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Ansari, R (2019) Dynamic Simulation Model for Project Change-Management Policies: Engineering Project Case. Journal of Construction Engineering and Management, 145(07).
Arditi, D and Alavipour, S M R (2019) Trends in Expectations about Duties and Responsibilities of Construction Managers. Journal of Construction Engineering and Management, 145(07).
Azeez, M, Gambatese, J and Hernandez, S (2019) What Do Construction Workers Really Want? A Study about Representation, Importance, and Perception of US Construction Occupational Rewards. Journal of Construction Engineering and Management, 145(07).
de la Fuente, A, Casanovas-Rubio, M d M, Pons, O and Armengou, J (2019) Sustainability of Column-Supported RC Slabs: Fiber Reinforcement as an Alternative. Journal of Construction Engineering and Management, 145(07).
Larsson, R and Rudberg, M (2019) Impact of Weather Conditions on In Situ Concrete Wall Operations Using a Simulation-Based Approach. Journal of Construction Engineering and Management, 145(07).
Osman, K K, Claveria, J B, Faust, K M and Hernandez, S (2019) Temporal Dynamics of Willingness to Pay for Alternatives That Increase the Reliability of Water and Wastewater Service. Journal of Construction Engineering and Management, 145(07).
Tavafzadeh Haghi, N, Ahmadian Nezhad Monfared, M, Hashemian, L and Bayat, A (2019) Capital Cost Comparison of Pavements Comprised of Insulation Layers: Case Study in Edmonton, Canada. Journal of Construction Engineering and Management, 145(07).
Yang, Z, Yuan, Y, Zhang, M, Zhao, X and Tian, B (2019) Assessment of Construction Workers’ Labor Intensity Based on Wearable Smartphone System. Journal of Construction Engineering and Management, 145(07).
- Type: Journal Article
- Keywords: Construction safety; Labor intensity; Smartphone sensors; Machine learning; Construction management;
- ISBN/ISSN: 0733-9364
- URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0001666
- Abstract:
Construction jobs are more labor intensive than other industrial jobs. Safety problems caused by overworked bodies are common, and the supervision of construction workers is always flawed. In China, piecework has long been the common way to evaluate workers’ workloads, because it is always inconvenient to obtain direct indicators. To improve this situation, this paper proposes a method based on smartphone sensor acquisition and the concept of labor intensity to evaluate construction workers’ workloads. A sensor application based on the smartphone platform was created to effectively measure labor intensity so that the application could track construction workers’ movement data in an unobtrusive way. Moreover, preprocessing and a machine learning algorithm were used to classify 25 groups of experimental data. Then, the accuracy of the method was tested. It was shown that not only did the application meet the portability requirement, but its output also satisfied the accuracy requirement for supervising construction workers’ activity. The research presented in this paper can help construction organizations promote the intelligent management level of monitoring workers’ activity in real time and evaluating the workers’ whole-day workload.