• Structural Monitoring and Maintenance
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• v.2 no.1
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• pp.77-93
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• 2015

The idea of using measured dynamic characteristics for damage detection is attractive because it allows for a global evaluation of the structural health and condition. However, vibration-based damage detection for complex structures such as long-span cable-supported bridges still remains a challenge. As a suspension or cable-stayed bridge involves in general thousands of structural components, the conventional damage detection methods based on model updating and/or parameter identification might result in ill-conditioning and non-uniqueness in the solution of inverse problems. Alternatively, methods that utilize, to the utmost extent, information from forward problems and avoid direct solution to inverse problems would be more suitable for vibration-based damage detection of long-span cable-supported bridges. The auto-associative neural network (ANN) technique and the probabilistic neural network (PNN) technique, that both eschew inverse problems, have been proposed for identifying and locating damage in suspension and cable-stayed bridges. Without the help of a structural model, ANNs with appropriate configuration can be trained using only the measured modal frequencies from healthy structure under varying environmental conditions, and a new set of modal frequency data acquired from an unknown state of the structure is then fed into the trained ANNs for damage presence identification. With the help of a structural model, PNNs can be configured using the relative changes of modal frequencies before and after damage by assuming damage at different locations, and then the measured modal frequencies from the structure can be presented to locate the damage. However, such formulated ANNs and PNNs may still be incompetent to identify damage occurring at the deck members of a cable-supported bridge because of very low modal sensitivity to the damage. The present study endeavors to enhance the damage identification capability of ANNs and PNNs when being applied for identification of damage incurred at deck members. Effort is first made to construct combined modal parameters which are synthesized from measured modal frequencies and modal shape components to train ANNs for damage alarming. With the purpose of improving identification accuracy, effort is then made to configure PNNs for damage localization by adapting the smoothing parameter in the Bayesian classifier to different values for different pattern classes. The performance of the ANNs with their input being modal frequencies and the combined modal parameters respectively and the PNNs with constant and adaptive smoothing parameters respectively is evaluated through simulation studies of identifying damage inflicted on different deck members of the double-deck suspension Tsing Ma Bridge.

• Journal of Ocean Engineering and Technology
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• v.14 no.1
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• pp.80-87
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• 2000

Despite many combined research efforts outstanding needs exist to develop robust safety-estimation methods for large complex structures. This paper presents a practical damage identification scheme which can be applied to truss structures using only limited modal responses. firstly a theory of pattern recognition (PR) is described. Secondly existing damage-detection algorithms are outlined and a newly-derived algorithms for truss structures. Finally the feasibility of the proposed scheme is evaluated using numerical examples of plane truss structures.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1990.10a
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• pp.79-84
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• 1990

The modal parameter estimations of linear multi-degree-of-freedom structural dynamic systems are carried out in time domain. For this purpose, the equation of motion is transformed into the autoregressive and moving average model with auxiliary stochastic input (ARMAX) model. The parameters of the ARMAX model are estimated by using the sequential prediction error method. Then, the modal parameters of the system are obtained thereafter. Experimental results are given for a 3-story building model subject to ground exitations.

• Wind and Structures
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• v.36 no.1
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• pp.61-73
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• 2023

In this article, a novel structural modal parameters identification methodology is developed to determine the natural frequencies and damping ratios of civil structures based on the symplectic geometry mode decomposition (SGMD) approach. The SGMD approach is a new decomposition algorithm that can decompose the complex response signals with better decomposition performance and robustness. The novel method firstly decomposes the measured structural vibration response signals into individual mode components using the SGMD approach. The natural excitation technique (NExT) method is then used to obtain the free vibration response of each individual mode component. Finally, modal natural frequencies and damping ratios are identified using the direct interpolating (DI) method and a curve fitting function. The effectiveness of the proposed method is demonstrated based on numerical simulation and field measurement. The structural modal parameters are identified utilizing the simulated non-stationary responses of a frame structure and the field measured non-stationary responses of a supertall building during a typhoon. The results demonstrate that the developed method can identify the natural frequencies and damping ratios of civil structures efficiently and accurately.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1998.04a
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• pp.244-249
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• 1998

