• The Journal of Engineering Geology
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• v.19 no.2
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• pp.139-144
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• 2009

The microseismic monitoring system with wide range of frequency has been operating in real time and it is remotely monitored at indoor and on-site for one year. This system was constructed and established in order to secure the safe and effective operation of the KAERI Underground Research Tunnel(KURT). For one year monitoring work, total 14 events were recorded in the vicinity of the KURT, and the majority of events are regarded as ultramicroseismic earthquake and artificial impacts around the tunnel. The major event is the magnitude 3.4 earthquake which was centered around Gongju city, Chungnam Province. It means that there is no significant evidence of high frequency microseismic event, which is associated with fracture initiation and/or propagation in the rock mass and shotcrete. Three components sensor was applied in order to analyze and define the direction of vibration as well as an epicenter of microseismic origin, and also properly designed and installed in a small borehole. This monitoring system is able to predict the location and timing of fracturing of rock mass and rock fall around an undreground openings as well as analysis on safety of various kinds of engineering structures such as nuclear facilities and other structures.

• Journal of Advanced Navigation Technology
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• v.23 no.1
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• pp.84-89
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• 2019

This is a pilot study of machine learning based structural health monitoring system using flight data of composite aircraft. In this study, the most suitable machine learning algorithm for structural health monitoring was selected and dimensionality reduction method for application on the actual flight data was conducted. For these tasks, impact test on the cantilever beam with added mass, which is the simulation of damage in the aircraft wing structure was conducted and classification model for damage states (damage location and level) was trained. Through vibration test of cantilever beam with fiber bragg grating (FBG) sensor, data of normal and 12 damaged states were acquired, and the most suitable algorithm was selected through comparison between algorithms like tree, discriminant, support vector machine (SVM), kNN, ensemble. Besides, through neighborhood component analysis (NCA) feature selection, dimensionality reduction which is necessary to deal with high dimensional flight data was conducted. As a result, quadratic SVMs performed best with 98.7% for without NCA and 95.9% for with NCA. It is also shown that the application of NCA improved prediction speed, training time, and model memory.

• Journal of the Korea institute for structural maintenance and inspection
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• v.18 no.6
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• pp.147-154
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• 2014

The cable damages of the bridge structures induce very important impact on the structural safety, which implies the close monitoring of the cable damage is required to secure sustained safety of the bridges. Most usual available maintenance techniques are based on the monitoring the change of the natural frequency of the structures by damages. However, existing method are based on vibration method to calculate lateral vibration and system identification can calculate the axial stiffness using sensitivity equation by trial error method. But the frequency study by the longitudinal movement need because of the sag effect in system identification. This study proposes a new method to investigate the damage magnitudes and status. The method improves the accuracies in the magnitudes and status of damages by adopting the natural frequency of longitudinal movement. The study results have been validated by comparing them with the approximate solution of FEM. Thus, the relationship of cable damage and frequency appear with relation that the severe damage has the little frequency. If we know the real frequency we can estimate the cable damage severity using this relationship. This method can be possible the efficient management of the cable damage.

• Journal of the Earthquake Engineering Society of Korea
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• v.10 no.6 s.52
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• pp.115-123
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• 2006

This paper presents long-term dynamic characteristics of a cable-stayed bridge where installed SHM (Structural Health Monitoring) system. Modal parameters such as natural frequencies and mode shapes are identified by modal analysis using three dimensional finite element model. The developed baseline model has a good correlation with measured natural frequencies identified from field ambient vibrations. By statistical data processing between measured natural frequencies and temperatures, it is demonstrated that the natural frequency is in linearly inverse proportion to the temperature. The estimation of temperature effects against frequency variations is performed. Mode shapes are identified from the TDD (Time Domain Decomposition) technique for ambient vibration measurements. Finally, these results demonstrate that the TDD method can apply to identify modal parameters of a cable-stayed bridge.

