Condition assessment of cables by pattern recognition of vehicle-induced cable tension ratio. (15th January 2018)

• Record Type: Journal Article
• Authors: Li, Shunlong; Wei, Shiyin; Bao, Yuequan; Li, Hui
• Is Part Of: Engineering structures. Volume 155(2018)
• Page Start: 1
• Page End: 15
• Publication Date: 2018-01-15
• Subjects: Structural health monitoring -- Cable-stayed bridges -- Cable tension force -- Condition assessment -- Pattern recognition -- Gaussian mixture model; Structural engineering -- Periodicals; Structural analysis (Engineering) -- Periodicals; Construction, Technique de la -- Périodiques; Génie parasismique -- Périodiques; Pression du vent -- Périodiques; Earthquake engineering; Structural engineering; Wind-pressure; Periodicals; 624.105
• DOI: 10.1016/j.engstruct.2017.09.063
• Languages: English
• ISSNs: 0141-0296

Abstract

Highlights: A machine learning-based condition assessment method for stay cables is proposed. Tension ratio is proposed as an effective feature that reflects cable's condition. The application on the in-situ SHM system implemented on the cable-stayed bridge. Abstract: The stay cables are one of most critical elements for cable-stayed bridges. This paper proposes a machine-learning based condition assessment method for stay cables by using the monitored cable tension force. First, based on the correlation of cable tension response between cable pairs (defined as the two cables at the upriver side and the opposite downriver side in the double cable planes), cable tension ratio is extracted as the feature variable, and the cable tension ratio is defined as the ratio of vehicle-induced cable tension between a cable pair. It is found that cable tension ratio is only related with cable properties and the transverse position of a vehicle over the deck. Vehicles on the bridge naturally cluster themselves into a few clusters that correspond to the traffic lanes, i.e. the vehicles in one lane form a cluster. Consequently, the vehicle-induced cable tension ratio forms the corresponding clusters or patterns. Gaussian Mixture Model (GMM) is employed for modelling the patterns of cable tension ratio, and each pattern (corresponds to a certain traffic lane) is modelled by a mono-Gaussian distribution. The Gaussian distribution parameters of tension ratio are used as condition indicator of stay cables because they are only related to cable conditions (the information of vehicle transverse location is presented in the number of tension ration patterns). The number of patterns which represents the model complexity are determined by Bayesian Information Criteria (BIC), while other parameters of GMM are estimated by using Expectation-Maximization algorithm under the Maximum Likelihood criteria, based on the monitored cable tension force. The cable condition is then evaluated according to the variation in estimated parameters of GMM. It is noted that pre-process of source separation is conducted to make the cable tension ratio independent from vehicle weight, environmental variant, and possible sensor errors. An FE model analysis is carried out to qualitatively illustrate the principle of the proposed method and physical sense of the cable tension ratio.