• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.04a
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• pp.566-568
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• 2008

An E-OPMT(Electronically-controllable OPMT) was developed as an alternative of OPMT which could adjust the direction of the generated guided waves in a plate manually. The key idea of controlling the wave direction electronically is based on a few sets of axisymmetric figure-of-eight coils and the magnet which is located for making static omni-directionally biasing magnetic field over the patch. However, in order to explain wave phenomenon generated by this transducer, a new approach is required because there are various combinations between static biasing magnetic field and dynamic actuating magnetic field on the patch, not similar to OPMT. In this paper, the experiments were performed to understand characteristics of E-OPMT and the new theoretical analysis was set up for explaining the result.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.35 no.8
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• pp.726-734
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• 2007

구조물에 손상이 발생하면 구조물의 강성변화로 구조물의 고유진동수에 변화가 발생하게 된다. 실험을 통해 얻을 수 있는 손상 전 구조물의 고유진동수와 해석적 방법을 사용하여 구하는 고유진동수가 같다고 가정하고 해석적인 방법으로 손상전후 고유진동수비를 구하여 3차원 그래프로 표시하였다. 손상이 한 부위에 존재할 경우 진동실험으로 구한 고유진동수비를 고유진동수비 그래프와 비교하여 손상의 위치, 크기 및 방향을 알 수 있었으나 여러 지점에 손상이 발생할 경우에는 손상을 파악하기 위해 고유진동수비 그래프 외에 주파수 응답함수를 병행하여 사용하였다.

• Steel and Composite Structures
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• v.29 no.6
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• pp.703-716
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• 2018

Rapid detection of damages in civil engineering structures, in order to assess their possible disorders and as a result produce competent decision making, are crucial to ensure their health and ultimately enhance the level of public safety. In traditional intelligent health monitoring methods, the features are manually extracted depending on prior knowledge and diagnostic expertise. Inspired by the idea of unsupervised feature learning that uses artificial intelligence techniques to learn features from raw data, a two-stage learning method is proposed here for intelligent health monitoring of civil engineering structures. In the first stage, $Nystr{\ddot{o}}m$ method is used for automatic feature extraction from structural vibration signals. In the second stage, Moving Kernel Principal Component Analysis (MKPCA) is employed to classify the health conditions based on the extracted features. In this paper, KPCA has been implemented in a new form as Moving KPCA for effectively segmenting large data and for determining the changes, as data are continuously collected. Numerical results revealed that the proposed health monitoring system has a satisfactory performance for detecting the damage scenarios of a three-story frame aluminum structure. Furthermore, the enhanced version of KPCA methods exhibited a significant improvement in sensitivity, accuracy, and effectiveness over conventional methods.

• Structural Engineering and Mechanics
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• v.77 no.4
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• pp.495-508
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• 2021

This study devises a novel approach, namely quadruple 1D convolutional neural network, for detecting connection stiffness reduction in steel truss bridge structure using experimental and numerical modal data. The method is developed based on expertise in two domains: firstly, in Structural Health Monitoring, the mode shapes and its high-order derivatives, including second, third, and fourth derivatives, are accurate indicators in assessing damages. Secondly, in the Machine Learning literature, the deep convolutional neural networks are able to extract relevant features from input data, then perform classification tasks with high accuracy and reduced time complexity. The efficacy and effectiveness of the present method are supported through an extensive case study with the railway Nam O bridge. It delivers highly accurate results in assessing damage localization and damage severity for single as well as multiple damage scenarios. In addition, the robustness of this method is tested with the presence of white noise reflecting unavoidable uncertainties in signal processing and modeling in reality. The proposed approach is able to provide stable results with data corrupted by noise up to 10%.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.12
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• pp.507-516
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• 2019

Costs invested in road maintenance and road development are on the rise. However, due to accidents such as portholes and ground subsidence, the risks to the drivers' safety and the material damage caused by accidents are also increasing. Following this trend, we have developed a system that determines road damage, according to the magnitude of vibration generated without directly intervening the driver when driving. In this paper, we implemented the system using a remote control car (RC car) simulator due to the limitation of the environment in which the actual vehicle is not available in the process of developing the system. In addition, we attached a vibration sensor and GPS sensor to the body of the RC car simulator to measure the vibration value and location information generated by the movement of the vehicle in real-time while driving, and transmitting the corresponding data to the server. In this way, we implemented a system that allows external users to check the damage of roads and the maintenance of the repaired roads based on data more easily than the existing systems. By using this system, we can perform early prediction of road breakage and pattern prediction based on the data. Further, for the RC car simulator, commercialization will be possible by combining it with business in other fields that require flatness.