• Smart Structures and Systems
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• v.12 no.1
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• pp.55-71
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• 2013

Structural health monitoring (SHM) is vital for detecting the onset of damage and for preventing catastrophic failure of civil infrastructure systems. In particular, piezoelectric transducers have the ability to excite and actively interrogate structures (e.g., using surface waves) while measuring their response for sensing and damage detection. In fact, piezoelectric transducers such as lead zirconate titanate (PZT) and poly(vinylidene fluoride) (PVDF) have been used for various laboratory/field tests and possess significant advantages as compared to visual inspection and vibration-based methods, to name a few. However, PZTs are inherently brittle, and PVDF films do not possess high piezoelectricity, thereby limiting each of these devices to certain specific applications. The objective of this study is to design, characterize, and validate piezoelectric nanocomposites consisting of zinc oxide (ZnO) nanoparticles assembled in a PVDF copolymer matrix for sensing and SHM applications. These films provide greater mechanical flexibility as compared to PZTs, yet possess enhanced piezoelectricity as compared to pristine PVDF copolymers. This study started with spin coating dispersed ZnO- and PVDF-TrFE-based solutions to fabricate the piezoelectric nanocomposites. The concentration of ZnO nanoparticles was varied from 0 to 20 wt.% (in 5 % increments) to determine their influence on bulk film piezoelectricity. Second, their electric polarization responses were obtained for quantifying thin film remnant polarization, which is directly correlated to piezoelectricity. Based on these results, the films were poled (at 50 $MV-m^{-1}$) to permanently align their electrical domains and to enhance their bulk film piezoelectricity. Then, a series of hammer impact tests were conducted, and the voltage generated by poled ZnO-based thin films was compared to commercially poled PVDF copolymer thin films. The hammer impact tests showed comparable results between the prototype and commercial samples, and increasing ZnO content provided enhanced piezoelectric performance. Lastly, the films were further validated for sensing using different energy levels of hammer impact, different distances between the impact locations and the film electrodes, and cantilever free vibration testing for dynamic strain sensing.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.16 no.2
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• pp.183-195
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• 2003

In this study, the method of the finite element modeling for free vibration control of beam-type smart structures with bonded plate-type piezoelectric sensors and actuators is proposed. Constitutive equations for the direct piezoelectric effect and converse piezoelectric effect of piezoelectric materials are considered. By using the variational principle, the equations of motion for the smart beam finite element are derived. The proposed 2-node beam finite element is an isoparametric element based on Timoshenko beam theory. Therefore, by analyzing beam-type smart structures with smart beam finite elements, it is possible to simulate the control of the structural behavior by applying voltages to piezoelectric actuators and monitoring of the structural behavior by sensing voltages of piezoelectric sensors. By using the smart beam finite element and constant-gain feed back control scheme, the formulation of the free nitration control for the beam structures with bonded plate-tyPe Piezoelectric sensors and actuators is proposed.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.11a
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• pp.603-606
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• 2006

We present a study on the development of a practical and quantitative technique for the assessment of the structural health condition with using piezoceramic(PZT) sensors. The electro-impedance-based technique with the PZT patches is very sensitive for evaluation of the incipient and small damage in a high frequency range, and however the commonly traditional modal analysis method is effective only for considerably larger damages in low frequency range. The paper presents the technique in detecting and characterizing real-time damage on the specimen that is an aluminum plate fastened with bolts and nuts by different torques and as well a plate with a crack. By using the special arrangement of the PZT sensors, the required longitudinal wave is generated through the specimen. A large number of experiments are conducted and the different conditions of the specimens, i.e. the location and extent of loosening bolts, and the plate with a crack are simulated. respectively. Since fixing and loosening the loosened bolt is controlled by a torque wrench, we can control exactly the experiment of the different torques. Compared with the simulated healthy condition, we can find whether or not there is a damage in the specimen with using an impedance analyzer with the PZT sensors. Several indices are discussed and used for assessing the different simulated damages. As for the location of bolt loosening, the RMSD is found to be the most appropriate index for numerical assessment and as well the RMSD shows strongly linear relationship for assessing the extent of the bolt loosening, and the frequency peak shift ${\Delta}F$ is used to assess the cracked plate. The possibility of repeatability of the pristine condition signatures is also presented and the appropriate frequency range and interval are uniquely selected through large numbers of experiments.

