• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2002.09a
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• pp.173-180
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• 2002

This paper presents a signal processing procedure to detect damage locations of frame structures by using continuous wavelet transform. Morlet wavelet is used as a mother wavelet in wavelet transform. Wavelet transform has the characteristics that allows the use of long time intervals at more precise low-frequency information, and shorter regions at high-frequency information. By this wavelet transform characteristics, Morlet wavelet may be used to identify the locations of damages in the structures. The numerical case studies show that this method can be applied to detect the damage location under a controlled sweeping load.

• Wind and Structures
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• v.21 no.6
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• pp.657-675
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• 2015

In this study, the feasibility of vibration-based damage detection methods for the wind turbine tower (WTT) structure is evaluated. First, a frequency-based damage detection (FBDD) is outlined. A damage-localization algorithm is visited to locate damage from changes in natural frequencies. Second, a mode-shape-based damage detection (MBDD) method is outlined. A damage index algorithm is utilized to localize damage from estimating changes in modal strain energies. Third, a finite element (FE) model based on a real WTT is established by using commercial software, Midas FEA. Several damage scenarios are numerically simulated in the FE model of the WTT. Finally, both FBDD and MBDD methods are employed to identify the damage scenarios simulated in the WTT. Damage regions are chosen close to the bolt connection of WTT segments; from there, the stiffness of damage elements are reduced.

• Smart Structures and Systems
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• v.4 no.3
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• pp.343-365
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• 2008

In this study, the frequency domain method which utilizes the evaluation of changes in the structural mode shape is adopted to identify regions which contain localized damages. Frequency response function (FRF) values corresponding to the modal frequency, analogous to the mode shape coefficients, are used since change in natural frequency of the system is usually insignificant for localized damage. This method requires only few sensors to obtain the dynamic response of the structure at specific locations to determine the FRF via fast-Fourier transform (FFT). Numerical examples of an aluminum plate, which includes damages of varying severity, locations and combinations of multiple locations, are presented to demonstrate the feasibility of the method. An experimental verification of the method is also done using an aluminum plate with two different degrees of damage, namely a half-through notch and a through notch. The inconsistency in attaining the FRF values for practical applications due to varying impact load may be overcome via statistical averaging, although large variations in the loading in terms of the contact duration should still be avoided. Nonetheless, this method needs special attention when the damages induce notable changes in the modal frequency, such as when the damages are of high severity or cover more extensive area or near the boundary where the support condition is modified. This is largely due to the significant decrease in the frequency term compared to the increase in the vibration amplitude. For practical reasons such as the use of limited number of sensors and to facilitate automation, extending the resolution of this method of identification may not be efficient. Hence, methods based on wave propagation can be employed as a complement on the isolated region to provide an accurate localization as well as to trace the geometry of the damage.

• Smart Structures and Systems
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• v.31 no.4
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• pp.325-334
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• 2023

Computer vision-based damage detection enables non-contact, efficient and low-cost bridge health monitoring, which reduces the need for labor-intensive manual inspection or that for a large number of on-site sensing instruments. By leveraging recent semantic segmentation approaches, we can detect regions of critical structural components and identify damages at pixel level on images. However, existing methods perform poorly when detecting small and thin damages (e.g., cracks); the problem is exacerbated by imbalanced samples. To this end, we incorporate domain knowledge to introduce a hierarchical semantic segmentation framework that imposes a hierarchical semantic relationship between component categories and damage types. For instance, certain types of concrete cracks are only present on bridge columns, and therefore the noncolumn region may be masked out when detecting such damages. In this way, the damage detection model focuses on extracting features from relevant structural components and avoid those from irrelevant regions. We also utilize multi-scale augmentation to preserve contextual information of each image, without losing the ability to handle small and/or thin damages. In addition, our framework employs an importance sampling, where images with rare components are sampled more often, to address sample imbalance. We evaluated our framework on a public synthetic dataset that consists of 2,000 railway bridges. Our framework achieves a 0.836 mean intersection over union (IoU) for structural component segmentation and a 0.483 mean IoU for damage segmentation. Our results have in total 5% and 18% improvements for the structural component segmentation and damage segmentation tasks, respectively, compared to the best-performing baseline model.

• Smart Structures and Systems
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• v.21 no.4
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• pp.435-448
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• 2018

This paper reports the development of a theoretically rigorous method for permanent way engineers to assess the condition of railway ballast under a concrete sleeper with the potential to be extended to a smart system for long-term health monitoring of railway ballast. Owing to the uncertainties induced by the problems of modeling error and measurement noise, the Bayesian approach was followed in the development. After the selection of the most plausible model class for describing the damage status of the rail-sleeper-ballast system, Bayesian model updating is adopted to calculate the posterior PDF of the ballast stiffness at various regions under the sleeper. An obvious drop in ballast stiffness at a region under the sleeper is an evidence of ballast damage. In model updating, the model that can minimize the discrepancy between the measured and model-predicted modal parameters can be considered as the most probable model for calculating the posterior PDF under the Bayesian framework. To address the problems of non-uniqueness and local minima in the model updating process, a two-stage hybrid optimization method was developed. The modified evolutionary algorithm was developed in the first stage to identify the important regions in the parameter space and resulting in a set of initial trials for deterministic optimization to locate all most probable models in the second stage. The proposed methodology was numerically and experimentally verified. Using the identified model, a series of comprehensive numerical case studies was carried out to investigate the effects of data quantity and quality on the results of ballast damage detection. Difficulties to be overcome before the proposed method can be extended to a long-term ballast monitoring system are discussed in the conclusion.

