Abstract
Occupational 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.
| Original language | English |
|---|---|
| Article number | 103987 |
| Journal | Automation in Construction |
| Volume | 133 |
| DOIs | |
| Publication status | Published - Jan 2022 |
Bibliographical note
Publisher Copyright:© 2021
Funding
The authors would like to thank the Republic of Turkey, Social Security Institution (SSI) for their support and providing the dataset. The authors would like to acknowledge that this paper is submitted in partial fulfilment of the requirements for PhD degree at Yildiz Technical University. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Keywords
- Artificial intelligence
- Artificial neural network
- Machine learning
- Multi-variate adaptive regression splines
- Occupational health and safety
- Safety management
- Support vector regression
- Time series
- Wavelet decomposition