• Smart Structures and Systems
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• v.8 no.1
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• pp.119-137
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• 2011

A bio-inspired two-mode structural health monitoring (SHM) system based on the Na$\ddot{i}$ve Bayes (NB) classification method is discussed in this paper. To implement the molecular biology based Deoxyribonucleic acid (DNA) array concept in structural health monitoring, which has been demonstrated to be superior in disease detection, two types of array expression data have been proposed for the development of the SHM algorithm. For the micro-vibration mode, a two-tier auto-regression with exogenous (AR-ARX) process is used to extract the expression array from the recorded structural time history while an ARX process is applied for the analysis of the earthquake mode. The health condition of the structure is then determined using the NB classification method. In addition, the union concept in probability is used to improve the accuracy of the system. To verify the performance and reliability of the SHM algorithm, a downscaled eight-storey steel building located at the shaking table of the National Center for Research on Earthquake Engineering (NCREE) was used as the benchmark structure. The structural response from different damage levels and locations was collected and incorporated in the database to aid the structural health monitoring process. Preliminary verification has demonstrated that the structure health condition can be precisely detected by the proposed algorithm. To implement the developed SHM system in a practical application, a SHM prototype consisting of the input sensing module, the transmission module, and the SHM platform was developed. The vibration data were first measured by the deployed sensor, and subsequently the SHM mode corresponding to the desired excitation is chosen automatically to quickly evaluate the health condition of the structure. Test results from the ambient vibration and shaking table test showed that the condition and location of the benchmark structure damage can be successfully detected by the proposed SHM prototype system, and the information is instantaneously transmitted to a remote server to facilitate real-time monitoring. Implementing the bio-inspired two-mode SHM practically has been successfully demonstrated.

• Structural Monitoring and Maintenance
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• v.1 no.3
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• pp.249-271
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• 2014

This paper presents a unique study of Structural Health Monitoring (SHM) for the maintenance decision making about a real life movable bridge. The mechanical components of movable bridges are maintained on a scheduled basis. However, it is desired to have a condition-based maintenance by taking advantage of SHM. The main objective is to track the operation of a gearbox and a rack-pinion/open gear assembly, which are critical parts of bascule type movable bridges. Maintenance needs that may lead to major damage to these components needs to be identified and diagnosed timely since an early detection of faults may help avoid unexpected bridge closures or costly repairs. The fault prediction of the gearbox and rack-pinion/open gear is carried out using two types of Artificial Neural Networks (ANNs): 1) Multi-Layer Perceptron Neural Networks (MLP-NNs) and 2) Fuzzy Neural Networks (FNNs). Monitoring data is collected during regular opening and closing of the bridge as well as during artificially induced reversible damage conditions. Several statistical parameters are extracted from the time-domain vibration signals as characteristic features to be fed to the ANNs for constructing the MLP-NNs and FNNs independently. The required training and testing sets are obtained by processing the acceleration data for both damaged and undamaged condition of the aforementioned mechanical components. The performances of the developed ANNs are first evaluated using unseen test sets. Second, the selected networks are used for long-term condition evaluation of the rack-pinion/open gear of the movable bridge. It is shown that the vibration monitoring data with selected statistical parameters and particular network architectures give successful results to predict the undamaged and damaged condition of the bridge. It is also observed that the MLP-NNs performed better than the FNNs in the presented case. The successful results indicate that ANNs are promising tools for maintenance monitoring of movable bridge components and it is also shown that the ANN results can be employed in simple approach for day-to-day operation and maintenance of movable bridges.

• Steel and Composite Structures
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• v.49 no.1
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• pp.81-89
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• 2023

A simplified calculation method of natural vibration characteristics of high-speed railway multi-span bridge-longitudinal ballastless track system is proposed. The rail, track slab, base slab, main beam, bearing, pier, cap and pile foundation are taken into account, and the multi-span longitudinal ballastless track-beam-bearing-pier-cap-pile foundation integrated model (MBTIM) is established. The energy equation of each component of the MBTIM based on Timoshenko beam theory is constructed. Using the improved Fourier series, and the Rayleigh-Ritz method and Hamilton principle are combined to obtain the extremum of the total energy function. The simplified calculation formula of the natural vibration frequency of the MBTIM under the influence of vertical and longitudinal vibration is derived and verified by numerical methods. The influence law of the natural vibration frequency of the MBTIM is analyzed considering and not considering the participation of each component of the MBTIM, the damage of the track interlayer component and the stiffness change of each layer component. The results show that the error between the calculation results of the formula and the numerical method in this paper is less than 3%, which verifies the correctness of the method in this paper. The high-order frequency of the MBTIM is significantly affected considering the track, bridge pier, pile soil and pile cap, while considering the influence of pile cap on the low-order and high-order frequency of the MBTIM is large. The influence of component damage such as void beneath slab, mortar debonding and fastener failure on each order frequency of the MBTIM is basically the same, and the influence of component damage less than 10m on the first fourteen order frequency of the MBTIM is small. The bending stiffness of track slab and rail has no obvious influence on the natural frequency of the MBTIM, and the bending stiffness of main beam has influence on the natural frequency of the MBTIM. The bending stiffness of pier and base slab only has obvious influence on the high-order frequency of the MBTIM. The natural vibration characteristics of the MBTIM play an important guiding role in the safety analysis of high-speed train running, the damage detection of track-bridge structure and the seismic design of railway bridge.

• Journal of the Korea institute for structural maintenance and inspection
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• v.7 no.4
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• pp.121-128
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• 2003

In recent years, various artificial neural network algorithms are used in the damage assessment of civil infrastructures. So far, many researchers have used the artificial neural network as a pattern classifier for the structural damage assessment but, in this paper, the neural network is used as a structural reanalysis tool not as a pattern classifier. For the model updating using the optimization algorithm, the summation of the absolute differences in the structural vibration modes between undamaged structures and damaged ones is considered as an objective function. The stiffness of structural components are treated as unknown parameters to be determined. The structural damage detection is achieved using model updating based on the optimization techniques which determine the estimated stiffness of components minimizing the objective function. For the verification of the proposed damage identification algorithm, it is numerically applied to a simply supported bridge model.

• 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.