• Smart Structures and Systems
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• v.29 no.1
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• pp.181-193
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• 2022

Data-driven structural health monitoring (SHM) of civil infrastructure can be used to continuously assess the state of a structure, allowing preemptive safety measures to be carried out. Long-term monitoring of large-scale civil infrastructure often involves data-collection using a network of numerous sensors of various types. Malfunctioning sensors in the network are common, which can disrupt the condition assessment and even lead to false-negative indications of damage. The overwhelming size of the data collected renders manual approaches to ensure data quality intractable. The task of detecting and classifying an anomaly in the raw data is non-trivial. We propose an approach to automate this task, improving upon the previously developed technique of image-based pre-processing on one-dimensional (1D) data by enriching the features of the neural network input data with multiple channels. In particular, feature engineering is employed to convert the measured time histories into a 3-channel image comprised of (i) the time history, (ii) the spectrogram, and (iii) the probability density function representation of the signal. To demonstrate this approach, a CNN model is designed and trained on a dataset consisting of acceleration records of sensors installed on a long-span bridge, with the goal of fault detection and classification. The effect of imbalance in anomaly patterns observed is studied to better account for unseen test cases. The proposed framework achieves high overall accuracy and recall even when tested on an unseen dataset that is much larger than the samples used for training, offering a viable solution for implementation on full-scale structures where limited labeled-training data is available.

• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.189-195
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• 2024

Road transport occupies the largest share in domestic and international transport. It is of key importance for the development of the economy, forasmuch as it provides the livelihood of the population, the development of the national economy, the possibility of establishing foreign economic relations. The purpose of the research is as follows: analysis of the current state of functioning of the road transport sector in Eastern Europe and identification of key problems and trends in its development. Research methods: Methods of grouping, comparison and generalization, correlation analisys have been used to identify the dynamics of the main indicators of road transport in Eastern Europe. The method of correlation-regression analysis has been applied to determine the impact of increasing the length of roads on the turnover of the road freight transport and the number of employed population in this area. Results. It has been found that the increase in the employed population by 96% and increase in revenues from transportation and storage of goods, postal and courier services (turnover of the road freight transport - in the original language) in the field of road transport by 82% is explained by the change in transport infrastructure capacity by increasing length of highways. According to the correlation analysis, it has been revealed that there is a high direct dependence between the length of roads and increased revenues from transportation and storage of goods in the field of road transport, as well as between the length of roads and increasing employment in this area.

• Structural Monitoring and Maintenance
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• v.5 no.1
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• pp.67-78
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• 2018

The stability of cutting slope influences the safety of railway operation, and how to identify the stability of the slope quickly and determine the rational monitoring plan is a pressing problem at present. In this study, the attribute recognition model of risk assessment for high cutting slope stability in the heavy haul railway is established based on attribute mathematics theory, followed by the consequent monitoring scheme design. Firstly, based on comprehensive analysis on the risk factors of heavy haul railway loess slope, collapsibility, tectonic feature, slope shape, rainfall, vegetation conditions, train speed are selected as the indexes of the risk assessment, and the grading criteria of each index is established. Meanwhile, the weights of the assessment indexes are determined by AHP judgment matrix. Secondly, The attribute measurement functions are given to compute attribute measurement of single index and synthetic attribute, and the attribute recognition model was used to assess the risk of a typical heavy haul railway loess slope, Finally, according to the risk assessment results, the monitoring content and method of this loess slope were determined to avoid geological disasters and ensure the security of the railway infrastructure. This attribute identification- risk assessment- monitoring design mode could provide an effective way for the risk assessment and control of heavy haul railway in the loess plateau.

• Journal of Physiology & Pathology in Korean Medicine
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• v.31 no.4
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• pp.201-212
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• 2017

