Chen [22], which mostly address the nonstationary characteristics of typhoons in field measurements. However, for natural wind with strong randomness, it is very difficult to formulate universal standards and use them in the wind-resistant design of various engineering structures. The most common problem is that the design is too conservative to ensure the safety of the structures or that the lack of research on wind characteristics leads to insufficient wind-resistant design and structural safety problems. Therefore, it is necessary to conduct measured research in targeted cases. In this paper, we propose that the wavelet-matrix transform (WMT) be directly applied to the nonstationary characteristics of typhoons [23,24]. Because any complex function can be described by a dense equidistant sampling data series, the discrete WMT is still general. Compared with the nonstationary characteristics analysis method mentioned above, WMT entails analyzing the essence of the wavelet transform from another aspect. It can be used to directly understand the decomposition and reconstruction of the orthogonal wavelets from the time domain, and the calculation of wavelet orthogonal decomposition through a matrix analysis is faster and easier to understand. In this study, Typhoon Lekima, which landed on the Jintang Bridge in 2019, was taken as the background object. Based on the measured data of Typhoon Lekima collected by the structural health monitoring system (SHMS), a nonstationary wind speed model suitable for the nonstationary wind speed data and the corresponding time-varying mean wind speed and fluctuating wind speed extraction method were established. Comparison with the wind resistance specifications of Chinese bridges provides a basis for the wind resistance safety assessment of the bridge based on the SHMS and provides a reference for other large structures in similar marine environments, especially for other sea-crossing bridges in Zhejiang Province. 2. Case Study The Jintang Bridge in China is a long cable-stayed bridge carrying a dual four-lane highway on the upper level of the bridge deck. The overall length and the main span of the bridge are 21.029 km and 620 m, respectively. The height of its two towers is 204 m, as measured from the base level to the tower saddle. The width of the bridge deck is 30.1 m. The bridge was opened to traffic in 2009. It is also located in a typhoon-prone area in the northwest of the Pacific Ocean. Typhoon Lekima formed as a tropical depression at the east of Luzon, Philippines, on 4 August 2019. It directly landed at Wenling City, Zhejiang Province, at 01:45 a.m. (UTC+8) on 10 August 2019, with a maximum wind force near the center of 52 m/s, and it arrived at Shanghai at 00:00 a.m. on 11 August 2019. Finally, it crossed Jiangsu Province on 10 August 2019 and moved to Shandong and its adjacent waters, from where Lekima transformed into an extratropical cyclone. From 03:00 p.m. to 05:00 p.m. on 10 August 2019, it passed the site of the Jintang Bridge. Figure1 shows the route of Typhoon Lekima and the location of the Jintang Bridge. At present, the Jintang Bridge is equipped with an overall SHMS, which is used to evaluate the health status of the bridge by monitoring the wind environment, temperature distribution, traffic distribution, bridge vibration response, and other bridge working environments. However, the existing cable vibration monitoring sensor of the Jintang Bridge is a unidirectional acceleration sensor, which can only monitor the vibration in the plane of the cables, while the actual vibration is an in-plane and out-of-plane common vibration. Therefore, to fully understand the wind-induced vibration response of the stay cables, a two-way acceleration sensor and a displacement sensor were added to the existing SHMS (see Figure2). Four sensors were installed on the longest stay cable near the Ningbo side. One sensor can monitor the in-plane and out-of-plane vibration of the stay cable. Figure3 shows the field installation of the sensor, and Table1 lists the technical standard of the wireless acceleration node TZT3805. The sampling frequency of the anemometer was 32 Hz. In this study, the wind speed data collected by the anemometer on the Jintang Bridge and the data collected by the sensor on the stay cable Sensors 2020, 20, 4520 3 of 20 Sensors 2020, 20, x FOR PEER REVIEW 3 of 20 Sensors 2020, 20, x FOR PEER REVIEW 3 of 20 wereby the taken sensor as theon the research stay cable objects. were The taken wind as speed the research samples duringobjects. theThe landing wind speed period samples of the typhoon during were selected for analyzing the wind characteristics and the vibration characteristics of the stay cable. theby thelanding sensor period on the of stay the cabletyphoon were were taken selected as the research for analy objects.zing the The wind wind characteristics speed samples and during the Data from Typhoon Lekima were selected from 00:00 a.m. to 06:00 a.m. on 10 August. As can be seen vibrationthe landing characteristics period of theof the typhoon stay cable. were Data selected from forTyphoon analy zLekimaing the werewind selected characteristics from 00:00 and a.m. the from Figure4, the wind speed time history of the strong wind samples has clear time-varying and tovibration 06:00 a.m. characteristics on 10 August. of theAs canstay be cable. seen Data from from Figure Typhoon 4, the wind Lekima speed were time selected history from of the 00:00 strong a.m. local mutation characteristics. windto 06:00 samples a.m. on has 10 clear August. time -Asvarying can be and seen local from mutation Figure 4characteristics., the wind speed time history of the strong wind samples has clear time-varying and local mutation characteristics. Figure 1. Moving track of Typhoon Lekima. Figure 1. Moving track of Typhoon Lekima. Figure 2. Sensor layout on the Jintang Bridge (in units of meters). FigureFigure 2. 2.Sensor Sensor layoutlayout onon thethe JintangJintang BridgeBridge (in(in unitsunits ofof meters).meters). Sensors 2020, 20, x FOR PEER REVIEW 4 of 20 Sensors 2020, 20, 4520 4 of 20 Sensors 2020, 20, x FOR PEER REVIEW 4 of 20 Figure 3.
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