• Smart Structures and Systems
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• v.31 no.4
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• pp.383-392
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• 2023

Post-earthquake building condition assessment is crucial for subsequent rescue and remediation and can be automated by emerging computer vision and deep learning technologies. This study is based on an endeavour for the 2nd International Competition of Structural Health Monitoring (IC-SHM 2021). The task package includes five image segmentation objectives - defects (crack/spall/rebar exposure), structural component, and damage state. The structural component and damage state tasks are identified as the priority that can form actionable decisions. A multi-task Convolutional Neural Network (CNN) is proposed to conduct the two major tasks simultaneously. The rest 3 sub-tasks (spall/crack/rebar exposure) were incorporated as auxiliary tasks. By synchronously learning defect information (spall/crack/rebar exposure), the multi-task CNN model outperforms the counterpart single-task models in recognizing structural components and estimating damage states. Particularly, the pixel-level damage state estimation witnesses a mIoU (mean intersection over union) improvement from 0.5855 to 0.6374. For the defect detection tasks, rebar exposure is omitted due to the extremely biased sample distribution. The segmentations of crack and spall are automated by single-task U-Net but with extra efforts to resample the provided data. The segmentation of small objects (spall and crack) benefits from the resampling method, with a substantial IoU increment of nearly 10%.

• Journal of the military operations research society of Korea
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• v.5 no.1
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• pp.37-72
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• 1979

This paper presents the FORTRAN program for expected damage by surface-to-surface weapons. One of the methods can be used to determine the effectiveness of general purpose (GP) bombs and cluster weapons against single unitary targets, linear targets, area targets, and areas of unitary target elements, The effectiveness index is in terms of fractional damage ($F_D$) or the number of volleys ($N_V$).

• Structural Engineering and Mechanics
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• v.64 no.3
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• pp.353-360
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• 2017

This study presents the Big Bang and Big Crunch (BB-BC) optimization algorithm for detection of structure damage in large severity. Local damage is represented by a perturbation in the elemental stiffness parameter of the structural finite element model. A nonlinear objective function is established by minimizing the discrepancies between the measured and calculated acceleration responses (AR) of the structure. The BB-BC algorithm is utilized to solve the objective function, which can localize the damage position and obtain the severity of the damage efficiently. Numerical simulations have been conducted to identify both single and multiple structural damages for beam, plate and European Space Agency Structures. The present approach gives accurate identification results with artificial measurement noise.

• Toxicological Research
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• v.17 no.4
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• pp.249-253
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• 2001

Fumonisin B1, a mycotoxin, is thought to induce esophageal cancer in humans and apoptosis in animal cells by inhibiting ceramide synthase. Dumonisin Bl may also generate reactive oxygen species directly or indirectly, leading to DNA damage and lipid peroxidation. In this study, a DNA fragmentation assay, dichlorofluorescein (DCF) analysis, and single cell gel electrophoresis (SCGE) were used to investigate the involvement of cellular free radicals, specifically hydrogen peroxide, in the DNA damaging activity of fumonisin B1. From an in vitro DNA fragmentation assay, E. coli DNA, damage by fumonisin Bl was increased by the addition of superxide dismutase (SOD) and decreased by catalase. SCGE and DCF analysis in vivo showed that the nuclear DNA damage and intracellular free radicals in cultured rat hepatocytes treated with fumonisin B1 were increased with the concentration of fumonisin Bl . DNA damage and free radical generation were inhibited by the addition of catalase. Fumonisin Bl , in the presence of SOD, produces hydrogen peroxide causing oxidative DNA damage and protein malfunction, leading to genotoxicity and cytotoxicity of the toxin.

• Computational Structural Engineering
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• v.7 no.3
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• pp.153-166
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• 1994

A methodology to assess damage prediction accuracy as a function of model uncertainty in structures is presented. In the first part, a theory of approach is outlined. First, a damage detection algorithm to locate and size damage in structures using few modal responses of the structures is summarized. Next, methods to quantify model uncertainty and the damage detection accuracy are formulated. In the second part, a methodology to assess the effect of model uncertainty on the damage detection accuracy of real structures is designed. In the last part, the feasibility of the assessment methodology is demonstrated by using a plate-girder bridge for which only information on a single mode is available.

• 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.

