• Journal of Environmental Health Sciences
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• v.46 no.5
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• pp.588-598
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• 2020

Objectives: Human indices were developed to determine returning point of residents and damage restoration after the chemical accident Methods: To determine the returning point of residents after the chemical accident, a new concept, the standard man model was introduced as a human index, in which both H-code and its acute effects were main idea. To evaluate the applicability, a hydrogen fluoride leakage accident in Gumi was applied. The returning point were suggested as the conservative remission period of acute effects among relevant hazard effects and compared with actual returning point. The coverage of each age group were considered with reflecting average daily dose expected for actual residents. In addition, a relief-index as a social-scientific approach was reflected as well to apply the damage restoration Results: Actual returning point of residents in Gumi was 88 days; and that of standard man model suggested was 84 days. The expected amount of exposure at aged 12 or under was at least 2.35 times greater than that of this model, 40s, theoretically. However, their population ratio was less than 1%, so 99% of residents could be applied when the standard man model was applied. The relief-index was as an objective and quantitative methodology to apply the qualitative aspect. Conclusions: Although evaluated as a relatively positive result, there was a limitation such as the number of accident applied to the verification of standard man model. The relief index was also considered, but further research should be carried out to find threshold level for the relief.

• Computers and Concrete
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• v.27 no.5
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• pp.447-455
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• 2021

Research on damage detection methods in structures began a few decades ago with the introduction of methods based on structural vibration frequencies, which, of course, continues to this day. The value of important structures, on the one hand, and the countless maintenance costs on the other hand, have led researchers to always try to identify more accurate methods to diagnose damage to structures in the early stages. Among these, one of the most important and widely used methods in damage detection is the use of time-frequency representations. By using time-frequency representations, it is possible to process signals simultaneously in the time and frequency domains. In this research, the Short-Time Fourier transform, a known time-frequency function, has been used to process signals and identify the system. Besides, a new damage index has been introduced to identify damages in concrete piers of bridges. The proposed method has relatively simple calculations. To evaluate the method, the finite element model of an existing concrete bridge was created using as-built details. Based on the results, the method identifies the damages with high accuracy.

• Journal of the Korea institute for structural maintenance and inspection
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• v.24 no.6
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• pp.206-216
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• 2020

As the number of aging railway bridges in Korea increases, maintenance costs due to aging are increasing and continuous management is becoming more important. However, while the number of old facilities to be managed increases, there is a shortage of professional personnel capable of inspecting and diagnosing these old facilities. To solve these problems, this study presents an improved model that can detect Local damage to structures using machine learning techniques of AI technology. To construct a damage detection machine learning model, an analysis model of the bridge was set by referring to the design drawing of a non-ballasted plate-girder railroad bridge. Static strain data according to the damage scenario was extracted with the analysis model, and the Local damage index based on the reliability of the bridge was presented using statistical techniques. Damage was performed in a three-step process of identifying the damage existence, the damage location, and the damage severity. In the estimation of the damage severity, a linear regression model was additionally considered to detect random damage. Finally, the random damage location was estimated and verified using a machine learning-based damage detection classification learning model and a regression model.

• Smart Structures and Systems
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• v.18 no.6
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• pp.1233-1250
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• 2016

Cracks in plate-like structures are some of the main reasons for destruction of the entire structure. In this study, a novel two-stage methodology is proposed for damage detection of flexural plates using an optimized artificial neural network. In the first stage, location of damages in plates is investigated using curvature-moment and curvature-moment derivative concepts. After detecting the damaged areas, the equations for damage severity detection are solved via Bat Algorithm (BA). In the second stage, in order to efficiently reduce the computational cost of model updating during the optimization process of damage severity detection, multiple damage location assurance criterion index based on the frequency change vector of structures are evaluated using properly trained cascade feed-forward neural network (CFNN) as a surrogate model. In order to achieve the most generalized neural network as a surrogate model, its structure is optimized using binary version of BA. To validate this proposed solution method, two examples are presented. The results indicate that after determining the damage location based on curvature-moment derivative concept, the proposed solution method for damage severity detection leads to significant reduction of computational time compared with direct finite element method. Furthermore, integrating BA with the efficient approximation mechanism of finite element model, maintains the acceptable accuracy of damage severity detection.

• 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.

