• Title/Summary/Keyword: property damage

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Experimental Evaluation of Pullout Strength of Long-Rawlplug Screw Anchor according to the Compressive Strength of Concrete and Embedded Length (콘크리트 압축강도 및 매입깊이에 따른 긴 칼블럭앵커의 뽑힘강도 평가)

  • Park, Jun-Ryeol;Yang, Keun-Hyeok;Kim, Sang-Hee;Oh, Na-Kyung
    • Journal of the Korea institute for structural maintenance and inspection
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    • v.25 no.6
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    • pp.84-89
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    • 2021
  • In 2017, the Gyeongju earthquake caused many casualties and considerable property damage by overturning and dropping blocks and bricks. Various reinforcement techniques were proposed, but some problems, such as short length or difficult construction, were encountered. Therefore, this study proposes a long-rawlplug screw anchor to improve the existing rawlplug anchor and conducts an experiment to evaluate the pullout strength. Variables in the pullout test were the compressive strength of concrete and the embedded length of the long-rawlplug screw anchor. According to the results, the pullout strength of the long-rawlplug screw anchor increased as the compressive strength of concrete increased, and they were not affected by the embedded length. Rather, it was found that the screw length of the long-rawlplug was important to the pullout strength.

A Study on Fire Detection in Ship Engine Rooms Using Convolutional Neural Network (합성곱 신경망을 이용한 선박 기관실에서의 화재 검출에 관한 연구)

  • Park, Kyung-Min;Bae, Cherl-O
    • Journal of the Korean Society of Marine Environment & Safety
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    • v.25 no.4
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    • pp.476-481
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    • 2019
  • Early detection of fire is an important measure for minimizing the loss of life and property damage. However, fire and smoke need to be simultaneously detected. In this context, numerous studies have been conducted on image-based fire detection. Conventional fire detection methods are compute-intensive and comprise several algorithms for extracting the flame and smoke characteristics. Hence, deep learning algorithms and convolution neural networks can be alternatively employed for fire detection. In this study, recorded image data of fire in a ship engine room were analyzed. The flame and smoke characteristics were extracted from the outer box, and the YOLO (You Only Look Once) convolutional neural network algorithm was subsequently employed for learning and testing. Experimental results were evaluated with respect to three attributes, namely detection rate, error rate, and accuracy. The respective values of detection rate, error rate, and accuracy are found to be 0.994, 0.011, and 0.998 for the flame, 0.978, 0.021, and 0.978 for the smoke, and the calculation time is found to be 0.009 s.

Hazard Analysis Process Based on STPA Using SysML (SysML을 이용한 STPA 기반의 위험원 분석 프로세스)

  • Choi, Na-yeon;Lee, Byong-gul
    • Journal of Internet Computing and Services
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    • v.20 no.3
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    • pp.1-11
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    • 2019
  • Today's software systems are becoming larger and more complicated, and the risk of accidents and failures have also grown larger. Software failures and accidents in industrial fields such as automobiles, nuclear power plants, railroad industries, etc. may lead to severe damage of property and human life. The safety-related international standards, such as IEC 61508 have been established and applied to industries for decades. The safety life cycle specified in the standards emphasize the activities to develop safety requirements through hazard and risk analysis in the early stage of software development. In this paper, we propose 'Hazard Analysis Process based on STPA using SysML' in order to ensure the safety of software at the early stage of software development. The proposed hazard analysis can be effectively performed minimizing the loss of hazard by using the BDD and the IBD of SysML to define the control structure of a system. The proposed method also improves the specification of the safety constraints(requirement) by using SD. As a result, it is possible to identify the hazard without missing and identify the hazard scenarios in detail, and safety can be sufficiently ensured in the early stage of software development.

