• Structural Monitoring and Maintenance
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• 제4권3호
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• pp.237-253
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• 2017

Among various structural health monitoring technologies, impedance-based damage detection has been recognized as a promising tool for diagnosing critical members of civil structures. Since the piezoelectric transducers used in the impedance-based technique should be bonded to the surface of the structure using bonding layers (e.g., epoxy layer), it is hard to maintain the as-built condition of the bonding layers and to reconfigure the devices if needed. This study presents an experimental investigation by using magnetically attached PZT-interface for the impedance-based damage detection in bolted girder connections. Firstly, the principle of the impedance-based damage detection via the PZT-interface device is outlined. Secondly, a PZT-interface attachment method in which permanent magnets are used to replace the conventional bonding layers is proposed. Finally, the use of the magnetic attraction for the PZT-interface is experimentally evaluated via detecting the bolt-loosening events in a bolted girder connection. Also, the sensitivity of impedance signatures obtained from the PZT-interface is analyzed with regard to the interface's material.

• Structural Monitoring and Maintenance
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• 제1권3호
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• pp.339-356
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• 2014

This study has been motivated to evaluate the practicality of numerical simulation of impedance monitoring for damage detection in steel column connection. In order to achieve the objective, the following approaches are implemented. Firstly, the theory of electro-mechanical (E/M) impedance responses and impedance-based damage monitoring method are outlined. Secondly, the feasibility of numerical simulation of impedance monitoring is verified for several pre-published experimental examples on steel beams, cracked aluminum beams, and aluminum round plates. Undamaged and damaged steel and aluminum beams are simulated to compare to experimental impedance responses. An aluminum round plate with PZT patch in center is simulated to investigate sensitive range of impedance responses. Finally, numerical simulation of the impedance-based damage monitoring is performed for a steel column connection in which connection bolts are damaged. From the numerical simulation test, the applicability of the impedance-based monitoring to the target steel column connection can be evaluated.

• Smart Structures and Systems
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• 제14권2호
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• pp.85-104
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• 2014

Damage detection is a challenging, complex, and at the same time very important research topic in civil engineering. Identifying the location and severity of damage in a structure, as well as the global effects of local damage on the performance of the structure are fundamental elements of damage detection algorithms. Local damage detection is essential for structural health monitoring since local damages can propagate and become detrimental to the functionality of the entire structure. Existing studies present several methods which utilize sensor data, and track global changes in the structure. The challenging issue for these methods is to be sensitive enough in identifYing local damage. Autoregressive models with exogenous terms (ARX) are a popular class of modeling approaches which are the basis for a large group of local damage detection algorithms. This study presents an algorithm, called Influence-based Damage Detection Algorithm (IDDA), which is developed for identification of local damage based on regression of the vibration responses. The formulation of the algorithm and the post-processing statistical framework is presented and its performance is validated through implementation on an experimental beam-column connection which is instrumented by dense-clustered wired and wireless sensor networks. While implementing the algorithm, two different sensor networks with different sensing qualities are utilized and the results are compared. Based on the comparison of the results, the effect of sensor noise on the performance of the proposed algorithm is observed and discussed in this paper.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2006년도 정기 학술대회 논문집
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• pp.225-231
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• 2006

The purpose of this study is to develop a promising hybrid structural health monitoring system for structural joints. For this propose, the combined use of vibration-based techniques and electro-mechanical impedance technique is employed. For the verification of the proposed health monitoring scheme, a series of damage scenarios are designed to simulate various situations at which the connection joints can experience during their service life. The obtained experimental results, modal parameters and electro-magnetic impedance signatures, are carefully analyzed to recognize the connecting states and the target damage locations. From the analysis. it is shown that the proposed hybrid health monitoring system is successful for acquiring global and local damage information on the structural joints.

• Smart Structures and Systems
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• 제12권6호
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• pp.619-640
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• 2013

It is highly desirable to explore efficient algorithms for detecting structural damage of large size structural systems with limited input and output measurements. In this paper, a new structural damage detection algorithm based on substructure approach is proposed for large size structural systems with limited input and output measurements. Inter-connection effect between adjacent substructures is treated as 'additional unknown inputs' to substructures. Extended state vector of each substructure and its unknown excitations are estimated by sequential extended Kalman estimator and least-squares estimation, respectively. It is shown that the 'additional unknown inputs' can be estimated by the algorithm without the measurements on the substructure interface DOFs, which is superior to previous substructural identification approaches. Also, structural parameters and unknown excitation are estimated in a sequential manner, which simplifies the identification problem compared with other existing work. Structural damage can be detected from the degradation of the identified substructural element stiffness values. The performances of the proposed algorithm are demonstrated by several numerical examples and a lab experiment. Measurement noise effect is considered. Both the simulation results and experimental data validate that the proposed algorithm is viable for structural damage detection of large size structural systems with limited input and output measurements.

