Zobrazeno 1 - 10
of 66
pro vyhledávání: '"Ji-Myong Kim"'
Publikováno v:
Heliyon, Vol 10, Iss 11, Pp e32215- (2024)
Despite ongoing safety efforts, construction sites experience a concerningly high accident rate. Notwithstanding that policies and research to reduce the risk of accidents in the construction industry have been active for a long time, the accident ra
Externí odkaz:
https://doaj.org/article/ecd1d54109ed415fa91ea8c6a9d31864
Publikováno v:
Heliyon, Vol 10, Iss 1, Pp e23324- (2024)
Climate crises such as extreme weather events, natural disasters and climate change caused by climate transformations are causing much damage worldwide enough to be called a climate catastrophe. The private sector and the government across industries
Externí odkaz:
https://doaj.org/article/d9996af65daa43aba250cf405d659644
Publikováno v:
Geomatics, Natural Hazards & Risk, Vol 13, Iss 1, Pp 538-567 (2022)
The seismic site effect is critical in designing structures and the estimation of earthquake damage to existing structures, particularly in seismically active regions. To assess site-specific earthquake risks and design earthquake-resistant structure
Externí odkaz:
https://doaj.org/article/384f7ad54bc8486280861ad7576ad1e8
Publikováno v:
Frontiers in Earth Science, Vol 11 (2023)
The goal of this study is to suggest an approach to predict building loss due to typhoons using a deep learning algorithm. Due to the influence of climate change, the frequency and severity of typhoons gradually increase and cause exponential destruc
Externí odkaz:
https://doaj.org/article/90a49f2a7e8c418692f96c7361c7e22a
Autor:
Ji-Myong Kim, Kwang-Kyun Lim
Publikováno v:
Applied Sciences, Vol 14, Iss 5, p 1697 (2024)
Railroads play a pivotal role in the Korean national economy, necessitating a thorough understanding of factors influencing accidents for effective mitigation strategies. Unlike prior research focused on accident frequency and severity, this study de
Externí odkaz:
https://doaj.org/article/96d293c18f5f49148b06881d43b6d476
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-12 (2022)
Abstract This study aims to generate a deep learning algorithm-based model for quantitative prediction of financial losses due to accidents occurring at apartment construction sites. Recently, the construction of apartment buildings is rapidly increa
Externí odkaz:
https://doaj.org/article/d9fa4c971b1d4e4a83d816d1245d5119
Autor:
Kwang-Kyun Lim, Ji-Myong Kim
Publikováno v:
Applied Sciences, Vol 13, Iss 18, p 10418 (2023)
The purpose of this research is to build a deep learning algorithm-based model that can use weather indicators to quantitatively predict financial losses associated with weather-related railroad accidents. Extreme weather events and weather disasters
Externí odkaz:
https://doaj.org/article/246e4c7ae2b74dbd80858d1cc0f876b5
Publikováno v:
Journal of Asian Architecture and Building Engineering, Vol 20, Iss 5, Pp 546-555 (2021)
The Korean construction industry has attracted interest and investment demand for lease-oriented investment products, such as shopping malls and studio apartments, as a substitute for financial products because of the low interest rates of the banks
Externí odkaz:
https://doaj.org/article/86e18f9c293943e595fd5628a3896326
Publikováno v:
Journal of Asian Architecture and Building Engineering, Vol 18, Iss 6, Pp 507-516 (2019)
There are many risks and uncertainties in plant construction projects, because of their complexity, difficulty in loss prediction and size of construction being large. The risk management of plant construction projects should not be relied solely on
Externí odkaz:
https://doaj.org/article/1415d59cc5364e34b5278507578df003
Publikováno v:
Journal of Asian Architecture and Building Engineering, Vol 18, Iss 5, Pp 472-478 (2019)
The purpose of this study is to suggest a quantitative risk assessment approach for construction sites using risk indicators to predict economic damages. The frequency of damage in building construction has recently increased, and the associated cost
Externí odkaz:
https://doaj.org/article/74bc91f45f344dfda7b33d6958268f2f