• The Journal of Engineering Geology
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• v.28 no.3
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• pp.349-365
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• 2018

The strain and acoustic emission (AE) signals of Pocheon granite were measured during uniaxial compression tests to investigate microcrack formation and damage. Crack closure, initiation, and damage stresses of each sample were determined through an analysis of the crack volumetric strain and stiffness. The samples experienced four damage stages according to stress levels: stage 1 = crack closure stage; stage 2 = elastic stage; stage 3 = crack initiation stage; stage 4 = crack damage stage. At least 75% of all AE signals occurred in stages 3 and 4, and different AE parameters were detected in the four stress stages. Rise time, count, energy, and duration clearly showed a tendency to gradually increase with the damage stress stage. In particular, the rise time, energy, and duration increased by at least 95% in stage 4 as compared with stage 1. However, the maximum amplitude showed a smaller increase, and the average frequency decreased slightly at higher stages. These results indicate that as the degree of rock damage increases, the crack size grows larger. The crack types corresponding to the AE signals were determined using the relationship between RA (Rise time / Amplitude) values and average frequencies. Tension cracking was dominant in all stress stages. Shear cracking was rare in stages 1 and 2, but increased in stages 3 and 4. These results are consistent with previous studies that reported cracking begins after samples have already been damaged. Our study shows that the state of rock damage can be investigated solely through an analysis of AE parameters when rocks are under compressive stress. As such, this methodology is suitable for understanding and monitoring the stress state of bedrock.

• Smart Structures and Systems
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• v.30 no.6
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• pp.601-612
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• 2022

The prediction of structural mechanical behaviors is vital important to early perceive the abnormal conditions and avoid the occurrence of disasters. Especially for underground engineering, complex geological conditions make the structure more prone to disasters. Aiming at solving the problems existing in previous studies, such as incomplete consideration factors and can only predict the continuous performance, the deep attention fused temporal convolution network (DATCN) is proposed in this paper to predict the spatial mechanical behaviors of structure, which integrates both the temporal effect and spatial effect and realize the cross-time prediction. The temporal convolution network (TCN) and self-attention mechanism are employed to learn the temporal correlation of each monitoring point and the spatial correlation among different points, respectively. Then, the predicted result obtained from DATCN is compared with that obtained from some classical baselines, including SVR, LR, MLP, and RNNs. Also, the parameters involved in DATCN are discussed to optimize the prediction ability. The prediction result demonstrates that the proposed DATCN model outperforms the state-of-the-art baselines. The prediction accuracy of DATCN model after 24 hours reaches 90 percent. Also, the performance in last 14 hours plays a domain role to predict the short-term behaviors of the structure. As a study case, the proposed model is applied in an underwater shield tunnel to predict the stress variation of concrete segments in space.

• Journal of Korea Water Resources Association
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• v.51 no.11
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• pp.1011-1020
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• 2018

This study aims to develop a drought monitoring scheme based on groundwater which can be exploit for water supply under drought stress. In this context, groundwater level can be used as a proxy for better understanding the temporal evolution of drought state. First, kernel density estimator is presented in the monthly groundwater level over the entire national groundwater stations. The estimated cumulative distribution function is then utilized to map the monthly groundwater level into the standardized groundwater level index (SGI). The SGI for each station was eventually converted into the index for major cities through the Thiessen polygon approach. We provide a drought classification for a given SGI to better characterize the degree of drought condition. Ultimately, we conclude that the proposed monitoring framework enables a more reliable estimation of the drought stress, especially for a limited water supply area.

