A deep learning algorithm-driven approach to predicting repair costs associated with natural disaster indicators: the case of accommodation facilities

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

It is well known that accurate and reliable maintenance and repair cost estimates are important to maintain a building in its optimal condition, especially during the operation and maintenance phase within the whole life cycle. However, due to emerging trends in buildings that are high-performance, large-scale, complex, and high-rise, it is difficult to achieve those cost estimates. In addition, the impact of climate changes that tend to occur more frequent and severe natural disasters has caused increasing damages to buildings, yet little is still specifically known about predicting the impact of natural disasters on repair costs of accommodation facilities accurately and reliably. This study fills this gap by developing and validating a deep neural network (DNN) model that can generalize repair cost trends associated with natural disaster factors, including peak ground acceleration, precipitation, wind speed, geographic profiles of adjacent water systems, drawing on 1,125 insurance claim payout records on accommodation facilities. The robustness of the developed DNN model was scientifically tested and validated using the root mean squared error and the mean absolute error methods. Practical applicability of the proposed modeling framework was then demonstrated by creating predicted repair cost trends. This study contributes to the existing knowledge by proposing a deep learning method that predicts repair costs of accommodation facilities associated with natural disasters, while providing both facility managers and insurance companies with evidence-based reference to develop better-informed cost management strategies against potential natural disasters.
Original languageEnglish
Article number103098
Number of pages7
JournalJournal of Building Engineering
Volume42
Early online date8 Aug 2021
DOIs
Publication statusE-pub ahead of print - 8 Aug 2021

Keywords

  • Accommodation facilities
  • Deep learning
  • Deep neural network
  • Repair cost
  • Natural disaster

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