• Smart Structures and Systems
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• v.23 no.2
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• pp.107-122
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• 2019

As one of the most important parameters in structural health monitoring, structural frequency has many advantages, such as convenient to be measured, high precision, and insensitive to noise. In addition, frequency-change-ratio based method had been validated to have the ability to identify the damage occurrence and location. However, building a precise enough finite elemental model (FEM) for the test structure is still a huge challenge for this frequency-change-ratio based damage detection technique. In order to overcome this disadvantage and extend the application for frequencies in structural health monitoring area, a novel method was developed in this paper by combining the cross-model cross-mode (CMCM) model updating algorithm with the frequency-change-ratio based method. At first, assuming the physical parameters, including the element mass and stiffness, of the test structure had been known with a certain value, then an initial to-be-updated model with these assumed parameters was constructed according to the typical mass and stiffness distribution characteristic of shear buildings. After that, this to-be-updated model was updated using CMCM algorithm by combining with the measured frequencies of the actual structure when no damage was introduced. Thus, this updated model was regarded as a representation of the FEM model of actual structure, because their modal information were almost the same. Finally, based on this updated model, the frequency-change-ratio based method can be further proceed to realize the damage detection and localization. In order to verify the effectiveness of the developed method, a four-level shear building was numerically simulated and two actual shear structures, including a three-level shear model and an eight-story frame, were experimentally test in laboratory, and all the test results demonstrate that the developed method can identify the structural damage occurrence and location effectively, even only very limited modal frequencies of the test structure were provided.

• Structural Monitoring and Maintenance
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• v.1 no.1
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• pp.89-110
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• 2014

Structural Health Monitoring (SHM) is an effective alternative to conventional inspections which are time-consuming and subjective. SHM can detect damage early and reduce maintenance cost and thereby help reduce the likelihood of catastrophic structural events to infrastructure such as bridges. After reviewing the Damage Index Method (DIM), an Iterative Damage Index Method (IDIM) is proposed to improve the accuracy of damage detection. These two damage detection techniques are compared based on damage on two structures, a simply supported beam and a pedestrian bridge. Compared to the traditional damage detection algorithm, the proposed IDIM is shown to be less arbitrary and more accurate.

• Proceedings of the KSRS Conference
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• v.2
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• pp.698-701
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• 2006

Remotely sensed data have been utilized efficiently for damage detection immediately after the natural disaster since they provide valuable information on land cover change due to spatial synchronization and multitemporal observation over large areas. Damage information obtained at an early stage is important for rapid emergency response and recovery works. Many useful techniques to analyze the characteristics of the pre- and post-event satellite images in large-scale damage detection have been successfully investigated for emergency management. Since high-resolution satellite images provide a wealth of information on damage occurred in urban areas, they are successfully utilized for damage detection in urban areas. In this research, a method to perform automated damage detection is proposed based on the differences of the textural characteristics in pre- and post- high resolution satellite images.

• Journal of the Korea Institute of Building Construction
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• v.24 no.2
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• pp.193-202
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• 2024

This research delves into the evaluation of the suitability of ultrasonic pulse velocity as a diagnostic tool for early detection of frost damage in concrete. The investigation involves the measurement of compressive strength and ultrasonic pulse velocity concerning the depth of freezing for individual mortar specimens, followed by an analysis of their microstructure and their interrelation. The findings indicate a consistent decrease in both compressive strength and ultrasonic pulse velocity with increasing freezing depth. Furthermore, a correlation between compressive strength and ultrasonic pulse velocity concerning the depth of early frost damage is established. Consequently, the study asserts the potential of utilizing the ultrasonic pulse velocity method for early detection of frost damage in concrete, with prospects for quantifying the depth of damage through further research endeavors.

• Journal of the Korean association of regional geographers
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• v.14 no.1
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• pp.84-92
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• 2008

The first pine wilt damage in Korea, which called AIDS of pine, was found out at Mt. Geumjeong of Pusan province in 1988. The damage area spread 53's city, Gun, Gu throughout the Gyeongsangnamdo in December 2005 since then find out. The best treatment for these damaged forests is well known as fumigation method after early detection. But early detection by an observer is very difficult because of the damaged forest areas are spread over huge range. Also the access of observer is difficult in condition of Korea topographical characteristic. In this study, an attempt was done to investigation about early detection of pine wilt damage using near infrared CCD camera.

