• Structural Engineering and Mechanics
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• v.68 no.2
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• pp.227-235
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• 2018

Cables are the main load-bearing members of prestressed structure and other tensegrity structures. Based on the static equilibrium principle, a new cable force identification method considering cable flexural rigidity is proposed. Its computational formula is derived and the strategy to solve its implicit formula is introduced as well. In order to improve the reliability and practicality of this method, the influence of the cable flexural rigidity on cable force identification accuracy is also investigated. Through cable force identification experiments, the relationships among certain parameters including jacking force, jacking displacement, initial cable force, and sectional area (flexural rigidity) are studied. The results show that the cable force calculated by the proposed method considering flexural rigidity is in good agreement with the finite element results and experimental results. The proposed method with high computational accuracy and resolution efficiency can avoid the influences of the boundary condition and the length of the cable on calculation accuracy and is proven to be conveniently applied to cable force identification in practice.

• Smart Structures and Systems
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• v.29 no.3
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• pp.475-484
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• 2022

The loss of cable tension for civil infrastructure reduces structural bearing capacity and causes harmful deformation of structures. Currently, most of the structural health monitoring (SHM) approaches for cables rely on contact transducers. This paper proposes a cable tension identification technology using percussion sound, which provides a fast determination of steel cable tension without physical contact between cables and sensors. Notably, inspired by the concept of tensioning strings for piano tuning, this proposed technology predicts cable tension value by deep learning assisted classification of "percussion" sound from tapping a steel cable. To simulate the non-linear mapping of human ears to sound and to better quantify the minor changes in the high-frequency bands of the sound spectrum generated by percussions, Mel-frequency cepstral coefficients (MFCCs) were extracted as acoustic features to train the deep learning network. A convolutional neural network (CNN) with four convolutional layers and two global pooling layers was employed to identify the cable tension in a certain designed range. Moreover, theoretical and finite element methods (FEM) were conducted to prove the feasibility of the proposed technology. Finally, the identification performance of the proposed technology was experimentally investigated. Overall, results show that the proposed percussion-based technology has great potentials for estimating cable tension for in-situ structural safety assessment.

• Smart Structures and Systems
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• v.17 no.3
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• pp.445-470
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• 2016

In this study, the Bayesian probabilistic framework is investigated for modal identification and modal identifiability based on the field measurements provided in the structural health monitoring benchmark problem of an instrumented cable-stayed bridge named Ting Kau Bridge (TKB). The comprehensive structural health monitoring system on the cable-stayed TKB has been operated for more than ten years and it is recognized as one of the best test-beds with readily available field measurements. The benchmark problem of the cable-stayed bridge is established to stimulate investigations on modal identifiability and the present paper addresses this benchmark problem from the Bayesian prospective. In contrast to deterministic approaches, an appealing feature of the Bayesian approach is that not only the optimal values of the modal parameters can be obtained but also the associated estimation uncertainty can be quantified in the form of probability distribution. The uncertainty quantification provides necessary information to evaluate the reliability of parametric identification results as well as modal identifiability. Herein, the Bayesian spectral density approach is conducted for output-only modal identification and the Bayesian model class selection approach is used to evaluate the significance of different modes in modal identification. Detailed analysis on the modal identification and modal identifiability based on the measurements of the bridge will be presented. Moreover, the advantages and potentials of Bayesian probabilistic framework on structural health monitoring will be discussed.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.105-106
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• 2006

It is important to identify the exact target optical cable that a worker look for when he executes a cable construction work. Until now, An Optical cable identification work has been done by a manual pulling of an optical cable or by the method of using RF signals. But these methods not only consume much time and labor costs but also have a distance limit of the optical cable to identify. In this paper, we propose a method that uses the phase change signal of the optical interferometer to identify an optical cable. With the proposed method the field worker can have an efficient tool to prevent miscutting the optical cable and can also save operating costs.

