Development of Model to Predict Natural Disaster-Induced Financial Losses for Construction Projects Using Deep Learning Techniques

Ji-Myong Kim, Junseo Bae, Seunghyun Son, Kiyoung Son and Sang-Guk Yum
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Ji-Myong Kim: Department of Architectural Engineering, Mokpo National University, Mokpo 58554, Korea
Junseo Bae: School of Computing, Engineering and Physical Sciences, University of the West of Scotland, Paisley PA1 2BE, UK
Seunghyun Son: Department of Architectural Engineering, Kyung Hee University, Suwon 17104, Korea
Kiyoung Son: School of Architectural Engineering, University of Ulsan, Ulsan 44610, Korea
Sang-Guk Yum: Department of Civil Engineering, Gangneung-Wonju National University, Gangneung 25457, Korea

Sustainability, 2021, vol. 13, issue 9, 1-12

Abstract: This study goals to develop a model for predicting financial loss at construction sites using a deep learning algorithm to reduce and prevent the risk of financial loss at construction sites. Lately, as the construction of high-rise buildings and complex buildings increases and the scale of construction sites surges, the severity and frequency of accidents occurring at construction sites are swelling, and financial losses are also snowballing. Singularly, as natural disasters rise and construction projects in urban areas increase, the risk of financial loss for construction sites is mounting. Thus, a financial loss prediction model is desired to mitigate and manage the risk of such financial loss for maintainable and effective construction project management. This study reflects the financial loss incurred at the actual construction sites by collecting claim payout data from a major South Korean insurance company. A deep learning algorithm was presented in order to develop an objective and scientific prediction model. The results and framework of this study provide critical guidance on financial loss management necessary for sustainable and successful construction project management and can be used as a reference for various other construction project management studies.

Keywords: loss prediction model; construction project management; construction site; deep learning algorithm; deep neural network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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