Review on Design of Underground Mine Openings in Korea and Overseas (국내외 지하광산 갱도설계 현황에 대한 고찰)

  • Yoon, Dong-Ho;Song, Jae-Joon
    • Tunnel and Underground Space
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    • v.29 no.1
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    • pp.30-37
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    • 2019
  • Some leading countries in mining have a very quantitative guideline for underground mine opening design which is useful to minimize mine hazards such as rockfall and collapse. Those hazards sometimes can cause a huge damage on human life and property in the mines. Construction guidelines of underground mines in Korea consist of qualitative and general expressions although the workers' safety rules and guides are well provided. Recently, mining operations in Korea are going underground due to the environmental regulations and resource depletion at shallow depth, and therefore there is a growing demand on a specialized and systematic guideline for mine opening design securing the underground stability. In this paper, current status of mining industry, research trends, and mining guidelines in Korea and overseas have been reviewed to give an insight into developing a new Korean guideline for underground mine design.

Current and Future Status of GIS-based Landslide Susceptibility Mapping: A Literature Review

  • Lee, Saro
    • Korean Journal of Remote Sensing
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    • v.35 no.1
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    • pp.179-193
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    • 2019
  • Landslides are one of the most damaging geological hazards worldwide, threating both humans and property. Hence, there have been many efforts to prevent landslides and mitigate the damage that they cause. Among such efforts, there have been many studies on mapping landslide susceptibility. Geographic information system (GIS)-based techniques have been developed and applied widely, and are now the main tools used to map landslide susceptibility. We reviewed the status of landslide susceptibility mapping using GIS by number of papers, year, study area, number of landslides, cause, and models applied, based on 776 articles over the last 20 years (1999-2018). The number of studies published annually increased rapidly over time. The total study area spanned 65 countries, and 47.7% of study areas were in China, India, South Korea, and Iran, where more than 500 landslides, 27.3% of all landslides, have occurred. Slope (97.6% of total articles) and geology (82.7% of total articles) were most often implicated as causes, and logistic regression (26.9% of total articles) and frequency ratio (24.7% of total article) models were the most widely used models. We analyzed trends in the causes of and models used to simulate landslides. The main causes were similar each year, but machine learning models have increased in popularity over time. In the future, more study areas should be investigated to improve the generalizability and accuracy of the results. Furthermore, more causes, especially those related to topography and soil, should be considered and more machine learning models should be applied. Finally, landslide hazard and risk maps should be studied in addition to landslide susceptibility maps.

Real-Time Fire Detection based on CNN and Grad-CAM (CNN과 Grad-CAM 기반의 실시간 화재 감지)

  • Kim, Young-Jin;Kim, Eun-Gyung
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.22 no.12
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    • pp.1596-1603
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    • 2018
  • Rapidly detecting and warning of fires is necessary for minimizing human injury and property damage. Generally, when fires occur, both the smoke and the flames are generated, so fire detection systems need to detect both the smoke and the flames. However, most fire detection systems only detect flames or smoke and have the disadvantage of slower processing speed due to additional preprocessing task. In this paper, we implemented a fire detection system which predicts the flames and the smoke at the same time by constructing a CNN model that supports multi-labeled classification. Also, the system can monitor the fire status in real time by using Grad-CAM which visualizes the position of classes based on the characteristics of CNN. Also, we tested our proposed system with 13 fire videos and got an average accuracy of 98.73% and 95.77% respectively for the flames and the smoke.

Application of Satellite Imagery to Research on Earthquake and Volcano (지진·화산 연구에 대한 위성영상 활용)

  • Lee, Won-Jin;Park, Sun-Cheon;Kim, Sang-Wan;Lee, Duk Kee
    • Korean Journal of Remote Sensing
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    • v.34 no.6_4
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    • pp.1469-1478
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    • 2018
  • Earthquakes and volcanic eruptions are disaster that causes billions of dollars in property damage and the loss of human life. Therefore, it is required to effectively monitor earthquakes and volcanoes. With the increase of satellite data, researches on earthquake and volcano using satellite imagery has been improved. Satellite images can be divided into three types i.e. optical, thermal, Synthetic Aperture Radar (SAR) and each image has different characteristics. In this article, we summarized its advantages and disadvantages of each type of satellite image. Moreover, we investigated the previous researches about earthquake and volcano using satellite images. Finally, we suggest application method to respond earthquake and volcano disaster using satellite images.

