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A Model for Predicting Construction Worker Fatigue

Authors

  • Ahmed Senouci Email 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

DOI:

https://doi.org/10.30564/jsbct.v4i2.4628
Received: 13 April 2022 | Published Online: 29 September 2022

Abstract

Fatigue impairs workers’ judgment, reduces their productivity, and jeop
ardizes their safety. The paper presents a tool to predict workers’ fatigue
based on their vital signs. An experimental study was conducted in which
the heart rate and sleep quality for three individuals were monitored using
fitness trackers (wearable sensors). The data collected were used to develop
two models based on regression analysis and Artificial Neural Networks
(ANN), to predict their fatigue level. A Borg’s scale was used to estimate
the Rating of Perceived Exertion (RPE) of the participants. The two models
were able to satisfactorily predict the RPE (workers fatigue level) with an
average validity of 75% and 80% for the regression ANN models, respec
tively. The developed models can provide project managers and superinten
dents 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

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 Con￾struction 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.136(10).

[10] 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.

[11] 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.

[12] 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

[13] 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

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

[15] 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

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

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

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

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

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Issue

2022 Vol.4 No.2

Article Type

Article

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Copyright © 2022 Ahmed Senouci, Surya Anuradha Garimella, Kyungki Kim, Neil Eldin

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.