• Proceedings of the Computational Structural Engineering Institute Conference
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• 2007.04a
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• pp.43-48
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• 2007

In this study, an ANN-based damage detection algorithm using acceleration signals is developed for alarming locations of damage in beam-type structures. A new ANN-algorithm using output-only acceleration responses is designed for damage detection in real time. The cross-covariance of two acceleration signals measured at two different locations is selected as the feature representing the structural condition. Neural networks are trained for potential loading patterns and damage scenarios of the target structure for which its actual loadings are unknown. The feasibility and practicality of the proposed method are evaluated from laboratory-model tests on free-free beams for which accelerations were measured before and after several damage cases.

• Smart Structures and Systems
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• v.25 no.1
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• pp.47-56
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• 2020

This paper proposes a model-based approach for structural damage identification and quantification. Using pseudo modal strain energy and mode shape vectors, a damage-sensitive objective function is introduced which is suitable for damage estimation and quantification in shear frames. Whale optimization algorithm (WOA) is used to solve the problem and report the optimal solution as damage detection results. To illustrate the capability of the proposed method, a numerical example of a shear frame under different damage patterns is studied in both ideal and noisy cases. Furthermore, the performance of the WOA is compared with particle swarm optimization algorithm, as one the widely-used optimization techniques. The applicability of the method is also experimentally investigated by studying a six-story shear frame tested on a shake table. Based on the obtained results, the proposed method is able to assess the health of the shear building structures with high level of accuracy.

• Structural Engineering and Mechanics
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• v.84 no.2
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• pp.181-206
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• 2022

This paper presents experimental modal analysis and static load testing results to validate the accuracy of dynamic parameters-based damage locating indices in RC structures. The study investigates the accuracy of different dynamic-based damage locating indices compared to observed crack patterns from static load tests and how different damage levels and scenarios impact them. The damage locating indices based on mode shape curvature and mode shape fourth derivate in their original forms were found to show anomalies along the beam length and at the supports. The modified forms of these indices show higher sensitivity in locating single and multi-cracks at different damage scenarios. The proposed stiffness reduction index shows good sensitivity in detecting single and multi-cracks. The proposed anomalies elimination procedure helps to remove the anomalies along the beam length. Also, the adoption of the proposed weighting method averaging procedure and normalization procedure help to draw the overall crack pattern based on the adopted set of modes.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2008.04a
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• pp.197-202
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• 2008

In this paper, a hybrid vibration-impedance approaches is newly proposed to detect the occurrence of damage, the location of damage, and extent of damage in steel plate-girder bridges. The hybrid scheme mainly consists of three sequential phases: 1) to alarm the occurrence of damage, 2) to classify the alarmed damage, and 3) to estimate the classified damage in detail. Damage types of interest include flexural stiffness-loss in girder and bolts-loose in supports. In the first phase, the global occurrence of damage is alarmed by monitoring changes in acceleration features. In the second phase, the alarmed damage is classified into subsystems by recognizing patterns of impedance features. In the final phase, the location and the extent of damage are estimated by using modal strain energy-based damage index method and root mean square deviation method. The feasibility of the proposed system is evaluated on a laboratory-scaled steel plate-girder bridge model for which hybrid vibration-impedance signatures were measured for several damage scenarios.

• Steel and Composite Structures
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• v.15 no.4
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• pp.357-377
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• 2013

In order to study the degradation and damage behaviors of steel frame welded connections, two series of tests in references with different connection constructions were carried out subjected to various cyclic loading patterns. Hysteretic curves, degradation and damage behaviours and fatigue properties of specimens were firstly studied. Typical failure modes and probable damage reasons were discussed. Then, various damage index models with variables of dissipative energy, cumulative displacement and combined energy and displacement were summarized and applied for all experimental specimens. The damage developing curves of ten damage index models for each connection were obtained. Finally, the predicted and evaluated capacities of damage index models were compared in order to describe the degraded performance and failure modes. The characteristics of each damage index model were discussed in depth, and then their distributive laws were summarized. The tests and analysis results showed that the loading histories significantly affected the distributive shapes of damage index models. Different models had their own ranges of application. The selected parameters of damage index models had great effect on the developing trends of damage curves. The model with only displacement variable was recommended because of a more simple form and no integral calculation, which was easier to be formulated and embedded in application programs.