Modeling for Debris Flow Behavior on Expressway Using FLO-2D (FLO-2D를 이용한 고속도로에서의 토석류 거동 모델링)

  • Lim, Jae-Tae;Kim, Byunghyun
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.39 no.2
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    • pp.263-272
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    • 2019
  • This study demonstrates the applicability of the FLO-2D for the influence analysis of the debris flow on expressway. To do this, the behavior of debris flow on the expressway was reproduced by applying the FLO-2D to actual generated debris flow. The study area is a part of the Deokyusan Service Area on the Daejon-Jinju Expressway, where traffic was blocked for 24 hours due to the debris flow in August 2005. Geographical analysis with GIS, hydrological analysis with HEC-HMS, and estimation of the amount of debris flow were carried out using field survey and soil property test data. Then, the optimum parameter combination of FLO-2D was selected through the parameter sensitivity analysis, and the behavior analysis of debris flow on expressway was applied. The comparison of the predictions with the observations shows the availability of FLO-2D for the behavior analysis of debris flow on the expressway.

Dynamic quantitative risk assessment of accidents induced by leakage on offshore platforms using DEMATEL-BN

  • Meng, Xiangkun;Chen, Guoming;Zhu, Gaogeng;Zhu, Yuan
    • International Journal of Naval Architecture and Ocean Engineering
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    • v.11 no.1
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    • pp.22-32
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    • 2019
  • On offshore platforms, oil and gas leaks are apt to be the initial events of major accidents that may result in significant loss of life and property damage. To prevent accidents induced by leakage, it is vital to perform a case-specific and accurate risk assessment. This paper presents an integrated method of Ddynamic Qquantitative Rrisk Aassessment (DQRA)-using the Decision Making Trial and Evaluation Laboratory (DEMATEL)-Bayesian Network (BN)-for evaluation of the system vulnerabilities and prediction of the occurrence probabilities of accidents induced by leakage. In the method, three-level indicators are established to identify factors, events, and subsystems that may lead to leakage, fire, and explosion. The critical indicators that directly influence the evolution of risk are identified using DEMATEL. Then, a sequential model is developed to describe the escalation of initial events using an Event Tree (ET), which is converted into a BN to calculate the posterior probabilities of indicators. Using the newly introduced accident precursor data, the failure probabilities of safety barriers and basic factors, and the occurrence probabilities of different consequences can be updated using the BN. The proposed method overcomes the limitations of traditional methods that cannot effectively utilize the operational data of platforms. This work shows trends of accident risks over time and provides useful information for risk control of floating marine platforms.

Analysis and Evaluation of the Distributed Control Braking System of Long Freight Car Brakes (장대화물열차의 분산제어 제동 시 연결기에 발생하는 충격력 해석 및 분석)

  • Cho, Byung Jin;Lee, Jeong Jun;Shim, Jae Seok;Koo, Jeong Seo;Mun, Hyung Seok
    • Journal of the Korean Society of Manufacturing Process Engineers
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    • v.18 no.2
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    • pp.65-72
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    • 2019
  • In long freight trains, there is a brake time delay in neighboring freight cars, which causes damage and fractures in the couplers, especially at their knuckle. A problem in the couplers of the cars can cause derailment and damages of human life and property. In this study, maximum forces on the couplers are studied when a long freight car brakes with the brake delay time and coupler gap. We make a dynamic model of 50 freight cars and couplers, applying contact between the couplers and a characteristic curve to express the force and displacement of the buffers using SIMPACK, which is a multi-body dynamics program. We use EN 14531-2, which is a standard of freight car brakes, to verify the dynamic model. Then, we compare the analyzed impact force with the coupler knuckle standard after applying the two carriages of a locomotive in the model based on the dispersed double head control system. The result shows that all coupler gap conditions satisfy the infinite lifetime of the material when the brake delay time is 0.1 second.