• Geomechanics and Engineering
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• v.14 no.5
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• pp.459-466
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• 2018

By conducting uniaxial loading cycle tests on the coal rock with outburst proneness, the dilatation characteristics at different loading rates were investigated. Under uniaxial loading and unloading, the lateral deformation of coal rock increased obviously before failure, leading to coal dilatation. Moreover, the post-unloading recovery of the lateral deformation was rather small, suggesting the onset of an accelerated failure. As the loading rate increased further, the ratio of the stress at the dilatation critical point to peak-intensity increased gradually, and the pre-peak volumetric deformation decreased with more severe post-peak damage. Based on the laboratory test results, the lateral deformation of the coals at different depths in the #1302 isolated coal pillars, Yangcheng Coal Mine, was monitored using wall rock displacement meter. The field monitoring result indicates that the coal lateral displacement went through various distinct stages: the lateral displacement of the coals at the depth of 2-6 m went through an "initial increase-stabilize-step up-plateau" series. When the coal wall of the working face was 24-18 m away from the measuring point, the coals in this region entered the accelerated failure stage; as the working face continued advancing, the lateral displacement of the coals at the depth over 6 m increased steadily, i.e., the coals in this region were in the stable failure stage.

• The Korean Journal of Emergency Medical Services
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• v.27 no.1
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• pp.79-90
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• 2023

Purpose: The study attempted to improve the health promotion behavior of university students by identifying the factors that affect health promotion behavior and by checking depression, anxiety, and stress levels of university students after the COVID-19 pandemic. Methods: We collected data using a structured questionnaire targeting 170 university students in C-province between December 1 and December 31, 2022. Results: Health promotion behavior had a significantly negative correlation with Depression (r=-.361, p<.001), Anxiety (r=-.191, p=.012), and Stress (r=-.301, p<.001), respectively. The influencing factors of health promotion behavior are gender (r=0.184, p<.001) and depression (r=-0.303, p<.001); the explanatory power is accounted for 15%. Conclusion: A practical method with counseling programs and mental health support services for early detection of risk groups by periodically monitoring the depression state of university students requires practicing health promotion behavior. Therefore, active support and attention should be provided to manage the mental health of university students.

• Smart Structures and Systems
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• v.14 no.2
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• pp.209-224
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• 2014

With more and more built long-term structural health monitoring (SHM) systems, it has been considered to apply monitored data to learn the reliability of bridges. In this paper, based on a long-term SHM system, especially in which the sensors were embedded from the beginning of the construction of the bridge, a method to calculate the localized reliability around an embedded sensor is recommended and implemented. In the reliability analysis, the probability distribution of loading can be the statistics of stress transferred from the monitored strain which covered the effects of both the live and dead loads directly, and it means that the mean value and deviation of loads are fully derived from the monitored data. The probability distribution of resistance may be the statistics of strength of the material of the bridge accordingly. With five years' monitored strains, the localized reliabilities around the monitoring sensors of a bridge were computed by the method. Further, the monitored stresses are classified into two time segments in one year period to count the loading probability distribution according to the local climate conditions, which helps us to learn the reliability in different time segments and their evolvement trends. The results show that reliabilities and their evolvement trends in different parts of the bridge are different though they are all reliable yet. The method recommended in this paper is feasible to learn the localized reliabilities revealed from monitored data of a long-term SHM system of bridges, which would help bridge engineers and managers to decide a bridge inspection or maintenance strategy.

• Tunnel and Underground Space
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• v.15 no.1 s.54
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• pp.1-8
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• 2005

Measurement of convergence was/is carried out manually throughout the world for tunnels under construction. However, manual method has certain limitations in terms of applicability for the tunnels in operation. This paper describes state of the art of convergence monitoring systems which are available for measuring displacement of existing tunnels. These technologies are analyzed as follows: 1 The Sofo system using the fiber optic sensors has been applied to the stress measurement of the tunnel lining. It has not yet been used for the monitoring of tunnel convergence because of its cost and reliability 2. A TPMS(Tunnel Profile Monitoring System) using tilt sensors and displacement sensors is used for the convergence monitoring of highway tunnels, subway tunnels and underground ducts. 3. A BCS(Bassett Convergence System) using a pair of tilt sensors can be used for the convergence monitoring of tunnels, however the accuracy of the measurement has to be improved because it uses AC input voltage during data acquisition. The system has to be validated before it can be applied to the tunnels in operation. Convergence monitoring systems using TPMS and/or BCS are recommended to be evaluated and improved by a series or tests in tunnels under construction in order to be applied to the main measuring section and the tunnels in operation.

