Accident prediction in construction using hybrid wavelet-machine learning

KOÇ K., Ekmekcioğlu Ö., GÜRGÜN A. P.

Automation in Construction, vol.133, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 133
  • Publication Date: 2022
  • Doi Number: 10.1016/j.autcon.2021.103987
  • Journal Name: Automation in Construction
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Communication Abstracts, Compendex, ICONDA Bibliographic, INSPEC, Metadex, Civil Engineering Abstracts
  • Keywords: Safety management, Machine learning, Artificial intelligence, Occupational health and safety, Time series, Artificial neural network, Support vector regression, Multi-variate adaptive regression splines, Wavelet decomposition, ADAPTIVE REGRESSION SPLINES, NEURAL-NETWORK, TIME-SERIES, SAFETY, MODELS, SYSTEM, WORK, CLASSIFICATION, DECOMPOSITION, TREE
  • Yıldız Technical University Affiliated: Yes


© 2021Occupational accident rates in construction projects are usually higher than other industries in most countries, even though safety management systems are continuously improving. This study aims to contribute to the body of construction safety management by coupling discrete wavelet transform (DWT) and different machine learning (ML) methods to predict the number of occupational accidents using time series data. A dataset that consists of 393,160 occupational accidents recorded in Turkey between 2012 and 2020 was analyzed to predict the number of accidents for short-term, mid-term and long-term time periods, 1-day, 7-day and 30-day ahead, respectively. Model performances of stand-alone ML algorithms are improved with DWT, and hybrid wavelet-ANN showed the best performance. A dynamic utilization plan was proposed to the field of safety management by introducing a new theoretical and practical framework. This study also aims to fill the gap in the literature related to time series prediction models in construction safety management.