• Transactions of the Korean Society for Noise and Vibration Engineering
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• 제26권6_spc호
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• pp.651-659
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• 2016

Condition monitoring (CM) encounters a large data problem due to sensors that measure vibration data with a continuous, and sometimes, high sampling rate. In this study, compressive sensing approaches for condition monitoring are proposed to demonstrate the efficiency in handling a large amount of data and to improve the damage detection capability of the current condition monitoring process. Compressive sensing is a novel sensing/sampling paradigm that takes much fewer samples compared to traditional sampling methods. For the experiments a built-in rotating system was used and all data were compressively sampled to obtain compressed data. Optimal signal features were then selected without the reconstruction process and were used to detect and classify damage. The experimental results show that the proposed method could improve the data processing speed and the accuracy of condition monitoring of rotating systems.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 한국정밀공학회 2012년도 춘계학술대회 논문집
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• pp.855-856
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• 2012

• Journal of the Korea Academia-Industrial cooperation Society
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• 제19권12호
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• pp.265-271
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• 2018

This paper presents a method for detecting damage to a structure at low cost using its impedance. The impedance technique is a typical method to detect local damage for structural health monitoring. This is a common technique for estimating damage by monitoring the electro-mechanical admittance signal of the structure. To apply this technique, an expensive impedance analyzer is generally used. On the other hand, it is necessary to develop a low-cost variant to effectively disseminate the technique. In this study, a method based on the transfer impedance using a function generator and digital multimeter, which are generally used in the laboratory instead of an impedance analyzer, was developed. That is, this technique estimates the damage by comparing the damage index using the amplitude ratio of the output voltage measured in the healthy and damaged state. A transfer impedance test was carried out on a steel specimen. By comparing the damage index, the presence of damage could be assessed reasonably. This study is a basic investigation of an impedance-based low-cost damage detection method that can be used effectively for structural health monitoring if supplemented with future research to estimate the damage location and severity.

• Smart Structures and Systems
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• 제32권3호
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• pp.153-166
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• 2023

Health monitoring of civil infrastructures, in particular, old bridges that are still in service, has become more than necessary, given the risk that a possible degradation or failure of these infrastructures can induce on the safety of users in addition to the resulting commercial and economic impact. Bridge integrity assessment has attracted significant research efforts over the past forty years with the aim of developing new damage identification methods applicable to real structures. The bridge of Ouled Mimoun (Tlemcen, Algeria) is one of the oldest railway structure in the country. It was built in 1889. This bridge, which is too low with respect to the level of the road, has suffered multiple shocks from various machines that caused considerable damage to its central part. The present work aims to analyze the stability of this bridge by identifying damages and evaluating the damage rate in different parts of the structure on the basis of a finite element model. The applied method is based on an inverse analysis of the normal stress responses that were calculated from the corresponding recorded strains, during the passage of a real train, by means of a set of strain gauges placed on certain elements of the bridge. The results obtained from the inverse analysis made it possible to successfully locate areas that were really damaged and to estimate the damage rate. These results were also used to detect an excessive rigidity in certain elements due to the presence of plates, which were neglected in the numerical reference model. In the case of the continuous bridge monitoring, this developed method will be a very powerful tool as a smart health monitoring system, allowing engineers to take in time decisions in the event of bridge damage.

• Journal of the Computational Structural Engineering Institute of Korea
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• 제21권6호
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• pp.615-632
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• 2008

The bridge health monitoring has become an important research topic in conjunction with damage assessment and safety evaluation of structures owing to the improvement of structural modeling techniques incorporating response measurements and the advancements in signal analysis and information processing capabilities. The bridge monitoring systems are generally composed of hardwares such as sensors, data acquisition equipment, data transmission systems, etc, and softwares such as signal processing, damage assessment, display and management, etc. In this paper, the research and development(R&D) activities on the information processing for structural health monitoring of bridges are reviewed. After a brief introduction to the process of bridge health monitoring, various information processing techniques including various signal processing and damage detection algorithms are introduced in detail. Several challenges addressing critical issues in the current bridge health monitoring system and future R&D activities are discussed.

• 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.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 한국해양공학회 2004년도 학술대회지
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• pp.151-156
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• 2004

최근 국내외에서 국제무역 물량의 증대에 따라 대규모 항만 건설 공사가 진행되고 있으며, 이에 경제성, 시공성이 뛰어난 Caisson 형식의 구조물이 많이 사용되어지고 있다. 특히 항만 및 어항의 외곽시설인 방파제는 계류선박의 안전과 하역 및 적화를 용이하게 하는 중요한 구조물이다. 따라서, 본 연구에서는 Caisson식 방파제에 태풍, 충격력과 같은 몇 가지 외력 조건에 대하여 구조 해석을 실시하여 손상메커니즘을 분석하였다 이러한 손상 메커니즘에 따라 손상을 인위적으로 발생시켜 손상 위치 탐색을 수행하였다.

• 한국방재학회:학술대회논문집
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• 한국방재학회 2008년도 정기총회 및 학술발표대회
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• pp.481-484
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• 2008

The research was carried out to suggest the bridge health monitoring systems that have been composed damage detection algorithm and a system for evaluation load carrying capacity of bridge by traffic loads for the purpose of safety management of bridge structure in efficient and economic.

• Structural Monitoring and Maintenance
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• 제5권2호
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• pp.273-295
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• 2018

In this study, an investigation of a damage detection methodology for global condition assessment is presented. A particular emphasis is put on the utilization of wireless sensors for more practical, less time consuming, less expensive and safer monitoring and eventually maintenance purposes. Wireless sensors are deployed with a sensor roving technique to maintain a dense sensor field yet requiring fewer sensors. The time series analysis method called ARX models (Auto-Regressive models with eXogeneous input) for different sensor clusters is implemented for the exploration of artificially induced damage and their locations. The performance of the technique is verified by making use of the data sets acquired from a 4-span bridge-type steel structure in a controlled laboratory environment. In that, the free response vibration data of the structure for a specific sensor cluster is measured by both wired and wireless sensors and the acceleration output of each sensor is used as an input to ARX model to estimate the response of the reference channel of that cluster. Using both data types, the ARX based time series analysis method is shown to be effective for damage detection and localization along with the interpretations and conclusions.

• Structural Monitoring and Maintenance
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• 제4권1호
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• pp.1-15
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• 2017

Installation of sensors networks for continuous in-service monitoring of structures and their efficiency conditions is a current research trend of paramount interest. On-line monitoring systems could be strategically useful for road infrastructures, which are expected to perform efficiently and be self-diagnostic, also in emergency scenarios. This work researches damage detection in composite concrete-steel structures that are typical for highway overpasses and bridges. The techniques herein proposed assume that typical damage in the deck occurs in form of delamination and cracking, and that it affects the peak power spectral density of dynamic curvature. The investigation is performed by combining results of measurements collected by long-gauge fiber optic strain sensors installed on monitored structure and a statistic approach. A finite element model has been also prepared and validated for deepening peculiar aspects of the investigation and the availability of the method. The proposed method for real time applications is able to detect a documented unusual behavior (e.g., damage or deterioration) through long-gauge fiber optic strain sensors measurements and a probabilistic study of the dynamic curvature power spectral density.