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.
Highlights
As the scale and complexity of construction works are increasing, variation of construction methods, and aggressive introduction of new construction methods are being made
This study is to develop a model for predicting financial loss of a construction site by using a deep learning algorithm based on actual loss data generated at construction sites
A financial loss model of a construction site was developed using a deep learning algorithm based on the financial loss data of a construction site
Summary
As the scale and complexity of construction works are increasing, variation of construction methods, and aggressive introduction of new construction methods are being made. Various new risk factors for fiscal loss are occurring, and uncertainty in the financial risk prediction is increasing rapidly [1]. The requirement for more dependable and scientific financial risk management in the complete construction project process is constantly being highlighted. Current construction project risk management techniques are not adequately responding to these demands [2]. On construction sites, various types of accidents occur and are exposed to the outside, and they are greatly affected by geographical and environmental factors, resulting in large and small personal injuries and physical losses [4]. Construction work near coastal, mountainous, and river areas, which is increasing with high preference, is greatly affected by geographic requirements.
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