• Journal of Korea Society of Industrial Information Systems
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• v.24 no.1
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• pp.45-63
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• 2019

The number of domestic construction company is expanding every year while the construction workers' exposure to disaster risk is increasing due to technological advancements and popularity of high-rise buildings. In particular, the industry faces greater fatalities and severe large scale accidents because of construction industry characteristics including influx of foreign workers with different language and culture, large number of aged workers, outsourcing, high place work, heavy machine construction. The construction industry is labor-intensive, which is to be completed under given timeline and consists of unique working environment with a lot of night shifts. In addition, when a fixed construction budget is not secured, there is less investment in safety management resulting in poor risk management at the construction site. Taking account that the construction industry has higher accident risk rate and fatality rate, risky and unique working environment, and various labor pool from foreign to aged workers, preemptive safety management through risk factor identification is a mandatory requirement for the construction industry and site. The study analyzes about 8,500 cases of construction accidents that occurred over the past 10 years and identified risk factor by construction industry sector to secure a systematic insight for risk management. Based on interrelation analysis between accident types, work types, original cause materials and assailing materials, there is correlation between each analysis factor and work industry. Especially for work types, there is great correlation between work tasks and industry type. For reinforced concrete and earthwork are among the most frequent types of accidents, and they are not only high in frequency of accidents, but also have a high risk in categories of occurrence.

• Journal of the Microelectronics and Packaging Society
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• v.31 no.1
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• pp.16-22
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• 2024

Along with the high interest in electric vehicles and new renewable energy, there is a growing demand to apply lithium-ion batteries in the construction equipment industry. The capacity of heavy construction equipment that performs various tasks at construction sites is rapidly decreasing. Therefore, it is essential to accurately predict the state of batteries such as SOC (State of Charge) and SOH (State of Health). In this paper, the errors between actual electrochemical measurement data and estimated data were compared using the Dual Extended Kalman Filter (DEKF) algorithm that can estimate SOC and SOH at the same time. The prediction of battery charge state was analyzed by measuring OCV at SOC 5% intervals under 0.2C-rate conditions after the battery cell was fully charged, and the degradation state of the battery was predicted after 50 cycles of aging tests under various C-rate (0.2, 0.3, 0.5, 1.0, 1.5C rate) conditions. It was confirmed that the SOC and SOH estimation errors using DEKF tended to increase as the C-rate increased. It was confirmed that the SOC estimation using DEKF showed less than 6% at 0.2, 0.5, and 1C-rate. In addition, it was confirmed that the SOH estimation results showed good performance within the maximum error of 1.0% and 1.3% at 0.2 and 0.3C-rate, respectively. Also, it was confirmed that the estimation error also increased from 1.5% to 2% as the C-rate increased from 0.5 to 1.5C-rate. However, this result shows that all SOH estimation results using DEKF were excellent within about 2%.