• Smart Structures and Systems
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• v.4 no.5
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• pp.605-628
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• 2008

In bridge structures, damage may induce an additional deflection which may naturally contain essential information about the damage. However, inverse mapping from the damage-induced deflection to the actual damage location and severity is generally complex, particularly for statically indeterminate systems. In this paper, a new load concept, called the positive-bending-inspection-load (PBIL) is proposed to construct a simple inverse mapping from the damage-induced deflection to the actual damage location. A PBIL for an inspection region is defined as a load or a system of loads which guarantees the bending moment to be positive in the inspection region. From the theoretical investigations, it was proven that the damage-induced chord-wise deflection (DI-CD) has the maximum value with the abrupt change in its slope at the damage location under a PBIL. Hence, a novel damage localization method is proposed based on the DI-CD under a PBIL. The procedure may be summarized as: (1) identification of the modal flexibility matrices from acceleration measurements, (2) design for a PBIL for an inspection region of interest in a structure, (3) calculation of the chord-wise deflections for the PBIL using the modal flexibility matrices, and (4) damage localization by finding the location with the maximum DI-CD with the abrupt change in its slope within the inspection region. Procedures from (2)-(4) can be repeated for several inspection regions to cover the whole structure complementarily. Numerical verification studies were carried out on a simply supported beam and a three-span continuous beam model. Experimental verification study was also carried out on a two-span continuous beam structure with a steel box-girder. It was found that the proposed method can identify the damage existence and damage location for small damage cases with narrow cuts at the bottom flange.

• Smart Structures and Systems
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• v.6 no.5_6
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• pp.561-578
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• 2010

This study proposes a multi-level damage localization strategy to achieve an effective damage detection system for civil infrastructure systems based on wireless sensors. The proposed system is designed for use of distributed computation in a wireless sensor network (WSN). Modal identification is achieved using the frequency-domain decomposition (FDD) method and the peak-picking technique. The ASH (angle-between-string-and-horizon) and AS (axial strain) flexibility-based methods are employed for identifying and localizing damage. Fundamentally, the multi-level damage localization strategy does not activate all of the sensor nodes in the network at once. Instead, relatively few sensors are used to perform coarse-grained damage localization; if damage is detected, only those sensors in the potentially damaged regions are incrementally added to the network to perform finer-grained damage localization. In this way, many nodes are able to remain asleep for part or all of the multi-level interrogations, and thus the total energy cost is reduced considerably. In addition, a novel distributed computing strategy is also proposed to reduce the energy consumed in a sensor node, which distributes modal identification and damage detection tasks across a WSN and only allows small amount of useful intermediate results to be transmitted wirelessly. Computations are first performed on each leaf node independently, and the aggregated information is transmitted to one cluster head in each cluster. A second stage of computations are performed on each cluster head, and the identified operational deflection shapes and natural frequencies are transmitted to the base station of the WSN. The damage indicators are extracted at the base station. The proposed strategy yields a WSN-based SHM system which can effectively and automatically identify and localize damage, and is efficient in energy usage. The proposed strategy is validated using two illustrative numerical simulations and experimental validation is performed using a cantilevered beam.

• The Journal of the Korean dental association
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• v.58 no.10
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• pp.627-638
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• 2020

This study was conducted to identify the subjective damage caused by COVID-19 and its related factors. The study subjects were members of the Korean Dental Association (KDA). We investigated the damage to dental clinics and hospitals caused by COVID-19 between January and April 2020. After analyzing the final 3,189 responses, the rate of decrease in patients was the highest at 34.9% in March, and the rate of decrease in income was the highest at 34.0% in April. As a result of the multilevel analysis, the damage caused by COVID-19 was greater in regions with more confirmed patients, more careers, and fewer dental staff. The government should establish a compensation plan for hospitals and clinics to prevent the collapse of the medical system due to the prolonged COVID-19. In addition, support for dentistry should be provided to maintain the oral health care system in the future.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.1
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• pp.89-113
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• 2023

This study aimed to identify heat wave vulnerable areas and discuss spatial typology and policy directions through spatial coincidence analysis of heat wave damage. By utilizing the climate change vulnerability assessment of the Intergovernmental Panel on Climate Change (IPCC) and Spatial Statistics Comparison Analysis, this study examined cities, counties, and districts in South Korea for five years (2015-2019), including 2018, when the heat wave was most extreme. It was determined that the number of heat wave days (exposure) was the most impactful among various factors for heat wave vulnerability. Sensitivity and adaptive capacity to heat waves were found to vary according to regional characteristics. The relationship between heat wave vulnerability and damage was categorized into four types through spatial coherence. Hot to Hot and Cold to Cold types have a positive relationship between vulnerability and damage, while Hot to Cold and Cold to Hot types have a negative relationship. The findings suggest that since different types of regions have distinct characteristics and conditions, policies and research for improvement should be directed to address each region separately. This study may be used as basic data for establishing heat-related policies in the future, as it categorizes regions by considering both heat vulnerability and damage and examines the direction of response by type.

• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1531-1538
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• 2014

The causes of cancer are diverse, complex, and only partially understood. Many factors including health behaviors, socioeconomic environments and geographical locations can directly damage genes or combine with existing genetic faults within cells to cause cancerous mutations. Collecting the cancer data and reporting the statistics, therefore, are important to help identify health trends and establish normal health changes in geographical areas. In this article, we analyzed cancer data and demon-strated how spatial patterns of the age-standardized rate and health indicators can be examined visually and simultaneously using linked micromap plots. As a result of data analysis, the age-standardized rate has positive correlativity with thyroid and breast cancer, but the rate has negative correlativity with smoking and drinking. In addition, the regions with high age-standardized rate are located in southwest and the areas of high population density while the standardized mortality ratio is higher in southwest and northeast where there are lots of rural areas.