Predicting financial losses due to apartment construction accidents utilizing deep learning techniques

Ji-Myong Kim, Junseo Bae, Hyunsoung Park, Sang-Guk Yum*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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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 increasing to solve housing shortage caused by increasing urban density. However, high-rise and large-scale construction projects are increasing the frequency and severity of accidents occurring inside and outside of construction sites, leading to increases of financial losses. In particular, the increase in severe weather and the surge in abnormal weather events due to climate change are aggravating the risk of financial losses associated with accidents occurring at construction sites. Therefore, for sustainable and efficient management of construction projects, a loss prediction model that prevents and reduces the risk of financial loss is essential. This study collected and analyzed insurance claim payout data from a main insurance company in South Korea regarding accidents occurring inside and outside of construction sites. Deep learning algorithms were applied to develop predictive models reflecting scientific and recent technologies. Results and framework of this study provide critical guidance on financial loss management necessary for sustainable and efficacious construction project management. They can be used as a reference for various other construction project management studies.
Original languageEnglish
Article number5365
Number of pages12
JournalScientific Reports
Volume12
DOIs
Publication statusPublished - 30 Mar 2022

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