• 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.

• Journal of Korean Association for Spatial Structures
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• v.15 no.1
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• pp.14-21
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• 2015

• Smart Structures and Systems
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• v.28 no.6
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• pp.799-810
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• 2021

When monitoring the structural integrity of a bridge using data collected through accelerometers, identifying the profile of the load exerted on the bridge from the vehicles passing over it becomes a crucial task. In this study, the speed and location of vehicles on the deck of a bridge is reconfigured using real-time video to implicitly associate the load applied to the bridge with the response from the bridge sensors to develop an image-based deep learning network model. Instead of directly measuring the load that a moving vehicle exerts on the bridge, the intention in the proposed method is to replace the correlation between the movement of vehicles from CCTV images and the corresponding response by the bridge with a neural network model. Given the framework of an input-output-based system identification, CCTV images secured from the bridge and the acceleration measurements from a cantilevered beam are combined during the process of training the neural network model. Since in reality, structural damage cannot be induced in a bridge, the focus of the study is on identifying local changes in parameters by adding mass to a cantilevered beam in the laboratory. The study successfully identified the change in the material parameters in the beam by using the deep-learning neural network model. Also, the method correctly predicted the acceleration response of the beam. The proposed approach can be extended to the structural health monitoring of actual bridges, and its sensitivity to damage can also be improved through optimization of the network training.

• Structural Monitoring and Maintenance
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• v.4 no.1
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• pp.33-52
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• 2017

A cantilever beam with a breathing crack is studied to improve the breathing crack identification sensitivity by the parametric optimization of sample entropy and wavelet transformation. Crack breathing is a special bi-linear phenomenon experienced by fatigue cracks which are under dynamic loadings. Entropy is a measure, which can quantify the complexity or irregularity in system dynamics, and hence employed to quantify the bi-linearity/irregularity of the vibration response, which is induced by the breathing phenomenon of a fatigue crack. To improve the sensitivity of entropy measurement for crack identification, wavelet transformation is merged with entropy. The crack identification is studied under different sinusoidal excitation frequencies of the cantilever beam. It is found that, for the excitation frequencies close to the first modal frequency of the beam structure, the method is capable of detecting only 22% of the crack depth percentage ratio with respect to the thickness of the beam. Using parametric optimization of sample entropy and wavelet transformation, this crack identification sensitivity is improved up to 8%. The experimental studies are carried out, and experimental results successfully validate the numerical parametric optimization process.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.893-913
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• 2017

The handwriting based person identification systems use their designer's perceived structural properties of handwriting as features. In this paper, we present a system that uses those structural properties as features that graphologists and expert handwriting analyzers use for determining the writer's personality traits and for making other assessments. The advantage of these features is that their definition is based on sound historical knowledge (i.e., the knowledge discovered by graphologists, psychiatrists, forensic experts, and experts of other domains in analyzing the relationships between handwritten stroke characteristics and the phenomena that imbeds individuality in stroke). Hence, each stroke characteristic reflects a personality trait. We have measured the effectiveness of these features on a subset of handwritten Devnagari and Latin script datasets from the Center for Pattern Analysis and Recognition (CPAR-2012), which were written by 100 people where each person wrote three samples of the Devnagari and Latin text that we have designed for our experiments. The experiment yielded 100% correct identification on the training set. However, we observed an 88% and 89% correct identification rate when we experimented with 200 training samples and 100 test samples on handwritten Devnagari and Latin text. By introducing the majority voting based rejection criteria, the identification accuracy increased to 97% on both script sets.

• Journal of the Korea institute for structural maintenance and inspection
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• v.8 no.2
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• pp.137-145
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• 2004

The paper provides a methodology of identifying a substructure model when sectional and material properties of the structure are not the a priori information. In defining a substructure model, it is required that structural responses be consistent with the actual behavior of the part of the structure. Substructure model is identified by estimating boundary spring constants and stiffness properties of the substructure. Static and modal system identification methods have been applied using responses measured at limited locations within the substructure. Simulation studies for static and dynamic responses have been carried. The results and associated problems are discussed in the paper. The procedure has been also applied to an actual multi-span plate-girder Gerber-type bridge with dynamic responses obtained from a moving truck test and construction blasting vibrations.