• Steel and Composite Structures
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• v.44 no.2
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• pp.241-254
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• 2022

The cables in a cable-stayed bridge are critical load-carrying parts. The potential damage to cables should be identified early to prevent disasters. In this study, an efficient deep learning model is proposed for the damage identification of cables using both a multi-layer perceptron (MLP) and a graph neural network (GNN). Datasets are first generated using the practical advanced analysis program (PAAP), which is a robust program for modeling and analyzing bridge structures with low computational costs. The model based on the MLP and GNN can capture complex nonlinear correlations between the vibration characteristics in the input data and the cable system damage in the output data. Multiple hidden layers with an activation function are used in the MLP to expand the original input vector of the limited measurement data to obtain a complete output data vector that preserves sufficient information for constructing the graph in the GNN. Using the gated recurrent unit and set2set model, the GNN maps the formed graph feature to the output cable damage through several updating times and provides the damage results to both the classification and regression outputs. The model is fine-tuned with the original input data using Adam optimization for the final objective function. A case study of an actual cable-stayed bridge was considered to evaluate the model performance. The results demonstrate that the proposed model provides high accuracy (over 90%) in classification and satisfactory correlation coefficients (over 0.98) in regression and is a robust approach to obtain effective identification results with a limited quantity of input data.

• Structural Engineering and Mechanics
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• v.19 no.2
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• pp.123-139
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• 2005

An automatic modal identification program is developed for continuous extraction of modal parameters of three cable-supported bridges in Hong Kong which are instrumented with a long-term monitoring system. The program employs the Complex Modal Indication Function (CMIF) algorithm for identifying modal properties from continuous ambient vibration measurements in an on-line manner. By using the LabVIEW graphical programming language, the software realizes the algorithm in Virtual Instrument (VI) style. The applicability and implementation issues of the developed software are demonstrated by using one-year measurement data acquired from 67 channels of accelerometers permanently installed on the cable-stayed Ting Kau Bridge. With the continuously identified results, variability in modal vectors due to varying environmental conditions and measurement errors is observed. Such an observation is very helpful for selection of appropriate measured modal vectors for structural health monitoring use.

• Smart Structures and Systems
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• v.11 no.5
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• pp.533-553
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• 2013

In this paper, system identification of a cable-stayed bridge in Korea, the Hwamyung Bridge, is performed using vibration responses measured by a wireless sensor system. First, an acceleration based-wireless sensor system is employed for the structural health monitoring of the bridge, and wireless sensor nodes are deployed on a deck, a pylon and several selected cables. Second, modal parameters of the bridge are obtained both from measured vibration responses and finite element (FE) analysis. Frequency domain decomposition and stochastic subspace identification methods are used to obtain the modal parameters from the measured vibration responses. The FE model of the bridge is established using commercial FE software package. Third, structural properties of the bridge are updated using a modal sensitivity-based method. The updating work improves the accuracy of the FE model so that structural behaviors of the bridge can be represented better using the updated FE model. Finally, cable forces of the selected cables are also identified and compared with both design and lift-off test values.

• Smart Structures and Systems
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• v.15 no.2
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• pp.425-445
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• 2015

Parametric identification of structures is one of the important aspects of structural health monitoring. Most of the techniques available in the literature have been proved to be effective for structures with small degree of freedoms. However, the problem becomes challenging when the structure system is large, such as bridge structures. Therefore, it is highly desirable to develop parametric identification methods that are applicable to complex structures. In this paper, the LSE based techniques will be combined with the substructure approach for identifying the parameters of a cable-stayed bridge with large degree of freedoms. Numerical analysis has been carried out for substructures extracted from the 2-dimentional (2D) finite element model of a cable-stayed bridge. Only vertical white noise excitations are applied to the structure, and two different cases are considered where the structural damping is not included or included. Simulation results demonstrate that the proposed approach is capable of identifying the structural parameters with high accuracy without measurement noises.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.880-883
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• 2015

Power-line communication technology is proposed to identify cables of power distribution systems. It can extend the application area of power-line communication. Distribution line cable circuits have only a limited ability to carry higher frequencies. Typically power transformers in the distribution system prevent propagating the higher frequency carrier signal. The proposed method uses the limited propagation ability to identify the cable. A novel power cable identification system is designed and implemented. The system consists of a transmitter and a receiver with power-line communication module. Some experiments are conducted to verify the theoretical concepts. Also some simulations are done to help and understand the concepts by using Simulink simulator.

• International Journal of Aeronautical and Space Sciences
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• v.18 no.2
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• pp.365-377
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• 2017

In this paper, we propose a method to identify the parameters of a rotorcraft slung load system using the modal characteristics of a flexible cable. The proposed method estimates the length of the cable and the mass of the payload by means of a frequency analysis. Dynamic equations of the slung load system with the flexible cable are derived using Udwadia-Kalaba equation (UKE) in order to build a simulation program, and the similarity of the simulated slung load movement is verified by comparison with flight test results. Using the computer simulation program, we show that the proposed method works well within various parameter ranges.