• Smart Structures and Systems
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• v.31 no.1
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• pp.13-27
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• 2023

Fatigue crack is a fatal problem for steel structures. Early detection and maintenance can help extend the service life and prevent hazards. This paper presents the ultrasonic guided waves-based (UGWs-based) fatigue crack detection of a steel I-beam. The semi-analytical finite element model has been built to obtain the wave propagation characteristics. Damage indices in both time and frequency domains were analyzed by considering the characteristic variations of UGWs including the amplitude, phase angle, and wave packet energy. The pulse-echo and pitch-catch methods were combined in the detection scheme. Lab-scale experiments were conducted on welded steel I-beams to verify the proposed method. Results show that the damage indices based on the characteristic variations in the time domain can identify and localize the fatigue crack before it enters the rapid growth stage. The damage severity can be reasonably evaluated by analyzing the time-domain damage indices. Two nonlinear damage indices in the frequency domain give earlier warnings of the fatigue crack than the time-domain damage indices do. The identification results based on the above two nonlinear indices are found to be less consistent under various excitation frequencies. More robust nonlinear techniques needed to be searched and tested for early crack detection in steel I-beams in further study.

• Journal of the Korean Society for Nondestructive Testing
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• v.32 no.3
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• pp.291-295
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• 2012

In the industrial field, real-time monitoring system like a fault early detection is very important. For this, the infrared thermography technique as a new diagnosis method is proposed. This study is focused on the damage detection and temperature characteristic analysis of ball bearing using the non-destructive infrared thermography method. In this paper, thermal image and temperature data were measured by a Cedip Silver 450 M infrared camera. Based on the results, the temperature characteristics under the conditions of normal, loss lubrication, damage, dynamic loading, and damage under loading were analyzed. It was confirmed that the infrared technique is very useful for the detection of the bearing damage.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2008.04a
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• pp.135-138
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• 2008

Many Victims and property damage are caused in fires. In this paper, an flame detection algorithm is proposed to early alarm fires. The proposed flame detection algorithm is based on 2-stage decision strategy of video processing. The first decision is to check with color distribution of input vidoe. In the second, the candidated region is settled as fire region with activity. As a result of simulation, it is shown that the proposed algorithm is useful for fire recognition.

• Smart Structures and Systems
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• v.14 no.3
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• pp.419-444
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• 2014

Within this paper a new approach for early damage detection in rotor blades of wind energy converters is presented, which is shown to have a more sensitive reaction to damage than eigenfrequency-based methods. The new approach is based on the extension of Gasch's proportionality method, according to which maximum oscillation velocity and maximum stress are proportional by a factor, which describes the dynamic behavior of the structure. A change in the proportionality factor can be used as damage indicator. In addition, a novel deflection sensor was developed, which was specifically designed for use in wind turbine rotor blades. This deflection sensor was used during the experimental tests conducted for the measurement of the blade deflection. The method was applied on numerical models for different damage cases and damage extents. Additionally, the method and the sensing concept were applied on a real 50.8 m blade during a fatigue test in the edgewise direction. During the test, a damage of 1.5 m length was induced on the upper trailing edge bondline. Both the initial damage and the increase of its length were successfully detected by the decrease of the proportionality factor. This decrease coincided significantly with the decrease of the factor calculated from the numerical analyses.

• Smart Structures and Systems
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• v.29 no.1
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• pp.105-116
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• 2022

Stay cables play an essential role in cable-stayed bridges. Severe vibrations and/or harsh environment may result in cable failures. Therefore, an efficient structural health monitoring (SHM) solution for cable damage detection is necessary. This study proposes a data-driven method for immediately detecting cable damage from measured cable forces by recognizing pattern transition from the intact condition when damage occurs. In the proposed method, pattern recognition for cable damage detection is realized by time series classification (TSC) using a deep learning (DL) model, namely, the long short term memory fully convolutional network (LSTM-FCN). First, a TSC classifier is trained and validated using the cable forces (or cable force ratios) collected from intact stay cables, setting the segmented data series as input and the cable (or cable pair) ID as class labels. Subsequently, the classifier is tested using the data collected under possible damaged conditions. Finally, the cable or cable pair corresponding to the least classification accuracy is recommended as the most probable damaged cable or cable pair. A case study using measured cable forces from an in-service cable-stayed bridge shows that the cable with damage can be correctly identified using the proposed DL-TSC method. Compared with existing cable damage detection methods in the literature, the DL-TSC method requires minor data preprocessing and feature engineering and thus enables fast and convenient early detection in real applications.