• Smart Structures and Systems
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• 제16권2호
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• pp.243-262
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• 2015

Optimal sensor placement (OSP) is a critical issue in construction and implementation of a sophisticated structural health monitoring (SHM) system. The uncertainties in the identified structural parameters based on the measured data may dramatically reduce the reliability of the condition evaluation results. In this paper, the information entropy, which provides an uncertainty metric for the identified structural parameters, is adopted as the performance measure for a sensor configuration, and the OSP problem is formulated as the multi-objective optimization problem of extracting the Pareto optimal sensor configurations that simultaneously minimize the appropriately defined information entropy indices. The nondirective movement glowworm swarm optimization (NMGSO) algorithm (based on the basic glowworm swarm optimization (GSO) algorithm) is proposed for identifying the effective Pareto optimal sensor configurations. The one-dimensional binary coding system is introduced to code the glowworms instead of the real vector coding method. The Hamming distance is employed to describe the divergence of different glowworms. The luciferin level of the glowworm is defined as a function of the rank value (RV) and the crowding distance (CD), which are deduced by non-dominated sorting. In addition, nondirective movement is developed to relocate the glowworms. A numerical simulation of a long-span suspension bridge is performed to demonstrate the effectiveness of the NMGSO algorithm. The results indicate that the NMGSO algorithm is capable of capturing the Pareto optimal sensor configurations with high accuracy and efficiency.

• Smart Structures and Systems
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• 제15권4호
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• pp.997-1018
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• 2015

Structural health monitoring along with damage detection and assessment of its severity level in non-accessible reinforced concrete members using piezoelectric materials becomes essential since engineers often face the problem of detecting hidden damage. In this study, the potential of the detection of flexural damage state in the lower part of the mid-span area of a simply supported reinforced concrete beam using piezoelectric sensors is analytically investigated. Two common severity levels of flexural damage are examined: (i) cracking of concrete that extends from the external lower fiber of concrete up to the steel reinforcement and (ii) yielding of reinforcing bars that occurs for higher levels of bending moment and after the flexural cracking. The purpose of this investigation is to apply finite element modeling using admittance based signature data to analyze its accuracy and to check the potential use of this technique to monitor structural damage in real-time. It has been indicated that damage detection capability greatly depends on the frequency selection rather than on the level of the harmonic excitation loading. This way, the excitation loading sequence can have a level low enough that the technique may be considered as applicable and effective for real structures. Further, it is concluded that the closest applied piezoelectric sensor to the flexural damage demonstrates higher overall sensitivity to structural damage in the entire frequency band for both damage states with respect to the other used sensors. However, the observed sensitivity of the other sensors becomes comparatively high in the peak values of the root mean square deviation index.

• Smart Structures and Systems
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• 제31권4호
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• pp.393-407
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• 2023

To assess structural condition in a non-destructive manner, computer vision-based structural health monitoring (SHM) has become a focus. Compared to traditional contact-type sensors, the advantages of computer vision-based measurement systems include lower installation costs and broader measurement areas. In this study, we propose a novel computer vision-based vibration measurement and coarse-to-fine damage assessment method for truss bridges. First, a deep learning model FairMOT is introduced to track the regions of interest (ROIs) that include joints to enhance the automation performance compared with traditional target tracking algorithms. To calculate the displacement of the tracked ROIs accurately, a normalized cross-correlation method is adopted to fine-tune the offset, while the Harris corner matching is utilized to correct the vibration displacement errors caused by the non-parallel between the truss plane and the image plane. Then, based on the advantages of the stochastic damage locating vector (SDLV) and Bayesian inference-based stochastic model updating (BI-SMU), they are combined to achieve the coarse-to-fine localization of the truss bridge's damaged elements. Finally, the severity quantification of the damaged components is performed by the BI-SMU. The experiment results show that the proposed method can accurately recognize the vibration displacement and evaluate the structural damage.

• Smart Structures and Systems
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• 제12권1호
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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.

• Smart Structures and Systems
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• 제29권1호
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• pp.237-250
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• 2022

Structural health monitoring (SHM) plays an important role in ensuring the safety and functionality of critical civil infrastructure. In recent years, numerous researchers have conducted studies to develop computer vision and machine learning techniques for SHM purposes, offering the potential to reduce the laborious nature and improve the effectiveness of field inspections. However, high-quality vision data from various types of damaged structures is relatively difficult to obtain, because of the rare occurrence of damaged structures. The lack of data is particularly acute for fatigue crack in steel bridge girder. As a result, the lack of data for training purposes is one of the main issues that hinders wider application of these powerful techniques for SHM. To address this problem, the use of synthetic data is proposed in this article to augment real-world datasets used for training neural networks that can identify fatigue cracks in steel structures. First, random textures representing the surface of steel structures with fatigue cracks are created and mapped onto a 3D graphics model. Subsequently, this model is used to generate synthetic images for various lighting conditions and camera angles. A fully convolutional network is then trained for two cases: (1) using only real-word data, and (2) using both synthetic and real-word data. By employing synthetic data augmentation in the training process, the crack identification performance of the neural network for the test dataset is seen to improve from 35% to 40% and 49% to 62% for intersection over union (IoU) and precision, respectively, demonstrating the efficacy of the proposed approach.

• 제어로봇시스템학회논문지
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• 제17권10호
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• pp.989-994
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• 2011

This study aims to demonstrate the feasibility of a visual servoing-based paired structured light (SL) robot for estimating structural displacement under various external loads. The former paired SL robot, which was proposed in the previous study, was composed of two screens facing with each other, each with one or two lasers and a camera. It was found that the paired SL robot could estimate the translational and rotational displacement each in 3-DOF with high accuracy and low cost. However, the measurable range is fairly limited due to the limited screen size. In this paper, therefore, a visual servoing-based 2-DOF manipulator which controls the pose of lasers is introduced. By controlling the positions of the projected laser points to be on the screen, the proposed robot can estimate the displacement regardless of the screen size. We performed various simulations and experimental tests to verify the performance of the newly proposed robot. The results show that the proposed system overcomes the range limitation of the former system and it can be utilized to accurately estimate the structural displacement.

