A Sensor-Based Empirical Framework to Measure Construction Labor Productivity
Publication: Construction Research Congress 2022
ABSTRACT
Measurement of construction labor productivity involves various subjective factors (e.g., motivation, stress, and fatigue). Most measurement approaches for subjective factors in productivity applications require manual data collection (e.g., questionnaires, interviews, and observations); therefore, research gaps exist regarding how to (1) directly measure subjective factors using data that reflect workers’ real performance at single points in time, and (2) integrate these factors into existing or new models in labor productivity applications. This paper proposes an empirical framework for integrating real-time data from multiple sensors for directly measuring subjective factors affecting labor productivity. The proposed framework, which was designed, built, and evaluated using design science research methodology, contributes to the body of knowledge as part of a longer-term study proposing an empirical framework for triangulating data from a multi-sensor system to simultaneously measure multiple subjective factors affecting labor productivity. Study outcomes will complement existing artificial intelligence, simulation, and statistical models for construction productivity applications.
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REFERENCES
Ahn, C. R., Lee, S., Sun, C., Jebelli, H., Yang, K., and Choi, B. 2019. “Wearable sensing technology applications in construction safety and health.” J. Constr. Eng. Manage., 145 (11), 03119007.
Al Jassmi, H., Ahmed, S., Philip, B., Al Mughairbi, F., and Al Ahmad, M. 2019. “E-happiness physiological indicators of construction workers’ productivity: A machine learning approach.” J. Asian Arch. Build. Eng., 18 (6), 517–526.
Alberdi, A., Aztiria, A., and Basarab, A. 2016. “Towards an automatic early stress recognition system for office environments based on multimodal measurements: A review.” J. Bio. Info., 59, 49–75.
Arpaia, P., Moccaldi, N., Prevete, R., Sannino, I., and Tedesco, A. 2020. “A wearable EEG instrument for real-time frontal asymmetry monitoring in worker stress analysis.” IEEE Trans. Inst. Meas., 69 (10).
Aryal, A., Ghahramani, A., and Becerik-Gerber, B. 2017. “Monitoring fatigue in construction workers using physiological measurements.” Autom. Constr., 82, 154–165.
Bangaru, S., Wang, C., Busam, S., and Aghazadeh, F. 2021. “ANN-based automated scaffold builder activity recognition through wearable EMG and IMU sensors.” Autom. Constr., 126, 103653.
Chen, J., Song, X., and Lin, Z. 2016. “Revealing the ‘Invisible Gorilla’ in construction: Estimating construction safety through mental workload assessment.” Autom. Constr., 63, 173–183.
Choi, B., Jebelli, H., and Lee, S. 2019. “Feasibility analysis of electrodermal activity (EDA) acquired from wearable sensors to assess construction workers’ perceived risk.” Saf. Sci., 115, 110–120.
Gerami Seresht, N., and Fayek, A. R. 2018. “Dynamic modeling of multifactor construction productivity for equipment-intensive activities.” J. Constr. Eng. Manage., 144 (9), 04018091.
Hamzeh, F. R., Faek, F., and AlHussein, H. 2018. “Understanding improvisation in construction through antecedents, behaviors, and consequences.” Constr. Manag. Econ., 37 (2), 61–71.
Hasan, A., Baroudi, B., Elmualim, A., and Rameezdeen, R. 2018. “Factors affecting construction productivity: A 30-year systematic review.” Eng. Constr. Archit. Manag., 25 (7), 916–937.
Hwang, S., Jebelli, H., Choi, B., Choi, M., and Lee, S. 2018. “Measuring workers’ emotional state during construction tasks using wearable EEG.” J. Constr. Eng. Manage., 144 (7), 04018050.
Jebelli, H., Choi, B., and Lee, S. 2019. “Application of wearable biosensors to construction sites. I: Assessing workers’ stress.” J. Constr. Eng. Manage., 145 (12), 04019079.
Jebelli, H., Hwang, S., and Lee, S. 2018. “EEG-based workers’ stress recognition at construction sites.” Autom. Constr., 93, 315–324.