• Structural Monitoring and Maintenance
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• v.7 no.3
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• pp.195-213
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• 2020

In this paper an extension to the method for the identification of mechanical parameters of nonlinear systems proposed in Breccolotti and Materazzi (2007) for MDoF systems is presented. It can be used for damage identification purposes when damage modifies the linear characteristics of the investigated structure. It is based on the following two main features: the solution of the Fokker-Planck equation that describes the response probabilistic properties of the system when it is excited by external Gaussian loads; and a model updating technique that minimizes the differences between the response of the actual system and that of a parametric system used to identify the unknown parameters. Numerical analysis, that simulate virtual experimental tests, are used in the paper to show the capabilities of the method and to analyse the conditions required for its application.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1995.04a
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• pp.136-143
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• 1995

The problem of the structural identification becomes important, particularly with relation to the rapid increase of the number of the damaged or deteriorated structures, such as highway bridges, buildings, and industrial facilities. This paper summarizes the recent studies related to those problems by the present authors. The system identfication methods are generally classified as the time domain and the frequency domain methods. As time doamin methods, the sequential algorithms such as the extended Kalman filter and the sequential prediction error method are studied. Several techniques for improving the convergences are incorporated. As frequency domain methods, a new frequency response function estimator is introduced. For damage estimation of existing structures, the modal perturbation and the sensitivity matrix methods are studied. From the example analysis, it has been found that the combined utilization of the measurement data for the static response and the dynamic (modal) properties are very effictive for the damage estimation.

• Computational Structural Engineering
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• v.10 no.3
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• pp.157-165
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• 1997

This research aims to develop an algorithm of optimal transducer placement for health monitoring of large structural system. The structural vibration response-based health monitoring is considered one of the best for the system which requires a long-term, continuous monitoring. In its experimental modal testing, however, it is difficult to decide on the measurement locations and their number, especially for complex structures, which have a major influence on the quality of the results. In order to minimize the number of sensing operations and optimize the transducer location while maximizing the accuracy of results, this paper discusses about an optimum transducer placement criterion suitable for the identification of structural damage for continuous health monitoring. As a criterion algorithm, it proposes the Kinetic Energy Optimization Technique (EOT), and then addresses the numerical issues which are subsequently applicable to actual experiment where a bridge model is used. By using the experimental data, it compares the EOT with the EIM(Effective Indefence Method) which is generally used to optimize the transducer placement for the damage identification and control purposes. The comparison conclusively shows that the EOT algorithm proposed in this paper is preferable when a structure is to be instrumented with fewer sensors for monitoring purpose.

• Earthquakes and Structures
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• v.4 no.2
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• pp.203-217
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• 2013

In recent decades, it has been realized that increasing the lateral stiffness of structure subjected to lateral loads is not the only parameter enhancing safety or reducing damage. Factors such as ductility and damping govern the structural response due to lateral loads. Despite the significant contribution of damping in resisting lateral loads, especially at resonance, there is no accurate mathematical representation for it. The main objective of this study is to develop a damping identification procedure for linear systems based on a mixed numerical-experimental approach, assuming viscous damping. The proposed procedure has been applied to a laboratory experiment associated with a numerical model, where a hollow rectangular steel cantilever column, having three lumped masses, has been fixed on a shaking table subjected to different exciting waves. The modal damping ratio has been identified; in addition, the effect of adding filling material to the hollow specimen has been studied in relation to damping enhancement. The results have revealed that the numerically computed response based on the identified damping is in a good fitting with the measured response. Moreover, the filling material has a significant effect in increasing the modal damping.