• Smart Structures and Systems
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• v.20 no.6
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• pp.759-767
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• 2017

Wear and aging associated damage is a severe problem for safety and maintenance of engineering structures. To acquire structural operational state and provide warning about different types of damage, research on damage identification has gained increasing popularity in recent years. Among various damage identification methods, the Lamb wave-based methods have shown promising suitability and potential for damage identification of plate-type structures. In this paper, a comprehensive study was presented to elaborate four remarkable aspects regarding the Lamb wave-based damage identification method for plate-type structures, including wave velocity, signal denoising, image reconstruction, and sensor layout. Conclusions and path forward were summarized and classified serving as a starting point for research and application in this area.

• Smart Structures and Systems
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• v.30 no.3
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• pp.263-271
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• 2022

In this paper we present an overview of damage diagnosis algorithms that have been developed over the past two decades using vibration signals obtained from structures. Then, the paper focuses primarily on algorithms that can be used following an extreme event such as a large earthquake to identify structural damage for responding in a timely manner. The algorithms presented in the paper use measurements obtained from accelerometers and gyroscope to identify the occurrence of damage and classify the damage. Example algorithms are presented include those based on autoregressive moving average (ARMA), wavelet energies from wavelet transform and rotation models. The algorithms are illustrated through application of data from test structures such as the ASCE Benchmark structure and laboratory tests of scaled bridge columns and steel frames. The paper concludes by identifying needs for research and development in order for such algorithms to become viable in practice.

• 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 Engineering and Mechanics
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• v.70 no.4
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• pp.457-466
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• 2019

This study develops a damage detection method based on neural networks. The performance of the method is numerically and experimentally verified using a three-story shear building model. The framework is mainly composed of two hierarchical stages to identify damage location and extent using artificial neural network (ANN). The normalized damage signature index, that is a normalized ratio of the changes in the natural frequency and mode shape caused by the damage, is used to identify the damage location. The modal parameters extracted from the numerically developed structure for multiple damage scenarios are used to train the ANN. The positive alarm from the first stage of damage detection activates the second stage of ANN to assess the damage extent. The difference in mode shape vectors between the intact and damaged structures is used to determine the extent of the related damage. The entire procedure is verified using laboratory experiments. The damage is artificially modeled by replacing the column element with a narrow section, and a stochastic subspace identification method is used to identify the modal parameters. The results verify that the proposed method can accurately detect the damage location and extent.

• Structural Monitoring and Maintenance
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• v.2 no.1
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• pp.77-93
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• 2015

The idea of using measured dynamic characteristics for damage detection is attractive because it allows for a global evaluation of the structural health and condition. However, vibration-based damage detection for complex structures such as long-span cable-supported bridges still remains a challenge. As a suspension or cable-stayed bridge involves in general thousands of structural components, the conventional damage detection methods based on model updating and/or parameter identification might result in ill-conditioning and non-uniqueness in the solution of inverse problems. Alternatively, methods that utilize, to the utmost extent, information from forward problems and avoid direct solution to inverse problems would be more suitable for vibration-based damage detection of long-span cable-supported bridges. The auto-associative neural network (ANN) technique and the probabilistic neural network (PNN) technique, that both eschew inverse problems, have been proposed for identifying and locating damage in suspension and cable-stayed bridges. Without the help of a structural model, ANNs with appropriate configuration can be trained using only the measured modal frequencies from healthy structure under varying environmental conditions, and a new set of modal frequency data acquired from an unknown state of the structure is then fed into the trained ANNs for damage presence identification. With the help of a structural model, PNNs can be configured using the relative changes of modal frequencies before and after damage by assuming damage at different locations, and then the measured modal frequencies from the structure can be presented to locate the damage. However, such formulated ANNs and PNNs may still be incompetent to identify damage occurring at the deck members of a cable-supported bridge because of very low modal sensitivity to the damage. The present study endeavors to enhance the damage identification capability of ANNs and PNNs when being applied for identification of damage incurred at deck members. Effort is first made to construct combined modal parameters which are synthesized from measured modal frequencies and modal shape components to train ANNs for damage alarming. With the purpose of improving identification accuracy, effort is then made to configure PNNs for damage localization by adapting the smoothing parameter in the Bayesian classifier to different values for different pattern classes. The performance of the ANNs with their input being modal frequencies and the combined modal parameters respectively and the PNNs with constant and adaptive smoothing parameters respectively is evaluated through simulation studies of identifying damage inflicted on different deck members of the double-deck suspension Tsing Ma Bridge.

