Predicting Maximum Work Duration for Construction Workers

One of the most common health problems that threaten the transportation infrastructure construction workers in Hong Kong is heat stress. An effective way to reduce this problem is to design a proper work-rest schedule, and the key issue is predicting the maximum working duration given the different conditions of the workers and the surrounding environment, which is the research question of this study. Air temperature, an important input feature, is also determined by the maximum working duration itself, i.e., the input feature is a function of the prediction target. Therefore, the prediction model developed is different from ordinary prediction models and is hard to solve by standard statistical or machine learning models. For the prediction process, a trial-and-error algorithm is proposed to derive a solution based on two theorems that are rigorously proved; there exists a unique solution, and the solution is within a certain range in the prediction model. The proposed model and its solution approach were constructed and validated using simulated data; temperature data were collected from Hong Kong Observatory. The results showed that the mean squared error (MSE), mean absolute percentage error (MAPE), and R-2 of the test set were 0.1378, 0.1123, and 0.8182, respectively, showing that the prediction performance was generally accurate. This study can help construction practitioners and governments to rationally design the work-rest schedules of transportation infrastructure construction workers and thus protect them from the risks brought about by heat stress.

Automated summary

Heat stress is a common health problem for transportation infrastructure construction workers in Hong Kong. This study aims to predict the maximum working duration based on different conditions of the workers and the surrounding environment, in order to design a proper work-rest schedule. A unique solution within a certain range is derived using a trial-and-error algorithm and rigorously proved theorems. The proposed model was validated using simulated data from the Hong Kong Observatory, showing generally accurate prediction performance.