This study draws pattern differentiations of headache disorders on the ground of modern clinical applications and Korean medical literature. Categorization and symptoms of headache disorders are based on International Classification of Headache Disorders 3rd edition(beta version). And clinical papers are searched in China Academic Journals(CAJ) of China National Knowledge Infrastructure(CNKI). In the aspect of eight principle pattern identification, primary headache occurs due to lots of yang qi and has more inner pattern rather than exterior pattern, heat pattern rather than cold pattern, excess pattern rather than deficiency pattern. And primary headache is related with liver in the aspect of visceral pattern identification and blood stasis, wind and phlegm are relevant mechanisms. Migraine without aura is associated with ascendant hyperactivity of liver yang, phlegm turbidity, sunken spleen qi, wind-heat, blood deficiency or yin deficiency. Migraine with aura is mainly related with wind and it's major mechanisms are ascendant hyperactivity of liver yang, liver fire, yin deficiency of liver and kidney, blood deficiency or liver depression and qi stagnation. High repetition rate of tension-type headache can be identified as heat pattern or excess pattern. And trigeminal autonomic cephalalgias can also be accepted as heat pattern or excess pattern when the occurrence frequency is high and is relevant to combined pattern with excess pattern of external contraction and deficiency pattern of internal damage based on facial symptoms by external contraction and nervous and anxious status by liver deficiency. This study can be expected to be Korean medical basis of clinical practice guidelines on headache by proposing pattern identifications corresponding to the western classifications of headache disorders.

• The Journal of Korean Medicine Ophthalmology and Otolaryngology and Dermatology
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• v.33 no.2
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• pp.112-129
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• 2020

Objectives : The aim of this study is to explore the types of pattern identification (PI, 辨證) and the differential points of PI used for the treatment of psoriasis in Traditional Chinese Medicine (TCM) based on the Chinese references and to provide the evidences applying PI for the treatment of psoriasis in clinical practice. Methods : This study extracted patterns of psoriasis through database CNKI (China National Knowledge Infrastructure) and analysis the patterns and classification criteria of the patterns. Those examined in the study are dermal symptoms, general symptoms, formula and herbs which are different depending on the patterns. Results : Total 60 studies were selected and 44 pattern types were extracted from them. We categorized the main pattern types on psoriasis used in TCM as 'blood-heat syndrome(BHS, 血熱證)', blood-stasis syndrome(BSS, 血瘀證), and 'blood-dryness syndrome(BDS, 血燥證)', 'dampness-heat syndrome(DHS, 濕熱證)' and 'yang-deficiency syndrome(YDS, 陽虛證)'. Among these patterns, BHS was the most common. In TCM, the pattern of BHS tended to have skin symptoms and signs related to inflammatory erythema and heat. Both BSS and BDS were characterized by long disease duration and poor healing. In addition, DHS tended to have the skin symptoms and signs such as oozing and severe itching. The symptoms and signs related to coldness mainly showed in YDS. For PI criteria, 'qi-blood-essence criteria(氣血津液辨證)' and 'eight-doctrine criteria(八鋼辨證) are commonly used. Conclusions : Our findings show that each PI on psoriasis in TCM has different characteristics related to dermal and general symptoms or signs. Further studies are needed to develop the diagnostic tool of PI on psoriasis reflecting on clinical practices in Korean Medicine by referring to the findings of this study about PI on psoriasis in TCM.

• Korean Journal of Agricultural Science
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• v.34 no.1
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• pp.85-97
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• 2007

This study aimed at analyzing the economic effect of outdoor swimming pool investment using the reservoir's amenity resources. We focused on the identification of the amenity value of reservoir in the rural area and the economic evaluation for establishing This study aimed at analyzing the economic effect of outdoor swimming pool investment using the reservoir's amenity resources. We focused on the identification of the amenity value of reservoir in the rural area and the economic evaluation for establishing infrastructure such as swimming pool based on the reservoir's landscape value. To this end, we have conducted the case study on the outdoor swimming pool in connection with Go-Bok reservoir in Yeon-Gi county, Chungnam Province and estimated its income effect on the rural community by cost-benefit analysis method. The research results are as follows; 1) Outdoor swimming pool participants, with 11,581 visitors totaled to Yeon-gi county every year, was estimated to spend the worth of 58,446 thousand won paid for the agricultural product purchase and etc. 2) Internal rate return of the outdoor swimming pool project was estimated to 16.19%, which considered to be economically feasible comparing with 10% of current capital opportunity cost. Based on the results of this study, we suggest the following strategies for development of amenity value of swimming pool in connected with the reservoir; 1) Reservoir amenities should be well preserved even after construction of swimming pool lest losing amenity values while managing the facilities. 2) Measures to increase the marketing value of intangible reservoir's amenities through promotion should be established. 3) Effective program for more visitors with longer staying and more agricultural products sales should be designed.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2008.11a
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• pp.772-775
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• 2008

According to Korean Ministry of Land, Transportation and Maritime Affairs, total national SOC(Social Overhead Capital) stock comes up to 500 billion dollars. Until now, although the construction of SOC is more important than the maintenance of them in Korea, it is necessary to introduce of valuation based total asset management concept in nowadays. In this paper describes problems of exsiting data collection method, needs of Information Technology and introduction of RFID/USN(Radio Frequency IDentification/Ubiquitous Sensor Network) in data collection stage.