• Structural Engineering and Mechanics
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• 제77권4호
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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%.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2002년도 가을 학술발표회 논문집
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• pp.453-458
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• 2002

A recently developed Hilbert-Huang transform (HHT) technique is applied to the detection of the damage locations of bridge structures. The HHT may be used to identify the locations of damages which exhibit nonlinear and non-stationary behavior, since the instantaneous frequency characteristics of the measured signal can be analyzed by the HHT. Numerical simulations were conducted on two bridge systems with damages using controlled excitations with sweeping frequency. Nonlinear plastic model using a gap element is employed to model the behavior of the cracked elements in the numerical simulations. The results indicate that the HHT method can reasonably identify the damage locations based on a limited number of acceleration sensors. Experimental study has been 실so carried out on a steel frame to confirm the applicability of the HHT to detect a structural connection with loosened bolts.

• Smart Structures and Systems
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• 제25권4호
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• pp.423-432
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• 2020

In this paper, a novel but practical approach named series/parallel multi-sensing technique was proposed to evaluate the bolt looseness in a bolt group. The smart washers (SWs), which were fabricated by embedding a Lead Zirconate Titanate (PZT) transducer into two flat metal rings, were installed to the bolts group. By series connection of SWs, the impedance signals of different bolts can be obtained through only one sweep. Therefore, once the loosening occurred, the shift of different peak frequencies can be used to locate which bolt has loosened. The proposed multi input single output (MISO) damage detection scheme is very suitable for the structural health monitoring (SHM) of joint with a large number of bolts connection. Another notable contribution of this paper is the proposal of 3-dB bandwidth root mean square deviation (3 dB-RMSD) which can quantitatively evaluate the severity of bolt looseness. Compared with the traditional naked-eye observation method, the equivalent circuit based 3-dB bandwidth can accurately define the calculation range of RMSD. An experiment with three bolted connection specimens that installed the SWs was carried out to validate our proposed approach. Experimental result shows that the proposed 3 dB-RMSD based multi-sensing technique can not only identify the loosened bolt but also monitor the severity of bolt looseness.

• 한국멀티미디어학회논문지
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• 제12권11호
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• pp.1557-1562
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• 2009

단조프레스기의 유압실에서 배관의 연결부위의 손강으로 인한 누유는 인명피해와 기계 파손의 위험성이 있어 이를 조기에 발견하여 예방하는 시스템이 필요하다. 본 논문에서는 원격지에서 회전형 카메라를 이용하여 유압유의 누유여부를 자동 인식하는 시스템을 구현하였다. 구현한 시스템은 라벨링 과정에서 객체영역을 나타내는 경계사각형을 구하고 경계사각형의 높이와 폭에 대한 비율, 누유형상의 원형도를 이용하여, 올바른 누유영역을 추출하였다. 또한 잡음제거와 영상의 이동과 회전에 대한 보정을 전처리 과정으로 수행한다. 실험을 통하여 제안한 시스템이 여러 가지 조명 환경에서도 누유영역을 정확하게 찾아내는 것을 확인하였다.

• Smart Structures and Systems
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• 제24권3호
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• pp.293-301
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• 2019

Bolted joints are commonly used in civil infrastructure and mechanical assembly structures. Monitoring and identifying the connection status of bolts is the frontier problem of structural research. The existing research is mainly on the looseness of a single bolt. This article presents a study of assessing the loosening/tightening health state and identifying the loose bolt by using ultrasonic guided wave in a bolt group joint. A bolt-tightening index was proposed for evaluating the looseness of a bolt connection based on correlation coefficient. The tightening/loosening state of the bolt was simulated by changing the bolt torque. More than 180 different measurement tests for total of six bolts were conducted. The results showed that with the bolt torque increases, value of the proposed bolt-tightening index increases. The proposed bolt-tightening index trend was very well reproduced by an analytical expression using a function of the torque applied with an overall percentage error lower than 5%. The developed damage index based on the proposed bolt-tightening index can also be applied to locate the loosest bolt in a bolt group joint. To verify the effectiveness of the proposed method, a bolt group joint experiment with different positions of bolt looseness was performed. Experimental results show that the proposed approach is effective to detect and locate bolt looseness and has a good prospect of finding applications in real-time structural monitoring.