• Journal of Environmental Science International
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• v.17 no.6
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• pp.601-609
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• 2008

In this study for the development of area due to the increasing of industry, population and spreading of urbanization is rapidly increasing but about seventy percent of our nation's areas consists of the mountainous districts. In such case, when those areas have the heavy rains break, they are washed away by a fast-flowing stream of a valley and overflowed. Thus it could result on human life and property damage and also the widespread of flood damage in the downstream area. To decrease those damage, the construction of flood control reservoir is necessary. This research was aim to construct the flood runoff models of a mountainous small district and to determine the probability rainfall by analyzing precipitation. The study also examined the effects of location and size of flood control reservoir on flood reduction. The result showed that the construction of detention basin was an effective way to ensure the safety of flood control and multiple detention basin had superior result for reducing amount of runoff in the down stream area than the single detention basin.

• Wind and Structures
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• v.18 no.6
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• pp.669-691
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• 2014

Since low-rise residential buildings are the most common and vulnerable structures in coastal areas, a reliable prediction of their performance under hurricanes is necessary. The present study focuses on developing a refined finite element model that is able to more rigorously represent the load distributions or redistributions when the building behaves as a unit or any portion is overloaded. A typical 5:12 sloped low-rise residential building is chosen as the prototype and analyzed under wind pressures measured in the wind tunnel. The structural connections, including the frame-to-frame connections and sheathing-to-frame connections, are modeled extensively to represent the critical structural details that secure the load paths for the entire building system as well as the boundary conditions provided to the building envelope. The nail withdrawal, the excessive displacement of sheathing, the nail head pull-through, the sheathing in-plane shear, and the nail load-slip are found to be responsible for the building envelope damage. The uses of the nail type with a high withdrawal capacity, a thicker sheathing panel, and an optimized nail edge distance are observed to efficiently enhance the building envelope performance based on the present numerical damage predictions.

• Computers and Concrete
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• v.3 no.2_3
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• pp.103-122
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• 2006

Drying shrinkage of concrete occurs due to the loss of moisture and thus, it is controlled by moisture diffusion process. On the other hand, the shrinkage causes cracking of concrete and affects its moisture diffusion properties. Therefore, moisture diffusion and drying shrinkage are two coupled processes and their interactive effect is important for the durability of concrete structures. In this paper, the two material parameters in the moisture diffusion equation, i.e., the moisture capacity and humidity diffusivity, are modified by two different methods to include the effect of drying shrinkage on the moisture diffusion. The effect of drying shrinkage on the humidity diffusivity is introduced by the scalar damage parameter. The effect of drying shrinkage on the moisture capacity is evaluated by an analytical model based on non-equilibrium thermodynamics and minimum potential energy principle for a two-phase composite. The mechanical part of drying shrinkage is modeled as an elastoplastic damage problem. The coupled problem of moisture diffusion and drying shrinkage is solved using a finite element method. The present model can predict that the drying shrinkage accelerates the moisture diffusion in concrete, and in turn, the accelerated drying process increases the shrinkage strain. The coupling effects are demonstrated by a numerical example.

• Journal of Environmental Science International
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• v.14 no.6
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• pp.621-627
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• 2005

It is analysised the causes and extent of damage of natural disasters through the investigating of natural disaster states occurred in Gyeongsangnam-do. The data for this study were based on disaster annual report between 1987 and 2003. Especially, the data between 1993 and 2003 were used for the analysis in Gyeongsangnam-do area. A typhoon and a heavy rain were the major causes of the natural disasters in Gyeongsangnam-do. For all that the extent of damage by a heavy rain was twice as much as that of a typhoon, Gyongsangnam-do suffered heavy damage from a typhoon. So, special attentions should be paid to establish prevention plans for that in this area. Also, half of the natural disasters were occurred between July and August, the intensive prevention plans for the summer season are needed.

• Journal of the Earthquake Engineering Society of Korea
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• v.22 no.3
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• pp.121-128
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• 2018

Severe earthquakes can cause damage to society both socially and economically. An appropriate initial response can alleviate damage from severe earthquakes. In order to formulate an appropriate initial response, it is necessary to identify damage situations in societies; however, it is difficult to grasp this information immediately after an earthquake event. In this study, an earthquake damage assessment methodology for buildings is proposed for estimating damage situations immediately after severe earthquakes. A response spectrum database is constructed to provide response spectra at arbitrary locations from earthquake measurements immediately after the event. The fragility curves are used to estimate the damage of the buildings. Earthquake damage assessment is performed from the response spectrum database at the building scale to provide enhanced damage condition information. Earthquake damage assessment for Gyeongju city and Pohang city were conducted using the proposed methodology, when an earthquake occurred on September 12, 2016, and November 15, 2017. Results confirm that the proposed earthquake damage assessment effectively represented the earthquake damage situation in the city to decide on an appropriate initial response by providing detailed information at the building scale.