Johari, S., and Jha, K. N. 2020. “Impact of work motivation on construction labor productivity.” J. Manage. Eng., 36 (5), 04020052.
Kedir, N., Raoufi, M., and Fayek, A. R. 2020. “Fuzzy agent-based multicriteria decision-making model for analyzing construction crew performance.” J. Manage. Eng., 36 (5), 04020053.
Khowaja, S. A., Prabono, A. G., Setiawan, F., Yahya, B. N., and Lee, S. 2020. “Toward soft real-time stress detection using wrist-worn devices for human workspaces.” Soft Comput., 25, 2793–2820.
Lee, W., Lin, K., Seto, E., and Migliaccio, G. C. 2017. “Wearable sensors for monitoring on-duty and off-duty worker physiological status and activities in construction.” Autom. Constr., 83, 341–353.
Liu, S., Tong, J., Meng, J., Yang, J., Zhao, X., He, F., Qi, H., and Ming, D. 2016. “Study on an effective cross-stimulus emotion recognition model using EEGs based on feature selection and support vector machine.” Int. J. Mach. Learn. Cyber., 9 (5), 721–726.
McKinsey Global Institute. 2017. “Reinventing construction: A route to higher productivity.” London: McKinsey Global Institute. <https://www.mckinsey.com/industries/capital-projects-and-infrastructure/our-insights/reinventing-construction-through-a-productivity-revolution>(Jan. 15, 2021).
Nwaogu, J., and Chan, A. 2021. “Work-related stress, psychophysiological strain, and recovery among on-site construction personnel.” Autom. Constr., 125, 103629.
Ojha, A., Shakerian, S., Habibnezhad, M., Jebelli, H., Lee, S., and Fardhosseini, M. S. 2020. “Feasibility of using physiological signals from biosensor.” In The Creative Construction e-Conference. Budapest: Opatija, Croatia.
Raoufi, M., and Fayek, A. R. 2018. “Framework for identification of factors affecting construction crew motivation and performance.” J. Constr. Eng. Manage., 144 (9), 04018080.
Raoufi, M., and Fayek, A. R. 2020. “Fuzzy Monte Carlo agent-based simulation of construction crew performance.” J. Constr. Eng. Manage., 146 (5), 04020041.
Ryu, J., Alwasel, A., Haas, C. T., and Abdel-Rahman, E. 2020. “Analysis of relationships between body load and training, work methods, and work rate: Overcoming the novice mason’s risk hump.” J. Constr. Eng. Manage., 146 (8), 04020097.
Sun, C., Ahn, S., and Ahn, C. R. 2020. “Identifying workers’ safety behavior–related personality by sensing.” J. Constr. Eng. Manage., 146 (7), 04020078.
Tsehayae, A. A., and Fayek, A. R. 2016. “Developing and optimizing context-specific fuzzy inference system-based construction labor productivity models.” J. Constr. Eng. Manage., 142 (7), 04016017.
Umer, W., Li, H., Yantao, Y., Antwi-Afari, M. F., Anwer, S., and Luo, X. 2020. “Physical exertion modeling for construction tasks using combined cardiorespiratory and thermoregulatory measures.” Autom. Constr., 112, 103079.
Wang, D., Chen, J., Zhao, D., Dai, F., Zheng, C., and Wu, X. 2017. “Monitoring workers’ attention and vigilance in construction activities through a wireless and wearable electroencephalography system.” Autom. Constr., 82, 122–137.
Weber, R. 2018. Design-science research. Chap. 11 in Research methods: Information, systems, and contexts. 2nd ed. edited by K. Williamson and G. Johanson, 267–288. Cambridge, MA: Chandos.
Yi, W., and Chan, A. P. C. 2014. “Critical review of labor productivity research in construction journals.” J. Manage. Eng., 30 (2), 214–225.
Yi, W., and Chan, A. P. C. 2017. “Effects of heat stress on construction labor productivity in Hong Kong: A case study of rebar workers.” Int. J. Environ. Res. Public Health, 14 (9), 1055.
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Published online: Mar 7, 2022
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