• Journal of Animal Science and Technology
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• v.66 no.1
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• pp.31-56
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• 2024

Pig farming, a vital industry, necessitates proactive measures for early disease detection and crush symptom monitoring to ensure optimum pig health and safety. This review explores advanced thermal sensing technologies and computer vision-based thermal imaging techniques employed for pig disease and piglet crush symptom monitoring on pig farms. Infrared thermography (IRT) is a non-invasive and efficient technology for measuring pig body temperature, providing advantages such as non-destructive, long-distance, and high-sensitivity measurements. Unlike traditional methods, IRT offers a quick and labor-saving approach to acquiring physiological data impacted by environmental temperature, crucial for understanding pig body physiology and metabolism. IRT aids in early disease detection, respiratory health monitoring, and evaluating vaccination effectiveness. Challenges include body surface emissivity variations affecting measurement accuracy. Thermal imaging and deep learning algorithms are used for pig behavior recognition, with the dorsal plane effective for stress detection. Remote health monitoring through thermal imaging, deep learning, and wearable devices facilitates non-invasive assessment of pig health, minimizing medication use. Integration of advanced sensors, thermal imaging, and deep learning shows potential for disease detection and improvement in pig farming, but challenges and ethical considerations must be addressed for successful implementation. This review summarizes the state-of-the-art technologies used in the pig farming industry, including computer vision algorithms such as object detection, image segmentation, and deep learning techniques. It also discusses the benefits and limitations of IRT technology, providing an overview of the current research field. This study provides valuable insights for researchers and farmers regarding IRT application in pig production, highlighting notable approaches and the latest research findings in this field.

• Proceedings of the Korean Society for Applied Microbiology Conference
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• 2000.04a
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• pp.94-99
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• 2000

Bioluminescence is being used as a prevailing reporter of gene expression in microorganisms and mammalian cells. Bacterial bioluminescence draws special attention from environmental biotechnologists since it has many advantageous characteristics, such as no requirement of extra substractes, highly sensitive, and on-line measurability. Using bacterial bioluminescence as a reporter of toxicity has replaced the classical toxicity monitoring technology of using fish or daphnia with a cutting-edge technology. Fusion of bacterial stress promoters, which control the transcription of stress genes corresponding to heat-shock, DNA-, or oxidative-damaging stress, to the bacterial lux operon has resulted in the development of novel toxicity biosensors with a short measurement time, enhanced sensitivity, and ease and convenient usage. Therefore, these recombinant bioluminescent bacteria are expected to induce bacterial bioluminescence when the cells are exposed to stressful conditions, including toxic chemicals. We have used these recombinant bioluminescent bacteria in order to develop toxicity biosensors in a continuous, portable, or in-situ measurement from for air, water, and soil environments. All the data obtained from these toxicity biosensors for these environments were found to be repeatable and reproducible, and the minimum detection level of toxicity was found to be ppb (part per billion) levels for specific chemicals.

• Geomechanics and Engineering
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• v.25 no.1
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• pp.49-58
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• 2021

The resistance of soil to the tractive force of flowing water is one of the essential parameters for the stability of the soil when directly exposed to the movement of water such as in rivers and ocean beds. Biopolymers, which are new to sustainable geotechnical engineering practices, are known to enhance the mechanical properties of soil. This study addresses the surface erosion resistance of river-sand treated with several biopolymers that originated from micro-organisms, plants, and dairy products. We used a state-of-the-art erosion function apparatus with P-wave reflection monitoring. Experimental results have shown that biopolymers significantly improve the erosion resistance of soil surfaces. Specifically, the critical shear stress (i.e., the minimum shear stress needed to detach individual soil grains) of biopolymer-treated soils increased by 2 to 500 times. The erodibility coefficient (i.e., the rate of increase in erodibility as the shear stress increases) decreased following biopolymer treatment from 1 × 10-2 to 1 × 10-6 times compared to that of untreated river-sands. The scour prediction calculated using the SRICOS-EFA program has shown that a height of 14 m of an untreated surface is eroded during the ten years flow of the Nakdong River, while biopolymer treatment reduced this height to less than 2.5 m. The result of this study has demonstrated the possibility of cross-linked biopolymers for river-bed stabilization agents.