• Architectural research
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• v.13 no.4
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• pp.53-60
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• 2011

Damage detection algorithms based on a one-dimensional beam model can detect damage within a beam span caused by flexure only but cannot detect damage at a joint with prescribed boundary conditions or at the middle part of a beam section where the neutral axis is located. Considering the damage at a welded joint of beam elements in steel structures and modeling the damage with twodimensional plane elements, this study presents a new approach to detecting damage in the depth direction of the joint and beam section. Three damage scenarios at the upper, middle, and lower parts of a welded joint of a rectangular symmetric section are investigated. The damage is detected by evaluating the difference in the receptance magnitude between the undamaged and damaged states. This study also investigates the effect of measurement locations and noise on the capability of the method in detecting damage. The numerical results show the validity of the proposed method in detecting damage at the beam's welded joint.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.702-703
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• 2015

Currently, according to the climate change, serious damage by Typhoon has been occurred in the world. In this respect, the research on the damage prediction model to minimize the damage from various natural disaster has been conducted in several developed countries. In the case of U.S, various damage prediction models of buildings from natural disasters have been used widely in many organizations such as insurance companies and governments. In South Korea, although studies regarding damage prediction model of hurricane have been conducted, the scope has been only limited to consider the property of hurricane. However, it is necessary to consider various factors such as socio-economic, physical, geographical, and built environmental factors to predict the damages. Therefore, to address this issue, correlation analysis is conducted between various variables based on the data of hurricane from 2003 to 2012. The findings of this study can be utilized to develop for predicting the damage of hurricane on buildings.

• Structural Monitoring and Maintenance
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• v.1 no.2
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• pp.159-171
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• 2014

Real-time near and far-field monitoring of concrete structural components gives enough information on the time and condition at which damage occurs, thereby facilitating damage detection while in the same time evaluate the cause of the damage. This paper experimentally investigates an integrated monitoring technique for near and far-field damage detection in concrete structures based on simultaneous use of electromechanical admittance technique in combination with guided wave propagation. The proposed sensing system does not measure the electromechanical admittance itself but detect time variations in output voltages of the response signal obtained across the electrodes of piezoelectric transducers bonded on surfaces of concrete structures. The damage identification is based on the spectral estimation MUSIC algorithm. Experimental results show the efficiency and performance of the proposed measuring technique.

• Korean Institute of Interior Design Journal
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• v.25 no.3
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• pp.82-89
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• 2016

This study investigated the preservation status of wooden architectural heritages, designated as Chungcheongbuk-do designated heritage in Jecheon. The purpose of this paper is to find the damage cause and the preservation method of wooden architectural heritages. It was conducted using the research methods of the existing literature and field survey to compare a current status of wooden architectural heritages. The result are as followings. Firstly, it was found the problems of a break, a damage, etc. in the platform and the problems of a cracking, an exfoliation, a corrosion, a warp, etc. in the wall. And, it was found a cracking of the beam(梁) and a roof tile(瓦), a falling of Angto(仰土), a sagging roof, etc. in the roof. Secondly, the damage causes of wooden architectural heritages were mostly caused by the physical limitations of the materials. And, it was caused by a rainwater, a subsidence of ground settlement, a destruction of waterproof membrane, etc. Lastly, the repair methods are making a quicklime layer in the soil mound on a cracking and an exfoliation part, a resin treatment or strut operation on a corrosion part of column, etc.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.34 no.2
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• pp.3-10
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• 2018

Damage location and extent of structure could be detected by the inverse analysis on dynamic response properties such as frequencies and mode shapes. In practice the measured difference of natural frequencies represent the stiffness change reliably, however the measured mode shape is insensitive for stiffness change, but provides spatial information of damage. The damage detection index on shear building structures is formulated in this study. The damage detection index could be estimated from mode shape and srory stiffness of undamaged structure and frequency difference between undamaged and damaged structure. For the verification of the observed damage detection method, the numerical analysis of Matlab and MIDAS and shacking table test were performed. In results, the damage index of damaged story was estimated so higher than undamaged stories that indicates the damaged story apparently.