• Earthquakes and Structures
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• v.7 no.3
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• pp.349-365
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• 2014

The dynamic response of structures under extremely short duration dynamic loads is of great concern nowadays. This paper investigates structures' response as well as the associated structural damage to explosive loads considering and ignoring the supporting soil flexibility effect. In the analysis, buildings are modeled by two alternate approaches namely, (1) building with fixed supports, (2) building with supports accounting for soil-flexibility. A lumped parameter model with spring-dashpot elements is incorporated at the base of the building model to simulate the horizontal and rotational movements of supporting soil. The soil flexibility for various shear wave velocities has been considered in the investigation. In addition, the influence of variation of lateral natural periods of building models on the obtained response and peak response time-histories besides damage indices has also been investigated under blast loads with different peak over static pressures. The Dynamic response is obtained by solving the governing equations of motion of the considered building model using a developed Matlab code based on the finite element toolbox CALFEM. The predicted results expressed in time-domain by the building model incorporating SSI effect are compared with the corresponding model results ignoring soil flexibility effect. The results show that the effect of surrounding soil medium leads to significant changes in the obtained dynamic response of the considered systems and hence cannot be simply ignored in damage assessment and response time-histories of structures where it increases response and amplifies damage of structures subjected to blast loads. Moreover, the numerical results provide an understanding of level of damage of structure through the computed damage indices.

• Structural Engineering and Mechanics
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• v.12 no.3
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• pp.341-346
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• 2001

Near source earthquakes can be characterized not only by strong horizontal but also by strong vertical ground motions with broad range of dominant frequencies. The inelastic horizontal response of thin-walled L-shaped steel bridge piers, which are popularly used as highway bridge supports, subjected to simultaneous horizontal and vertical ground excitations of near source earthquakes is investigated. A comprehensive damage index and an evolutionary-degrading hysteretic model are applied. Numerical analysis reveals that the strong vertical excitation of a near source earthquake exerts considerable influences on the damage development and horizontal response of thin-walled L-shaped steel bridge piers.

• Smart Structures and Systems
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• v.29 no.1
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• pp.41-51
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• 2022

Cables are critical components of cable-stayed bridges. A structural health monitoring system provides real-time cable tension recording for cable health monitoring. However, the measurement data involve multiple sources of variability, i.e., varying environmental and operational factors, which increase the complexity of cable condition monitoring. In this study, a one-class classification method is developed for cable condition assessment using Bayesian factor analysis (FA). The single-peaked vehicle-induced cable tension is assumed to be relevant to vehicle positions and weights. The Bayesian FA is adopted to establish the correlation model between cable tensions and vehicles. Vehicle weights are assumed to be latent variables and the influences of different transverse positions are quantified by coefficient parameters. The Bayesian theorem is employed to estimate the parameters and variables automatically, and the damage index is defined on the basis of the well-trained model. The proposed method is applied to one cable-stayed bridge for cable damage detection. Significant deviations of the damage indices of Cable SJS11 were observed, indicating a damaged condition in 2011. This study develops a novel method to evaluate the health condition of individual cable using the FA in the Bayesian framework. Only vehicle-induced cable tensions are used and there is no need to monitor the vehicles. The entire process, including the data pre-processing, model training and damage index calculation of one cable, takes only 35 s, which is highly efficient.

• Journal of the Korea Institute of Military Science and Technology
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• v.21 no.3
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• pp.279-287
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• 2018

This study investigated applicability of the fast running model for damage assessment of underground structures by ground shock. For this reason, the fast running model that consists of two main models such as the ground shock generation and propagation model and the underground structural damage assessment model was developed. The ground shock generation and propagation model was programed using theoretical formula and empirical formula introduced in TM5-855-1(US army manual). The single degree of freedom model of structural components was utilized to predict structural dynamic displacements which are used as index to assess damage level of components. In order to confirm the feasibility of the developed fast running model, underground structural dynamic displacements estimated from the fast running model were compared to displacements obtained from the finite element analysis.

• Journal of Ocean Engineering and Technology
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• v.30 no.4
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• pp.287-293
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• 2016

This paper presents an effective damage detection method for offshore jackets using natural frequency change ratios. Two parameters, cosine similarity and magnitude index, are considered to estimate the location and severity of the damage in the structure. A numerical jacket structure model is considered to verify the performance of the proposed method. As observed through analysis, the damages in the structure are detected accurately.