Improving Heat Strain Prediction in Construction Workers: A Transfer Learning Approach to Overcome Variability Challenges

This research addresses the challenge of predicting heat strain in construction workers by leveraging transfer learning techniques to handle individual variability. The approach improves prediction accuracy across different workers and environmental conditions.

Recommended citation: Ojha, A., Zhao, X., Zhang, Y., Liu, Y., and Jebelli, H. (2024). “Improving Heat Strain Prediction in Construction Workers: A Transfer Learning Approach to Overcome Variability Challenges.” 2024 International Conference on Computing in Civil Engineering (i3CE), Pittsburgh, Pennsylvania, U.S. (Accepted).

Recommended citation: Ojha, A., Zhao, X., Zhang, Y., Liu, Y., and Jebelli, H. (2024). "Improving Heat Strain Prediction in Construction Workers: A Transfer Learning Approach to Overcome Variability Challenges." 2024 International Conference on Computing in Civil Engineering (i3CE), Pittsburgh, Pennsylvania, U.S. (Accepted).
Download Paper