• The Korean Journal of Physiology and Pharmacology
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• v.13 no.5
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• pp.343-348
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• 2009

53BP1 is an important genome stability regulator, which protects cells against double-strand breaks. Following DNA damage, 53BP1 is rapidly recruited to sites of DNA breakage, along with other DNA damage response proteins, including ${\gamma}$-H2AX, MDC1, and BRCA1. The recruitment of 53BP1 requires a tandem Tudor fold which associates with methylated histones H3 and H4. It has already been determined that the majority of DNA damage response proteins are phosphorylated by ATM and/or ATR after DNA damage, and then recruited to the break sites. 53BP1 is also phosphorylated at several sites, like other proteins after DNA damage, but this phosphorylation is not critically relevant to recruitment or repair processes. In this study, we evaluated the functions of phosphor-53BP1 and the role of the BRCT domain of 53BP1 in DNA repair. From our data, we were able to detect differences in the phosphorylation patterns in Ser25 and Ser1778 of 53BP1 after neocarzinostatin-induced DNA damage. Furthermore, the foci formation patterns in both phosphorylation sites of 53BP1 also evidenced sizeable differences following DNA damage. From our results, we concluded that each phosphoryaltion site of 53BP1 performs different roles, and Ser1778 is more important than Ser25 in the process of DNA repair.

• Smart Structures and Systems
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• v.12 no.3_4
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• pp.345-361
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• 2013

Locating and assessing the severity of damage in large or complex structures is one of the most challenging problems in the field of civil engineering. Considering that the wavelet packet transform (WPT) has the ability to clearly reflect the damage characteristics of structural response signals and the artificial neural network (ANN) is capable of learning in an unsupervised manner and of forming new classes when the structural exhibits change, this paper investigates a multi-stage structural damage diagnosis method by using the WPT and ANN based on "energy-damage" theory, in which, the wavelet packet component energies are first extracted to be damage sensitive feature and then adopted as input into an improved back propagation (BP) neural network model for damage diagnosis in a step by step mode. To validate the efficacy of the presented approach of the damage diagnosis, the benchmark structure of the American Society of Civil Engineers (ASCE) is employed in the case study. The results of damage diagnosis indicate that the method herein is computationally efficient and is able to detect the existence of different damage patterns in the simulated experiment where minor, moderate and severe damages corresponds to involving in the loss of stiffness on braces or the removal bracing in various combinations.

• Korean journal of applied entomology
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• v.60 no.2
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• pp.221-228
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• 2021

The fall armyworm (FAW), Spodoptera frugiperda (Smith), is a notorious invasive migratory pest native to the tropics that has recently invaded South Korea with subsequent damage to cornfields. This study was conducted to evaluate the damage patterns on corn plants caused by the migratory and subsequent generations of FAW. The early migrant generation-related infestation rates reached an average of 13.2%, ranging from a minimum of 4.3% ('Allog-i') to a maximum of 33.0% ('Chodang'), depending on the corn cultivar. The proportion of FAW larvae-infested corn plants, in which the FAW survived until the pupal stage was 19.3%. The subsequent FAW generation caused considerable damage to the ears, resulting in 60% of ears with damaged kernels. This damage was markedly different from the nearly negligible damage caused by the migratory generation. The FAW larval dispersion was the most dynamic during the second instar stage and occurred along the same cornrow in line. In addition, we discuss the development of corn pant damage patterns caused by FAW. In summary, the results of the present study would provide useful basic information for the damage analysis of this pest for future studies.

• 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.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2006.04a
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• pp.240-247
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• 2006

Piezoelectric sensor-based health monitoring technique using a two-step support vector machine (SYM) classifier is discussed for damage identification of a railroad track. An active sensing system composed of two PZT patches was investigated in conjunction with both impedance and guided wave propagation methods to detect two kinds of damage of the railroad track (one is a hole damage of 0.5cm in diameter at web section and the other is a transverse cut damage of 7.5cm in length and 0.5cm in depth at head section). Two damage-sensitive features were extracted one by one from each method; a) feature I: root mean square deviations (RMSD) of impedance signatures and b) feature II: wavelet coefficients for $A_0$ mode of guided waves. By defining damage indices from those damage-sensitive features, a two-dimensional damage feature (2-D DF) space was made. In order to minimize a false-positive indication of the current active sensing system, a two-step SYM classifier was applied to the 2-D DF space. As a result, optimal separable hyper-planes were successfully established by the two-step SYM classifier: Damage detection was accomplished by the first step-SYM, and damage classification was also carried out by the second step-SYM. Finally, the applicability of the proposed two-step SYM classifier has been verified by thirty test patterns.