Tension Force Estimation of Cable-Stayed Bridges Based on Computer Vision Without the Need for Direct Measurement of Mechanical Parameters of the Cables

Commonly, accelerometers are used to determine the tension force in cables through an indirect process; however, it is necessary to know the mechanical parameters of each element, such as mass and length. Typically, obtaining or measuring these parameters is not feasible. Therefore, this research pr...

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Vydané v:Sensors (Basel, Switzerland) Ročník 25; číslo 13; s. 3910
Hlavní autori: Guzman-Acevedo, German Michel, Quintana-Rodriguez, Juan A., Vazquez-Becerra, Guadalupe Esteban, Martinez-Trujano, Luis Alvaro, Carrion-Viramontes, Francisco J., Garcia-Armenta, Jorge
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Switzerland MDPI AG 23.06.2025
MDPI
Predmet:
Accelerometers
Algorithms
Bridges
Cables
Case studies
Computer vision
Data entry
Digital cameras
Machine learning
Machine vision
Measurement
Methods
SHM
smartphone
tension cable
Unmanned aerial vehicles
Vibration
video-images
Mexico
SHM
tension cable
smartphone
video-images
computer vision
ISSN:1424-8220, 1424-8220
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Popis
Shrnutí:Commonly, accelerometers are used to determine the tension force in cables through an indirect process; however, it is necessary to know the mechanical parameters of each element, such as mass and length. Typically, obtaining or measuring these parameters is not feasible. Therefore, this research proposed an alternative methodology to indirectly estimate them based on historical information about the so-called classic instruments (accelerometers and hydraulic jack). This case study focused on the Rio Papaloapan Bridge located in Veracruz, Mexico, a structure that has experienced material casting issues due to inadequate heat treatment in some cable top anchor over its lifespan. Thirteen cables from the structure were selected to evaluate the proposed methodology, yielding results within 3.8% of difference compared to direct tension estimation generated by a hydraulic jack. Furthermore, to enhance data collection, this process was complemented using a computer vision methodology. This involved remotely measuring the vibration frequency of cables from high-resolution videos recorded with a smartphone. The non-contact method was validated in a laboratory using a vibrating table, successfully estimating oscillation frequencies from video-recording with a fixed camera. A field test on eight cables of a bridge was also conducted to assess the performance and feasibility of the proposed method. The results demonstrated an RMS Error of approximately 2 mHz and a percentage difference in the tension force estimation below 3% compared to an accelerometer measurement approach. Finally, it was determined that this composed methodology for indirect tension force determination is a viable option when: (1) cables are challenging to access; (2) there is no line of sight between the camera and cables outside the bridge; (3) there is a lack of information about the mechanical parameters of the cables.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s25133910