The present paper improves the direct identification scheme based upon the equation error formulation with incomplete modal data. First, an indirect estimation technique is considered for estimating unmeasured elements of latent vectors by the combined use of a model and measured incomplete eigen vectors. It is used for estimating the other elements of eigen vectors, which are essential for identification but not available. Next an index is introduced here to indicate the quality of estimation with respect to the mode and the measured positions. A sensitivity formula for eigenvalues with respect to the unknown joint coefficient is also derived to select the modes appropriate for identification. An identification strategy is suggested to meet with practical problems with the help of the index and sensitivity formula. The index and the sensitivity are proved to be useful for selecting measurement positions and modes appropriate for identification A comprehensive simulation study is performed to test the proposed method.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.7
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• pp.1156-1165
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• 1997

Complex modal testing is employed for the in-situ parameter identification of a four-axis active magnetic bearing system while the system is in operation. In the test, magnetic bearings are used as exciters as well as actuators for feedback control. The experimental results show that the directional frequency response function, which is properly defined in the complex domain, is a powerful tool for identification of bearing as well as modal parameters. It is also shown that the position and current stiffnesses can be accurately estimated using the relations between the measured forces, displacements, and currents.

• Smart Structures and Systems
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• v.7 no.2
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• pp.133-152
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• 2011

System identification is a fundamental step towards the application of structural health monitoring and damage detection techniques. On this respect, the development of evolved identification strategies is a priority for obtaining reliable and repeatable baseline modal parameters of an undamaged structure to be adopted as references for future structural health assessments. The paper presents the identification of the modal parameters of the Guangzhou New Television Tower, China, using a data-driven stochastic subspace identification (SSI-data) approach complemented with an appropriate automatic mode selection strategy which proved to be successful in previous literature studies. This well-known approach is based on a clustering technique which is adopted to discriminate structural modes from spurious noise ones. The method is applied to the acceleration measurements made available within the task I of the ANCRiSST benchmark problem, which cover 24 hours of continuous monitoring of the structural response under ambient excitation. These records are then subdivided into a convenient number of data sets and the variability of modal parameter estimates with ambient temperature and mean wind velocity are pointed out. Both 10 minutes and 1 hour long records are considered for this purpose. A comparison with finite element model predictions is finally carried out, using the structural matrices provided within the benchmark, in order to check that all the structural modes contained in the considered frequency interval are effectively identified via SSI-data.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1997.04a
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• pp.97-105
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• 1997

A methodology to identify a baseline modal model of a complicated 3-D structure using limited structural and modal information is experimentally examined. In the first part, a system's identification theory for the methodology to identify, baseline modal responses of the structure is outlined. Next, an algorithm is designed to build a generic finite element model of the baseline structure and to calibrate the model by using only a set of post-damage modal parameters. In the second part, the feasibility of the methodology is examined experimentally using a field-tested truss bridge far which only post-damaged modal responses were measured for a few vibration modes. For the complex 3-D bridge with many members, we analyzed to identify unknown stiffness parameters of the structure by using modal parameters of the initial two modes of vibration.

• Smart Structures and Systems
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• v.10 no.4_5
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• pp.393-410
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• 2012

The 610 m high Canton Tower (formerly named Guangzhou New Television Tower) is currently considered as a benchmark problem for structural health monitoring (SHM) of high-rise slender structures. In the benchmark study task I, a set of 24-hour ambient vibration measurement data has been available for the output-only system identification study. In this paper, the vector autoregressive models (ARV) method is adopted in the operational modal analysis (OMA) for this TV tower. The identified natural frequencies, damping ratios and mode shapes are presented and compared with the available results from some other research groups which used different methods, e.g., the data-driven stochastic subspace identification (SSI-DATA) method, the enhanced frequency domain decomposition (EFDD) algorithm, and an improved modal identification method based on NExT-ERA technique. Furthermore, the environmental effects on the estimated modal parameters are also discussed.

• Structural Engineering and Mechanics
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• v.26 no.2
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• pp.179-190
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• 2007

Damage detection based on measured vibration data has received intensive studies recently. Frequently, the damage to a structure may be reflected by a change of some system parameters, such as a degradation of the stiffness. In this paper, we apply a method to nondestructively locate and estimate the severity of damage in corrosion pipeline for which a few natural frequencies or mode shapes are available. The method is based on the strain modal sensitivity ratio (SMSR) and the orthogonality conditions sensitivities (OCS) applied to vibration features identified during the monitoring of the pipeline. The advantage of these methods is that it only requires measuring few modal parameters. The SMSR-based and OCS-based damage detection methods are illustrated using computer-simulated and laboratory testing data. The results show that the current method provides a precise indication of both the location and the extent of corrosion pipeline.