• Smart Structures and Systems
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• v.15 no.3
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• pp.699-715
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• 2015

A modal parameter based damage sensitive feature (DSF) is defined to mimic the relative change in any diagonal element of the stiffness matrix of a model of a structure. The damage assessment is performed in a statistical pattern recognition framework using empirical complementary cumulative distribution functions (ECCDFs) of the DSFs extracted from measured operational vibration response data. Methods are discussed to perform probabilistic structural health assessment with respect to the following questions: (a) "Is there a change in the current state of the structure compared to the baseline state?", (b) "Does the change indicate a localized stiffness reduction or increase?", with the latter representing a situation of retrofitting operations, and (c) "What is the severity of the change in a probabilistic sense?". To identify a range of normal structural variations due to environmental and operational conditions, lower and upper bound ECCDFs are used to define the baseline structural state. Such an approach attempts to decouple "non-damage" related variations from damage induced changes, and account for the unknown environmental/operational conditions of the current state. The damage assessment procedure is discussed using numerical simulations of ambient vibration testing of a bridge deck system, as well as shake table experimental data from a 4-story steel frame.

• Smart Structures and Systems
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• v.14 no.2
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• pp.159-189
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• 2014

One of the most important requirements in the evaluation of existing structural systems and ensuring a safe performance during their service life is damage assessment. Damage can be defined as a weakening of the structure that adversely affects its current or future performance which may cause undesirable displacements, stresses or vibrations to the structure. The mass and stiffness of a structure will change due to the damage, which in turn changes the measured dynamic response of the system. Damage detection can increase safety, reduce maintenance costs and increase serviceability of the structures. Artificial Neural Networks (ANNs) are simplified models of the human brain and evolved as one of the most useful mathematical concepts used in almost all branches of science and engineering. ANNs have been applied increasingly due to its powerful computational and excellent pattern recognition ability for detecting damage in structural engineering. This paper presents and reviews the technical literature for past two decades on structural damage detection using ANNs with modal parameters such as natural frequencies and mode shapes as inputs.

• Smart Structures and Systems
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• v.18 no.3
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• pp.525-540
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• 2016

The CFRP-confined circular concrete-filled steel tubular column is composed of concrete, steel, and CFRP. Its failure mechanics are complex. The most important difficulties are lack of an available method to establish a relationship between a specific damage mechanism and its acoustic emission (AE) characteristic parameter. In this study, AE technique was used to monitor the evolution of damage in CFRP-confined circular concrete-filled steel tubular columns. A fuzzy c-means method was developed to determine the relationship between the AE signal and failure mechanisms. Cluster analysis results indicate that the main AE sources include five types: matrix cracking, debonding, fiber fracture, steel buckling, and concrete crushing. This technology can not only totally separate five types of damage sources, but also make it easier to judge the damage evolution process. Furthermore, typical damage waveforms were analyzed through wavelet analysis based on the cluster results, and the damage modes were determined according to the frequency distribution of AE signals.

• Smart Structures and Systems
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• v.23 no.5
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• pp.507-520
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• 2019

In the structural health monitoring field, damage detection has been commonly carried out based on the structural model and the engineering features related to the model. However, the extracted features are often subjected to various errors, which makes the pattern recognition for damage detection still challenging. In this study, an automated damage identification method is presented for hanger cables in a tied-arch bridge using a convolutional neural network (CNN). Raw measurement data for Fourier amplitude spectra (FAS) of acceleration responses are used without a complex data pre-processing for modal identification. A CNN is a kind of deep neural network that typically consists of convolution, pooling, and fully-connected layers. A numerical simulation study was performed for multiple damage detection in the hangers using ambient wind vibration data on the bridge deck. The results show that the current CNN using FAS data performs better under various damage states than the CNN using time-history data and the traditional neural network using FAS. Robustness of the present CNN has been proven under various observational noise levels and wind speeds.

• Smart Structures and Systems
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• v.29 no.1
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• pp.105-116
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• 2022

Stay cables play an essential role in cable-stayed bridges. Severe vibrations and/or harsh environment may result in cable failures. Therefore, an efficient structural health monitoring (SHM) solution for cable damage detection is necessary. This study proposes a data-driven method for immediately detecting cable damage from measured cable forces by recognizing pattern transition from the intact condition when damage occurs. In the proposed method, pattern recognition for cable damage detection is realized by time series classification (TSC) using a deep learning (DL) model, namely, the long short term memory fully convolutional network (LSTM-FCN). First, a TSC classifier is trained and validated using the cable forces (or cable force ratios) collected from intact stay cables, setting the segmented data series as input and the cable (or cable pair) ID as class labels. Subsequently, the classifier is tested using the data collected under possible damaged conditions. Finally, the cable or cable pair corresponding to the least classification accuracy is recommended as the most probable damaged cable or cable pair. A case study using measured cable forces from an in-service cable-stayed bridge shows that the cable with damage can be correctly identified using the proposed DL-TSC method. Compared with existing cable damage detection methods in the literature, the DL-TSC method requires minor data preprocessing and feature engineering and thus enables fast and convenient early detection in real applications.

