A Model for Predicting Construction Worker Fatigue

Ahmed Senouci (Department of Construction Management, University of Houston, US)
Surya Anuradha Garimella (Department of Construction Management, University of Houston, US)
Kyungki Kim (Durham School of Architectural Engineering & Construction, University of Nebraska, US)
Neil Eldin (Department of Construction Management, University of Houston, US)

Article ID: 4628

DOI: https://doi.org/10.30564/jsbct.v4i2.4628

Abstract


Fatigue impairs workers’ judgment, reduces their productivity, and jeopardizes their safety. The paper presents a tool to predict workers’ fatiguebased on their vital signs. An experimental study was conducted in whichthe heart rate and sleep quality for three individuals were monitored usingfitness trackers (wearable sensors). The data collected were used to developtwo models based on regression analysis and Artificial Neural Networks(ANN), to predict their fatigue level. A Borg’s scale was used to estimatethe Rating of Perceived Exertion (RPE) of the participants. The two modelswere able to satisfactorily predict the RPE (workers fatigue level) with anaverage validity of 75% and 80% for the regression ANN models, respectively. The developed models can provide project managers and superintendents with early warning to avoid potential worker overexertion, injuries,and fatalities.

Keywords


Fatigue assessment; Linear regression; Artificial neural network; Prediction models; Heart rate monitoring; Sleep quality; Wearable sensors

Full Text:

PDF

References


[1] OSHA, 2010. Construction Industry. https://www.osha.gov/doc/ (Accessed on Sep. 1, 2017).

[2] Abdelhamid, T.S., Everett, J.G., 2002. Physiological Demands during Construction Work. Journal of Construction Engineering and Management. 128(5), 427-437.

[3] Aryal, A., Ghahramani, A., Becerik-Gerber, B., 2017.Monitoring fatigue in construction workers using physiological measurements. Automation in Construction. 82, 154-165.

[4] Williamson, A.M., Feyer, A.M., 2000. Moderate sleep deprivation produces impairments in cognitive and motor performance equivalent to legally prescribed levels of alcohol intoxication. Occupational and Environmental Medicine. 57, 649-655.

[5] Zhang, M., Murphy, L.A., Fang, D., et al., 2015. Influence of fatigue on construction workers’ physical and cognitive function. Occupational Medicine (London). 65(3), 245-250.

[6] OSHA, 2015. Commonly used statistics. https://www.osha.gov/oshstats/commonstats.html (Accessed on August 1, 2017).

[7] Bureau of Labor Statistics (BLS), 2015. Nonfatal Occupational Injuries and Illnesses Requiring Days Away From Work. https://www.bls.gov/news.release/pdf/osh2.pdf.

[8] Gatti, U., Schneider, S., Migliaccio, G., 2014. Physiological condition monitoring of construction workers. Automation in Construction. 44, 227-233.

[9] Powell, R., Copping, A., 2010. Sleep Deprivation and Its Consequences in Construction Workers. Journal of Construction Engineering and Management.

[10] (10).

[11] Hwang, S., Seo, J., Jebelli, H., et al., 2016. Feasibility analysis of heart rate monitoring of construction workers using a photoplethysmography (PPG) sensor embedded in a wristband-type activity tracker. Automation in Construction. 71(2), 372-381.

[12] Foster C., Florhaug, J.A., Franklin, J., et al., 2001.Approach to monitoring exercise training. Journal of Strength and Conditioning Research. 15(1), 109-115.

[13] Borg, G., 1990. Psychophysical scaling with applications in physical work and the perception of exertion.Scandinavian journal of work, environment & health. 16(Suppl 1), 55-8. DOI: https://doi.org/10.5271/sjweh.1815

[14] Chan, A., Yam, M., Chung, J., et al., 2012. Developing a heat stress model for construction workers.Journal of Facilities Management. 10, 59-74. DOI: https://doi.org/10.1108/14725961211200405

[15] Borg, G.A., 1982. Psychophysical bases of perceived exertion. Medicine and Science in Sports and Exercise. 14(5), 377-381.

[16] Tanaka, H., Monahan, K.D., Seals, D.R., 2001.Age-Predicted Maximal Heart Rate Revisited. Journal of the American College of Cardiology. 37(1), 153-156. DOI: https://doi.org/10.1016/s0735-1097(00)01054-8

[17] Neter, J., Kutner, M.H., Nachtsheim, C.J., et al.,1996. Applied Linear Statistical Models. 4th Edition, WCB McGraw-Hill, New York.

[18] Sadiq, R., Rajani, B., Kleiner, Y., 2004. Fuzzy-Based Method to Evaluate Soil Corrosivity for Prediction of Water Main Deterioration. Journal of Infrastructure Systems. 10(4), 149.

[19] DOI: https://doi.org/10.1061/(ASCE)1076-0342(2004) 10:4(149)

[20] GMDH, Inc., 2021. GMDH Shell. (ComputeSoftware), Retrieved from https://gmdhsoftware.com/neural-network-software/.

[21] Zayed, T., Halpin, D., 2005. Deterministic models for assessing productivity and cost of bored piles.Construction Management and Economics. 23(5), 531-543.


Refbacks

  • There are currently no refbacks.
Copyright © 2022 Ahmed Senouci, Surya Anuradha Garimella, Kyungki Kim, Neil Eldin Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.