• Journal of Korean Association for Spatial Structures
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• v.18 no.2
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• pp.87-97
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• 2018

Stone pagoda structures have continued to be aged due to the combination of various damage factors. However, some studies on nonstructural damage have been carried out, but assessment studies on structural damage have not been done in various ways. Therefore, in this study, structural and nonstructural influencing factors according to the damage types are classified and the damage assessment according to the structural influencing factors affecting the behavior of the stone pagoda structure is performed. In addition, the damage rating classification criteria for each type of structural damages or damage locations are presented, and the damage index is calculated by providing the criteria for the classification of damage according to the degree of damage to which the damage is caused. Therefore, this study can evaluate quantitatively the damage status of stone pagoda structures.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.35 no.3
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• pp.3-10
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• 2019

On November 15, 2017, an earthquake($M_L5.4$) occurred in Pohang. Pohang earthquake was the second largest earthquake since earthquake was observed in Korea, but structural damage caused by earthquake was biggest. Structural damage caused by Pohang earthquake was mainly caused by schools and pilotis, above all damage to pilotis was outstanding. This is because area where pilotis structures are concentrated is located near epicenter, and seismic performance of pilotis structures is not excellent compared with general structures. In this study, described results of damage investigation and analysis of damage causes through analysis of pilotis Structures on 131 buildings that were investigated immediately after Pohang earthquake. In addition, cause of damage was analyzed through analysis of seismic wave. Investigation site was selected to Jangseong-dong, where damage occurred in large numbers. Damage level was classified into A, B, and C level by measuring residual crack width and story drift of structural members.

• Architectural research
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• v.10 no.2
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• pp.21-26
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• 2008

The number of measured degrees of freedom for detecting the damage of any structures is usually less than the number of model degrees of freedom. It is necessary to expand the measured data to full set of model degrees of freedom for updating modal data. This study presents the expansion methods to estimate all static displacements and dynamic modal data of finite element model from the measured data. The static and dynamic methods are derived by minimizing the variation of the potential energy and the Gauss's function, respectively. The applications illustrate the validity of the proposed methods. It is observed that the numerical results obtained by the static approach correspond with the Guyan condensation method and the derived static and dynamic approaches provide the fundamental idea for damage detection.

• Journal of the Korean Institute of Rural Architecture
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• v.15 no.1
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• pp.29-36
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• 2013

This study is to diagnose causes of damage and defects on external walls of brick structures, focused on "Naju Noahn" Catholic Church of Modern Architectural Properties. The causes of crack and deflection are overloading, shortage strength of arch. Among those, main cause is cauesd by shortage strength of arch because center of arch is dislocated and skew back of arch is small angle. The causes of damage and elimination are weathering, plants of friction, freezing and thawing, durability decrement of material and attach defection. This defects and damage is caused by high-moisture that occurs in soil. The causes of discoloration are change of soil moisture and flimsy brickwork. These defects and damage are mainly occurred in coping of cornice, weathering of window sill.

• Journal of Korean Association for Spatial Structures
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• v.19 no.1
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• pp.93-100
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• 2019

There has been considerable recent interest in deep learning techniques for structural analysis and design. However, despite newer algorithms and more precise methods have been developed in the field of computer science, the recent effective deep learning techniques have not been applied to the damage detection topics. In this study, we have explored the structural damage detection method of truss structures using the state-of-the-art deep learning techniques. The deep neural networks are used to train knowledge of the patterns in the response of the undamaged and the damaged structures. A 31-bar planar truss are considered to show the capabilities of the deep learning techniques for identifying the single or multiple-structural damage. The frequency responses and the elasticity moduli of individual elements are used as input and output datasets, respectively. In all considered cases, the neural network can assess damage conditions with very good accuracy.