• Structural Engineering and Mechanics
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• v.40 no.4
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• pp.471-488
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• 2011

The Ting Kau Bridge in Hong Kong is a cable-stayed bridge comprising two main spans and two side spans. The bridge deck is supported by three towers, an end pier and an abutment. Each of the three towers consists of a single reinforced concrete mast strengthened by transverse cables and struts. The bridge deck is supported by four inclined planes of cables emanating from anchorages at the tower tops. In view of the heavy traffic on the bridge, and threats from typhoons and earthquakes originated in areas nearby, the dynamic behaviour of long-span cable-supported bridges in the region is always an important consideration in their design. Baseline finite element models of various levels of sophistication have been built not only to match the bridge geometry and cable forces specified on the as-constructed drawings but also to be calibrated using the vibration measurement data captured by the Wind and Structural Health Monitoring System. This paper further describes the analysis of axle loading data, as well as the generation of random axle loads and simulation of vibrations of the bridge using the finite element models. Various factors affecting the vehicular loading on the bridge will also be examined.

• Structural Engineering and Mechanics
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• v.54 no.2
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• pp.257-289
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• 2015

Shear connectors are generally used to link the slab and girders together in slab-on-girder bridge structures. Damage of shear connectors in such structures will result in shear slippage between the slab and girders, which significantly reduces the load-carrying capacity of the bridge. Because shear connectors are buried inside the structure, routine visual inspection is not able to detect conditions of shear connectors. A few methods have been proposed in the literature to detect the condition of shear connectors based on vibration measurements. This paper proposes a different dynamic condition assessment approach to identify the damage of shear connectors in slab-on-girder bridge structures based on power spectral density transmissibility (PSDT). PSDT formulates the relationship between the auto-spectral densities of two responses in the frequency domain. It can be used to identify shear connector conditions with or without reference data of the undamaged structure (or the baseline). Measured impact force and acceleration responses from hammer tests are analyzed to obtain the frequency response functions at sensor locations by experimental modal analysis. PSDT from the slab response to the girder response is derived with the obtained frequency response functions. PSDT vectors in the undamaged and damaged states can be compared to identify the damage of shear connectors. When the baseline is not available, as in most practical cases, PSDT vectors from the measured response at a reference sensor to those of the slab and girder in the damaged state can be used to detect the damage of shear connectors. Numerical and experimental studies on a concrete slab supported by two steel girders are conducted to investigate the accuracy and efficiency of the proposed approach. Identification results demonstrate that damages of shear connectors are identified accurately and efficiently with and without the baseline. The proposed method is also used to evaluate the conditions of shear connectors in a real composite bridge with in-field testing data.

• Smart Structures and Systems
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• v.26 no.1
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• pp.35-47
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• 2020

This paper proposes the use of transmissibility functions combined with a machine learning algorithm, Artificial Neural Networks (ANNs), to assess damage in a truss bridge. A new approach method, which makes use of the input parameters calculated from the transmissibility function, is proposed. The network not only can predict the existence of damage, but also can classify the damage types and identity the location of the damage. Sensors are installed in the truss joints in order to measure the bridge vibration responses under train and ambient excitations. A finite element (FE) model is constructed for the bridge and updated using FE software and experimental data. Both single damage and multiple damage cases are simulated in the bridge model with different scenarios. In each scenario, the vibration responses at the considered nodes are recorded and then used to calculate the transmissibility functions. The transmissibility damage indicators are calculated and stored as ANNs inputs. The outputs of the ANNs are the damage type, location and severity. Two machine learning algorithms are used; one for classifying the type and location of damage, whereas the other for finding the severity of damage. The measurements of the Nam O bridge, a truss railway bridge in Vietnam, is used to illustrate the method. The proposed method not only can distinguish the damage type, but also it can accurately identify damage level.