• Smart Structures and Systems
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• 제31권4호
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• pp.383-392
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• 2023

Post-earthquake building condition assessment is crucial for subsequent rescue and remediation and can be automated by emerging computer vision and deep learning technologies. This study is based on an endeavour for the 2nd International Competition of Structural Health Monitoring (IC-SHM 2021). The task package includes five image segmentation objectives - defects (crack/spall/rebar exposure), structural component, and damage state. The structural component and damage state tasks are identified as the priority that can form actionable decisions. A multi-task Convolutional Neural Network (CNN) is proposed to conduct the two major tasks simultaneously. The rest 3 sub-tasks (spall/crack/rebar exposure) were incorporated as auxiliary tasks. By synchronously learning defect information (spall/crack/rebar exposure), the multi-task CNN model outperforms the counterpart single-task models in recognizing structural components and estimating damage states. Particularly, the pixel-level damage state estimation witnesses a mIoU (mean intersection over union) improvement from 0.5855 to 0.6374. For the defect detection tasks, rebar exposure is omitted due to the extremely biased sample distribution. The segmentations of crack and spall are automated by single-task U-Net but with extra efforts to resample the provided data. The segmentation of small objects (spall and crack) benefits from the resampling method, with a substantial IoU increment of nearly 10%.

• Structural Engineering and Mechanics
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• 제37권6호
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• pp.671-684
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• 2011

Deciding on an optimal sensor placement (OSP) is a common problem encountered in many engineering applications and is also a critical issue in the construction and implementation of an effective structural health monitoring (SHM) system. The present study focuses with techniques for selecting optimal sensor locations in a sensor network designed to monitor the health condition of Dalian World Trade Building which is the tallest in the northeast of China. Since the number of degree-of-freedom (DOF) of the building structure is too large, multi-modes should be selected to describe the dynamic behavior of a structural system with sufficient accuracy to allow its health state to be determined effectively. However, it's difficult to accurately distinguish the translational and rotational modes for the flexible structures with closely spaced modes by the modal participation mass ratios. In this paper, a new method of the OSP that computing the mode shape matrix in the weak axis of structure by the simplified multi-DOF system was presented based on the equivalent rigidity parameter identification method. The initial sensor assignment was obtained by the QR-factorization of the structural mode shape matrix. Taking the maximum off-diagonal element of the modal assurance criterion (MAC) matrix as a target function, one more sensor was added each time until the maximum off-diagonal element of the MAC reaches the threshold. Considering the economic factors, the final plan of sensor placement was determined. The numerical example demonstrated the feasibility and effectiveness of the proposed scheme.

• 비파괴검사학회지
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• 제35권1호
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• pp.39-45
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• 2015

지능형 센서 기반의 구조 건전성 감시를 통해 안전성을 확보하기 위한 연구는 우주항공을 비롯하여 기계/토목 구조물, 수송 기계 분야로 확대되었다. 특히, 실시간으로 운용되는 구조물은 사고로 인한 재산 및 인명 피해를 예방하기 위해 여러 스마트 센서 기반의 구조 건전성 감시 기술이 요구되는 결과로 이어졌다. 한편, 상용화되어 있는 대부분의 센서는 전자기 기반의 센서로써 전자기 간섭 및 부식과 같은 적용성의 제한과 환경적 요인에 취약할 수 있다. 따라서, 전자기 기반 센서의 단점을 보완하기 위한 신개념 센서로 광섬유 센서가 최근 각광을 받고 있다. 하지만, 광섬유 센서를 이용한 실제 구조물의 감시를 위해서는 고가 장비와 시스템이 요구되어 어려움이 존재한다. 따라서, 본 연구에서는 한 가닥의 광섬유를 이용하여 여러 지점에서 발생할 수 있는 충격을 검출하는 센서 시스템을 제안하였다. 이를 위해, 광섬유 굽힘 손실 현상을 이용하여 같은 충격에 대해 위치별 광 강도의 변화량 차이가 존재하도록 센서부의 모듈을 제작하였다. 그리고, 광 강도 변화에 영향을 미치는 변수들을 이용하여 실험 설계를 하였으며, 충격 위치 검출이 가능함을 실험적으로 검증하였다.

• 정보처리학회논문지:컴퓨터 및 통신 시스템
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• 제12권12호
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• pp.357-362
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• 2023

교량은 중요한 교통 인프라지만 다양한 환경적 요인과 지속적인 교통 부하로 손상 및 균열을 겪게 되며, 이러한 요인들은 교량의 노후화를 가속화시킨다. 현재 건설한 지 오래된 교량이 많아지면서 안전성을 보장하고 노후화를 진단하기 위한 시스템의 필요성이 대두되고 있다. 이미 교량에서는 실시간 또는 주기적으로 교량의 상태를 모니터링하기 위해 구조물 건전도 모니터링(SHM) 기술이 활용되고 있다. 이 기술과 함께 인공지능과 사물인터넷 기술을 활용한 지능형 교량 모니터링 기술 개발이 진행 중이다. 본 논문에서는 노후화된 교량의 유지관리를 위해 고속 푸리에 변환과 차원 축소 알고리즘을 활용한 교량 안전성을 예측 엣지 시스템 기법을 연구한다. 특히, 기존 연구와는 다르게 실제 교량에서 수집된 센서 데이터를 이용하여 데이터셋을 형성하고 교량의 안전성을 확인할 수 있는지 알아본다.