• Smart Structures and Systems
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• v.6 no.5_6
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• pp.561-578
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• 2010

This study proposes a multi-level damage localization strategy to achieve an effective damage detection system for civil infrastructure systems based on wireless sensors. The proposed system is designed for use of distributed computation in a wireless sensor network (WSN). Modal identification is achieved using the frequency-domain decomposition (FDD) method and the peak-picking technique. The ASH (angle-between-string-and-horizon) and AS (axial strain) flexibility-based methods are employed for identifying and localizing damage. Fundamentally, the multi-level damage localization strategy does not activate all of the sensor nodes in the network at once. Instead, relatively few sensors are used to perform coarse-grained damage localization; if damage is detected, only those sensors in the potentially damaged regions are incrementally added to the network to perform finer-grained damage localization. In this way, many nodes are able to remain asleep for part or all of the multi-level interrogations, and thus the total energy cost is reduced considerably. In addition, a novel distributed computing strategy is also proposed to reduce the energy consumed in a sensor node, which distributes modal identification and damage detection tasks across a WSN and only allows small amount of useful intermediate results to be transmitted wirelessly. Computations are first performed on each leaf node independently, and the aggregated information is transmitted to one cluster head in each cluster. A second stage of computations are performed on each cluster head, and the identified operational deflection shapes and natural frequencies are transmitted to the base station of the WSN. The damage indicators are extracted at the base station. The proposed strategy yields a WSN-based SHM system which can effectively and automatically identify and localize damage, and is efficient in energy usage. The proposed strategy is validated using two illustrative numerical simulations and experimental validation is performed using a cantilevered beam.

• Smart Structures and Systems
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• v.30 no.6
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• pp.673-686
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• 2022

Deep learning algorithms for Structural Health Monitoring (SHM) have been extracting the interest of researchers and engineers. These algorithms commonly used loss functions and evaluation indices like the mean square error (MSE) which were not originally designed for SHM problems. An updated loss function which was specifically constructed for deep-learning-based structural damage detection problems has been proposed in this study. By tuning the coefficients of the loss function, the weights for damage localization and quantification can be adapted to the real situation and the deep learning network can avoid unnecessary iterations on damage localization and focus on the damage severity identification. To prove efficiency of the proposed method, structural damage detection using convolutional neural networks (CNNs) was conducted on a truss bridge model. Results showed that the validation curve with the updated loss function converged faster than the traditional MSE. Data augmentation was conducted to improve the anti-noise ability of the proposed method. For reducing the training time, the normalized modal strain energy change (NMSEC) was extracted, and the principal component analysis (PCA) was adopted for dimension reduction. The results showed that the training time was reduced by 90% and the damage identification accuracy could also have a slight increase. Furthermore, the effect of different modes and elements on the training dataset was also analyzed. The proposed method could greatly improve the performance for structural damage detection on both the training time and detection accuracy.

• Smart Structures and Systems
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• v.29 no.1
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• pp.237-250
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• 2022

Structural health monitoring (SHM) plays an important role in ensuring the safety and functionality of critical civil infrastructure. In recent years, numerous researchers have conducted studies to develop computer vision and machine learning techniques for SHM purposes, offering the potential to reduce the laborious nature and improve the effectiveness of field inspections. However, high-quality vision data from various types of damaged structures is relatively difficult to obtain, because of the rare occurrence of damaged structures. The lack of data is particularly acute for fatigue crack in steel bridge girder. As a result, the lack of data for training purposes is one of the main issues that hinders wider application of these powerful techniques for SHM. To address this problem, the use of synthetic data is proposed in this article to augment real-world datasets used for training neural networks that can identify fatigue cracks in steel structures. First, random textures representing the surface of steel structures with fatigue cracks are created and mapped onto a 3D graphics model. Subsequently, this model is used to generate synthetic images for various lighting conditions and camera angles. A fully convolutional network is then trained for two cases: (1) using only real-word data, and (2) using both synthetic and real-word data. By employing synthetic data augmentation in the training process, the crack identification performance of the neural network for the test dataset is seen to improve from 35% to 40% and 49% to 62% for intersection over union (IoU) and precision, respectively, demonstrating the efficacy of the proposed approach.