• Computers and Concrete
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• v.4 no.3
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• pp.243-257
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• 2007

A novel method for recognition, characterization, and quantification of deterioration in bridge components and laboratory concrete samples is presented in this paper. The proposed scheme is based on grey level co-occurrence matrix texture analysis using Haar's discrete wavelet transform on concrete imagery. Each image is described by a subset of band-filtered images containing wavelet coefficients, and then reconstructed images are employed in characterizing the texture, using grey level co-occurrence matrices, of the different types and degrees of damage: map-cracking, spalling and steel corrosion. A comparative study was conducted to evaluate the efficiency of the supervised maximum likelihood and unsupervised K-means classification techniques, in order to classify and quantify the deterioration and its extent. Experimental results show both methods are relatively effective in characterizing and quantifying damage; however, the supervised technique produced more accurate results, with overall classification accuracies ranging from 76.8% to 79.1%.

• Journal of Dental Anesthesia and Pain Medicine
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• v.20 no.1
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• pp.45-47
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• 2020

Endotracheal tube damage is a well-known complication of maxillary surgery. We report a case of failure to ventilate due to superficial damage to the tubing between the cuff and pilot balloon in the nasal portion of a north facing Ring, Adair and Elwyn pre-formed endotracheal tube during Surgically Assisted Rapid Palatal Expansion surgery. The endotracheal tube was replaced uneventfully and surgery completed successfully. On reflection, we feel that that the vulnerable position of the cuff-pilot tubing significantly contributed to this critical incident and suggest that increased recognition of this is vital for the prevention of such cases in the future.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.6_3
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• pp.1285-1291
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• 2022

In this paper, we propose a low-cost, low-power embedded environment-based deep learning lightweight model for input images to recognize laundry management codes. Laundry franchise companies mainly use barcode recognition-based systems to record laundry consignee information and laundry information for laundry collection management. Conventional laundry collection management systems using barcodes require barcode printing costs, and due to barcode damage and contamination, it is necessary to improve the cost of reprinting the barcode book in its entirety of 1 billion won annually. It is also difficult to do. Recognition performance is improved by applying the VGG model with 7 layers, which is a reduced-transformation of the VGGNet model for number recognition. As a result of the numerical recognition experiment of service parts drawings, the proposed method obtained a significantly improved result over the conventional method with an F1-Score of 0.95.

• Journal of Technologic Dentistry
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• v.38 no.3
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• pp.209-216
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• 2016

Purpose: The purpose of this study is to discover the recognition of dental technicians on MSDS and to research the furnishing rate of Material Safety Data Sheet (MSDS) in dental laboratories. Method: 104 dental technicians who were self-written questionnaire. The results were analyzed by SPSS 12.0. The answers to the questionnaire underwent frequency analysis, chi-square test were performed. Results: The result of cross analysis on the occurrence of occupational diseases according to whether MSDS is furnished showed that the group with MSDS furnished had 37.8% of damages due to their occupation, but the group with MSDS unfurnished had 65.7% of damages due to their occupation, which displayed a significant difference(p<0.05). The result of cross analysis on whether the education about chemicals according to whether MSDS is furnished showed there were relatively more responses of no education on the types and characteristics of chemicals, the effect on human body and its risk, emergency measure when exposed to risk, the treating and storing methods and the disposal methods. In addition, there were significant differences on whether all the above education was done between the group with MSDS furnished and the group without MSDS(p<0.000). Conclusion: In order to secure safe working environment of dental technicians and to reduce occurrence of damage caused by chemicals, the related education is necessary. The recognition of dental technicians on MSDS and furnishing rate is increasing but still insufficient, so the related organizations need to establish education and PR measures for prevention of damage.

• Computers and Concrete
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• v.32 no.5
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• pp.475-486
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• 2023

Nondestructive evaluation (NDE) is an important task of civil engineering structure monitoring and inspection, but minor damage such as small cracks in local structure is difficult to observe. If cracks continued expansion may cause partial or even overall damage to the structure. Therefore, monitoring and detecting the structure in the early stage of crack propagation is important. The crack detection technology based on machine vision has been widely studied, but there are still some problems such as bad recognition effect for small cracks. In this paper, we proposed a deep learning method based on sweep signals to evaluate concrete surface crack with a width less than 1 mm. Two convolutional neural networks (CNNs) are used to analyze the one-dimensional (1D) frequency sweep signal and the two-dimensional (2D) time-frequency image, respectively, and the probability value of average damage (ADPV) is proposed to evaluate the minor damage of structural. Finally, we use the standard deviation of energy ratio change (ERVSD) and infrared thermography (IRT) to compare with ADPV to verify the effectiveness of the method proposed in this paper. The experiment results show that the method proposed in this paper can effectively predict whether the concrete surface is damaged and the severity of damage.