• Journal of Radiation Protection and Research
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• v.42 no.1
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• pp.63-68
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• 2017

Background: The combined effect of the low energy electron (LEE) irradiation and $Cu^{2+}$ ion on DNA damage was investigated. Materials and Methods: Lyophilized pBR322 plasmid DNA films with various concentrations (1-15 mM) of $Cu^{2+}$ ion were independently irradiated by monochromatic LEEs with 5 eV. The types of DNA damage, single strand break (SSB) and double strand break (DSB), were separated and quantified by gel electrophoresis. Results and Discussion: Without electron irradiation, DNA damage was slightly increased with increasing Cu ion concentration via Fenton reaction. LEE-induced DNA damage, with no Cu ion, was only 6.6% via dissociative electron attachment (DEA) process. However, DNA damage was significantly increased through the combined effect of LEE-irradiation and Cu ion, except around 9 mM Cu ion. The possible pathways of DNA damage for each of these different cases were suggested. Conclusion: The combined effect of LEE-irradiation and Cu ion is likely to cause increasing dissociation after elevated transient negative ion state, resulting in the enhanced DNA damage. For the decrease of DNA damage at around 9-mM Cu ion, it is assumed to be related to the structural stabilization due to DNA inter- and intra-crosslinks via Cu ion.

• Structural Engineering and Mechanics
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• v.77 no.2
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• pp.151-165
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• 2021

This paper aims to present a novelty damage detection method to identify damage locations by the simultaneous use of both the energy and displacement damage indices. Using this novelty method, the damaged location and even the damaged floor are accurately detected. As a first method, a combination of the instantaneous frequency energy index (EDI) and the structural acceleration responses are used. To evaluate the first method and also present a rapid assessment method, the Displacement Damage Index (DDI), which consists of the error reliability (β) and Normal Probability Density Function (NPDF) indices, are introduced. The innovation of this method is the simultaneous use of displacement-acceleration responses during one process, which is more effective in the rapid evaluation of damage patterns with velocity vectors. In order to evaluate the effectiveness of the proposed method, various damage scenarios of the ASCE benchmark problem, and the effects of measurement noise were studied numerically. Extensive analyses show that the rapid proposed method is capable of accurately detecting the location of sparse damages through the building. Finally, the proposed method was validated by experimental studies of a six-story steel building structure with single and multiple damage cases.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.6
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• pp.93-101
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• 2022

RC Slab bridges in Korea account for more than 70% of the total bridges for more than 20 years of service. As the number of aging structures increases, the importance of safety diagnosis and maintenance of structures increases. For highway bridges, cracks are a main cause of deck deterioration, which is very closely related to the decrease in bridge durability and service life. In addition, the damage rate of expansion joints and bearings accounts for approximately 73% higher than that of major members. Therefore, this study defined damage scenarios combined with devices damages and deck deterioration. The stress distribution and maximum stress on the deck were then evaluated using design vehicle load and daily temperature gradient for single and combined damage scenarios. Furthermore, this study performed damage-spread analysis and predicted condition ratings according to a deck deterioration model generated from the inspection and diagnosis history data of cracks. The heterogeneous damages combined with the member damages of expansion joints and bearings increased the rate of crack area and damage spread, which accelerated the time to reach the condition rating of C. Therefore, damage to bridge members requires proper and prompt repair and replacement, and otherwise it can cause the damage to bridge deck and the spread of the damage.

• Composites Research
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• v.17 no.4
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• pp.61-67
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• 2004

Nondestructive damage sensitivity on single-basalt fiber/epoxy composites was evaluated by micromechanical technique and acoustic emission (AE). Piezoelectric lead-zirconate-titanate (PZT), polyvinylidene fluoride (PVDF) and poly(vinylidene fluoride-trifluoroethylene) (P(VDF-TrFE)) copolymer were used as AE sensor, respectively. In single-fiber composite, the damage sensing with different sensor types were compared to each other. Piezoelectric PVDF polymer sensor was embedded in and attached on the composite, whereas PZT sensor was only attached on the surface of specimen. In case of embedded polymer sensors, responding sensitivity was higher than that of the attached case. It can be due to full constraint inside specimen to transfer elastic wave coming from micro-deformation. For both the attached and the embedded cases, the sensitivity of P(VDF-TrFE) sensor was almost same as that of conventional PVDF sensor.