• Computational Structural Engineering
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• v.33 no.3
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• pp.7-15
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• 2020

• Journal of the Society of Disaster Information
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• v.20 no.1
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• pp.106-116
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• 2024

Purpose: This paper conducts IoT and CCTV-based safety monitoring to analyze accidents and potential risks occurring at construction sites, and detect and analyze risks such as falls and collisions or abnormalities and to establish a system for early warning using devices like a walkie-talkie and chatbot service. Method: A safety management service model is presented through smart construction technology case studies at the construction site and review a relevant literature analysis. Result: According to 'Construction Accident Statistics,' in 2021, there were 26,888 casualties in the construction industry, accounting for 26.3% of all reported accidents. Fatalities in construction-related accidents amounted to 417 individuals, representing 50.5% of all industrial accident-related deaths. This study suggests implementing AI chatbot services for construction site safety management utilizing IoT-based health monitoring technologies in smart construction practices. Construction sites where stakeholders such as workers participate were demonstrated by implementing an artificial intelligence chatbot system by selecting major risk areas within the workplace, such as scaffolding processes, openings, and access to hazardous machinery. Conclusion: The possibility of commercialization was confirmed by receiving more than 90 points in the satisfaction survey of participating workers regarding the empirical results of the artificial intelligence chatbot service at construction sites.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.33 no.2
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• pp.113-119
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• 2020

The IoT-based sensor network is one of the methods that can be efficiently applied to maintain the facilities, such as bridges, at a low cost. In this study, based on LoRa LPWAN, one of the IoT communications, sensor board for cable tension monitoring, data acquisition board for constructing sensor network along with existing measurement sensors, are developed to create bridge structural health monitoring system. In addition, we designed and manufactured a smart sensor node for LoRa communication and established a sensor network for monitoring. Further, we constructed a test bed at the Yeonggwang Bridge to verify the performance of the system. The test bed verification results suggested that the LoRa LPWAN-based sensor network can be applied as one of the technologies for monitoring the bridge structure soundness; this is excellent in terms of data rate, accuracy, and economy.

• Journal of the Society of Disaster Information
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• v.18 no.4
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• pp.847-860
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• 2022

Purpose: The purpose of this paper is to present a basic study on the development of a self-generation infrastructure for monitoring the health of harbour structures. Method: By developing a self-generation system and fiber optic sensors for seawater, the study provides basic research data on port structure health monitoring. Result: Through sunlight simulation analysis, 4-5 hours of sunlight can be secure in the domestic environment. Through this, the optical splitter (Introgate) that collects the raw data from the FBG sensor applicable to seawater, the MCU that calculates it, the IoT module with wireless communication functionality, the monitoring server and the supply system are set up. Conclusion: Monitoring port structures directly with fiber optic probes (FBG) and the possibility of using selfpowered systems were confirmed.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.4
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• pp.751-756
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• 2018

In smart factories and industrial IoT, all facilities in a factory are monitored over the Internet, thereby facility can reduce the downtime and increase the availiability by preventive maintenance before it breaks down. The abnormal conditions of the major facilities in the plant are caused by abnormal temperature rise, vibration, and variations in noise. Consequently, it is critical to develop a very small smart device that is easily installed in a small space to enable real-time monitoring of the vibration status of the facility. In this study, smart devices were developed for smart factory fault prediction and robustness management using ultra small micro-controllers with WiFi capabilities and MEMS acceleration sensors.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.4
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• pp.543-550
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• 2018

The rapid growth of IoT technology induced by the fourth industrial revolution has resulted in research into various types of wireless sensors, and applications based on this technology are prevalent in many areas. However, among the various sites where this technology is used, railway bridges and tunnels with lengths of tens of kilometers have problems with data acquisition, due to the signal noise induced by the long distance measurement and EMI induced by the high voltage power feeding system, when conventional electric sensors are used. To overcome these problems, many studies on fiber optic sensors have been conducted as a substitute for the conventional electric sensors. However, restrictions on the types of fiber optic sensors have limited their application in railways. For this reason, a hybrid measurement system with IoT based wireless data communication, in which both electric and fiber optic sensors can be applied simultaneously, has been developed. In this study, in order to evaluate the applicability of the hybrid measurement system developed in the previous study, a real-time test for 4 types of measurement environments, which reflect possible railway sites, is performed. As a result, it was confirmed that the signals from both the electric and fiber optic sensors, which were acquired at a remote area in real-time, showed good agreement with each other and that this measurement system has the potential to handle sensors with a sampling rate of 2.5 kHz. In the future, it is expected that the IoT-based hybrid measurement system will contribute to the improvement of structural safety by enabling real-time structural health monitoring when applied to various measurement sites.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.6
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• pp.517-524
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• 2017

The objective of this research is to improve the efficiency of data collection from many machine components on smart factory floors using IoT(Internet of things) techniques and cloud platform, and to make it easy to update outdated diagnostic schemes through online deployment methods from cloud resources. The short-term analysis is implemented by a micro-controller, and it includes machine-learning algorithms for inferring snapshot information of the machine components. For long-term analysis, time-series and high-dimension data are used for root cause analysis by combining a cloud platform and multivariate analysis techniques. The diagnostic results are visualized in a web-based display dashboard for an unconstrained user access. The implementation is demonstrated to identify its performance in data acquisition and analysis for rotating machinery.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.439-439
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• 2023

근래 국내에서는 기후변화로 인한 국지성 호우가 점점 늘어나는 추세로 급격한 하천 수위상승 및 유량 증가로 인해 지속적으로 홍수피해가 발생하고 있으며, 이를 예방하기 위한 실시간 자료수집의 중요성이 증대되고 있다. 이러한 사회적 환경을 고려하여 우리는 물 순환에 관한 자료를 실시간으로 수집하고 홍수예보를 위한 수문조사시설을 설치하여 운영하고 있으나, 대부분 하천과 인접한 곳에 설치되는 시설 특성상 시스템 오류, 전원 이상 발생 등 다양한 요인으로 발생하는 자료 결측·손실에 즉각적인 조치가 어려운 실정이다. 이에, 현장 기반 시설의 안정적인 운영을 통한 연속성 있는 자료 제공을 위해 수문조사시설 중 하천 내 설치된 유량측정시스템에 ICT·사물인터넷(IoT, Internet of Things)을 적용하여 현장 환경-정보 등 언택트(non-contact) 모니터링을 통해 실시간 점검을 수행하였다. 그 결과 2022년 기준 총 508회(현장점검 358회) 점검 중 150회 원격점검을 수행하였고, 이중 74회 즉각 점검 및 복구 조치가 이루어져 점검 시간 단축을 통한 자료 결측 최소화, 현장점검 최소화를 통해 효율적인 시설 운영이 가능하도록 하였다. 또한, 점검을 위해 현장 이동 시 발생하는 이산화탄소 배출량 저감으로 탄소중립 효과도 나타낼 수 있었다. 코로나바이러스감염증-19 이후 사회환경 패러다임 전환에 따라 비대면 활성화, 탄소중립, 안전하고 건전한 사회환경 조성 등과 같이 대면 위주로 운영되는 현장 시설의 관리 방향 또한 사회적 상황을 고려하여 효율적인 시설물 운영, 예산 절감, 자료의 연속성 확보 등을 위해 적극적인 운영 방향의 전환이 필요하다고 판단된다.