Development of a Low-Cost Automated Tension Estimation System for Cable-Stayed Bridges

Author(s):  
Soojin Cho ◽  
Jerome Peter Lynch ◽  
Chung-Bang Yun

Cable tension force is one of the most important structural parameters to monitor in cable-stayed bridges. For example, cable tension needs to be monitored during construction and maintenance to ensure the bridge is not overloaded. To economically monitor tension forces, this study proposes the use of an automated wireless tension force estimation system (WFTES) developed solely for cable force estimation. The design of the WFTES system can be divided into two parts: low-cost hardware and automated software. The low-cost hardware consists of an integrated platform containing a wireless sensing unit constructed from commercial off-the-shelf components, a low-cost commercial MEMS accelerometer, and a signal conditioning board for signal amplification and filtering. With respect to the automated software, a vibration-based algorithm using estimated modal parameters and information on the cable sag and bending stiffness is embedded into the wireless sensing unit. Since modal parameters are inputs to the algorithm, additional algorithms are necessary to extract modal features from measured cable accelerations. To validate the proposed WFTES, a scaled-down cable model was constructed in the laboratory using steel rope wire. The wire was exposed to broad-band excitations while the WFTES recorded the cable response and embedded algorithms interrogated the measured acceleration to estimate tension force. The results reveal the embedded algorithms properly identify the lower natural frequencies of the cable and make accurate estimates of cable tension. This paper concludes with a summary of the salient research findings and suggestions for future work.

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7229
Author(s):  
Min Zhang ◽  
Huating He ◽  
Gengying Li ◽  
Haiyang Wang

Accurate estimation of cable tension is crucial for the structural health monitoring of cable-supported structures. Identifying the cable’s force from its vibration data is probably the most widely adopted method of cable tension estimation. According to string theory, the accuracy of estimated cable tension is highly related to identified modal parameters including natural frequencies and frequency order. To alleviate the factors that impact the accuracy of modal parameters when using the peak-picking method in wireless sensor networks, a fully automated and robust identifying method is proposed in this paper. This novel method was implemented on the Xnode wireless sensor system and validated with the data obtained from Jindo Bridge. The experiment results indicate that, through this method, the wireless sensor is able to distinguish the cognizable power spectrum, extract the peaks, eliminate false frequencies and determine frequency orders automatically to estimate cable tension force without any manual intervention or preprocessing. Meanwhile, the results of natural frequencies, corresponding orders and cable tension force obtained from the Xnode system show excellent agreement with the results obtained using the Matlab program method. This demonstrates the effectiveness and reliability of the Xnode estimation system. Furthermore, this method is also appropriate for other high-performance wireless sensor network systems to realize self-identification of cable in long-term monitoring.


2010 ◽  
Vol 2010 ◽  
pp. 1-9 ◽  
Author(s):  
Y. Lei ◽  
W. A. Shen ◽  
Y. Song ◽  
Y. Wang

Wireless sensing systems have been proposed for structural heath monitoring in recent years. While wireless sensors are cost-competitive compared to tethered monitoring systems, their significant merit also lies in their embedded computational capabilities. In this paper, performance of the two embedded engineering algorithms, namely the fast Fourier transform and peak-picking algorithm implemented in the wireless sensing nodes codeveloped at Stanford University and the University of Michigan is investigated through laboratory and field experimental studies. Furthermore, the wireless sensor network embedded with the engineering algorithms is adopted for the identification of structural modal parameters and forces in steel bridge cables. Identification results by the embedded algorithms in the intelligent wireless sensors are compared with those obtained by conventional offline analysis of the measured time-history data. Such a comparison serves to validate the effectiveness of the intelligent wireless sensor network. In addition, it is shown that self-interrogation of measurement data based upon the two embedded algorithms in wireless sensor nodes greatly reduces the amount of data to be transmitted by the wireless sensing network. Thus, the intelligent wireless sensors offer scalable network solutions that are power-efficient for the health monitoring of civil infrastructures.


2013 ◽  
Vol 12 (3_4) ◽  
pp. 465-482 ◽  
Author(s):  
Jinsuk Yim ◽  
Ming L. Wang ◽  
Sung Woo Shin ◽  
Chung-Bang Yun ◽  
Hyung-Jo Jung ◽  
...  

Measurement ◽  
2021 ◽  
pp. 110053
Author(s):  
Hyeon Cheol Jo ◽  
Soo Hyung Kim ◽  
Jisang Lee ◽  
Hong-Gyoo Sohn ◽  
Yun Mook Lim

2021 ◽  
Author(s):  
Li Dong ◽  
Bin Xie ◽  
Dongli Sun ◽  
Yizhuo Zhang

<p>Cable forces are primary factors influencing the design of a cable-stayed bridge. A fast and practical method for cable force estimation is proposed in this paper. For this purpose, five input parameters representing the main characteristics of a cable-stayed bridge and two output parameters representing the cable forces in two key construction stages are defined. Twenty different representative cable-stayed bridges are selected for further prediction. The cable forces are carefully optimized through finite element analysis. Then, discrete and fuzzy processing is applied in data processing to improve their reliability and practicality. Finally, based on the input parameters of a target bridge, the maximum possible output parameters are calculated by Bayes estimation based on the processed data. The calculation results show that the average prediction error of this method is less than 1% for the twenty bridges themselves, which provide the primary data and less than 3% for an under-construction bridge.</p>


2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Li Rui ◽  
Xie Xiaoyu ◽  
Duan Xueyan

In Yunnan and other plateau mountainous areas, hydropower and mineral resources are abundant, and there are relatively many vehicles used for the transportation of large hydropower facilities. The widespread phenomenon of vehicle overload causes severe fatigue among the drivers. However, there is no reference vehicle load spectrum for fatigue analysis in the existing research. The application of wireless sensing technology to bridge health monitoring is favorable for the entire monitoring system’s low-cost and intelligent development. In this study, wireless sensors are used to collect sensing data in the measured area and perform preliminary filtering processing. The data collected by the sensing layer is aggregated at the TD gateway layer to realize local short-term storage of monitoring data, and 3G wireless transmission is used for the effective processing of the data. The clustering method is used to classify the vehicle models based on investigating the most representative expressway traffic flow information in Yunnan Province. Moreover, the weighted probability distribution model of different vehicle models is established through statistical analysis, which simplifies the composition’s fatigue intensity spectrum model. The selection of five vehicles of the equivalent model followed by a six-axle vehicle has the most significant impact on bridge damage as the standard fatigue vehicle. The research results establish a basis for the fatigue design of highway bridges in plateau and mountainous areas and provide data to establish vehicle fatigue load spectra in national highway regions.


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