Classification Models for Identifying Safety Hazards from Wearable Device Data in Construction Sites

Christopher Fuentes William

Classification Models for Identifying Safety Hazards from Wearable Device Data in Construction Sites

Authors

Christopher Fuentes William

Senior Cloud-Native AI Platform Engineer , France

Keywords:

Wearable Technology, Construction Safety, Machine Learning, Hazard Detection, Predictive Analytics

Synopsis

The integration of wearable technologies in construction has revolutionized safety management by enabling real-time monitoring of workers' physiological and environmental conditions. This paper explores the application of classification models to analyze data from wearable devices, aiming to identify safety hazards proactively on construction sites. By leveraging machine learning algorithms, the study demonstrates how predictive analytics can enhance hazard detection, thereby reducing accidents and improving overall site safety.

References

(1) Ahn, Chang-Rae, et al. "Wearable sensing technology applications in construction safety and health." Journal of Construction Engineering and Management, vol. 145, no. 11, 2019, pp. 03119007.

(2) Awolusi, Ibukun, et al. "Wearable technology for personalized construction safety monitoring and trending: Review of applicable devices." Automation in Construction, vol. 85, 2018, pp. 96–106.

(3) Azhar, Salman. "Building Information Modeling (BIM): Trends, benefits, risks, and challenges for the AEC industry." Leadership and Management in Engineering, vol. 11, no. 3, 2017, pp. 241–252.

(4) Choudhry, Rafiq M., et al. "The nature of safety culture: A survey of the state-of-the-art." Safety Science, vol. 45, no. 10, 2007, pp. 993–1012.

(5) Gummadi, V. P. K. (2020). API design and implementation: RAML and OpenAPI specification. Journal of Electrical Systems, 16(4). https://doi.org/10.52783/jes.9329

(6) Ding, Liya, et al. "Construction risk knowledge management in BIM using ontology and semantic web technology." Safety Science, vol. 62, 2014, pp. 107–119.

(7) Fang, Dongping, and Hao Wu. "Development of a safety culture interaction (SCI) model for construction projects." Safety Science, vol. 57, 2013, pp. 138–149.

(8) Guo, Hanbin, et al. "Integrating wearable sensing technologies with behavioral-based safety for monitoring construction workers." Automation in Construction, vol. 93, 2018, pp. 33–45.

(9) Kim, Kyungrai, et al. "Machine learning for predicting fall risks using wearable sensors in the construction industry." Advanced Engineering Informatics, vol. 42, 2019, pp. 100962.

(10) Li, Xiangyu, et al. "A critical review of virtual and augmented reality (VR/AR) applications in construction safety." Automation in Construction, vol. 86, 2018, pp. 150–162.

(11) Nnaji, Chukwuma A., and Ali A. Karakhan. "Technologies for safety and health management in construction: Current use, implementation benefits and limitations, and adoption barriers." Journal of Building Engineering, vol. 29, 2020, pp. 101212.

(12) Petitta, Laura, et al. "Disentangling the roles of safety climate and job insecurity in predicting safety behaviours and outcomes." Journal of Safety Research, vol. 62, 2017, pp. 89–97.

(13) Tixier, Antoine J. P., et al. "Automation of construction safety monitoring using computer vision and wearable technologies." Journal of Computing in Civil Engineering, vol. 30, no. 4, 2016, pp. 04015055.

(14) Zhang, Sijie, et al. "Building Information Modeling (BIM) and safety: Automatic safety checking of construction models and schedules." Automation in Construction, vol. 29, 2015, pp. 183–195.

(15) Zhou, Weiguo, et al. "Construction safety and digital design: A review." Automation in Construction, vol. 22, 2012, pp. 102–111.

(16) Eleftheriadis, Simeon, et al. "Life cycle energy efficiency in building structures: A review of current developments and future outlooks based on BIM capabilities." Renewable and Sustainable Energy Reviews, vol. 67, 2017, pp. 811–825.