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Al-Bayati, A J and Panzer, L (2019) Reducing Damage to Underground Utilities: Lessons Learned from Damage Data and Excavators in North Carolina. Journal of Construction Engineering and Management, 145(12).

Andrić, J M, Wang, J, Zou, P X W, Zhang, J and Zhong, R (2019) Fuzzy Logic–Based Method for Risk Assessment of Belt and Road Infrastructure Projects. Journal of Construction Engineering and Management, 145(12).

Cha, G, Park, S and Oh, T (2019) A Terrestrial LiDAR-Based Detection of Shape Deformation for Maintenance of Bridge Structures. Journal of Construction Engineering and Management, 145(12).

Chang, T, Deng, X, Hwang, B and Zhao, X (2019) Improving Quantitative Assessment of Political Risk in International Construction Projects: The Case of Chinese Construction Companies. Journal of Construction Engineering and Management, 145(12).

Han, Y, Yin, Z, Liu, J, Jin, R, Gidado, K, Painting, N, Yang, Y and Yan, L (2019) Defining and Testing a Safety Cognition Framework Incorporating Safety Hazard Perception. Journal of Construction Engineering and Management, 145(12).

Innella, F, Arashpour, M and Bai, Y (2019) Lean Methodologies and Techniques for Modular Construction: Chronological and Critical Review. Journal of Construction Engineering and Management, 145(12).

Jebelli, H, Choi, B and Lee, S (2019) Application of Wearable Biosensors to Construction Sites. I: Assessing Workers’ Stress. Journal of Construction Engineering and Management, 145(12).

  • Type: Journal Article
  • Keywords: Construction workers’ stress prediction; Physiological signals; Wearable sensor; Supervised learning; Workers’ safety; Health and productivity; Occupational stress;
  • ISBN/ISSN: 0733-9364
  • URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0001729
  • Abstract:
    One of the major hazards of the workplace, and in life in general, is occupational stress, which adversely affects workers’ well-being, safety, and productivity. The construction industry is one of the most stressful occupations. Current stress-assessment tools rely either on a subject’s perceived stress (e.g., stress questionnaires) or an individual’s chemical reaction to stressors (e.g., cortisol hormone). However, these methods can interrupt ongoing tasks and therefore may not be suitable for continuous measurement. To address this problem, the authors aim to develop and validate a framework for noninvasive and nonsubjective measurement of worker stress by examining changes in workers’ physiological signals collected from a wearable biosensor. The framework applies various filtering methods to reduce physiological signal noises and extracts the patterns of physiological signals as workers experience various stress levels. Then, the framework learns these patterns by applying a supervised-learning algorithm. To examine the performance of the proposed framework, the authors collected a physiological signal from 10 construction workers in the field. The proposed framework resulted in a stress-prediction accuracy of 84.48% in distinguishing between low and high stress levels and 73.28% in distinguishing among low, medium, and high stress levels. The results confirmed the potential of the proposed framework for assessing workers’ stress in the field. Automatic predictions of workers’ physical demand levels based on physiological signals is described in a companion paper. This study, along with the companion paper, contributes to the body of knowledge on the in-depth understanding of construction workers’ stress on construction sites by developing a noninvasive means for continuous monitoring and assessing workers’ stress. The proposed stress-recognition framework is expected to enhance workers’ health, safety, and productivity through early detection of occupational stressors on actual sites.

Jebelli, H, Choi, B and Lee, S (2019) Application of Wearable Biosensors to Construction Sites. II: Assessing Workers’ Physical Demand. Journal of Construction Engineering and Management, 145(12).

Li, X, Li, H, Cao, D, Tang, Y, Luo, X and Wang, G (2019) Modeling Dynamics of Project-Based Collaborative Networks for BIM Implementation in the Construction Industry: Empirical Study in Hong Kong. Journal of Construction Engineering and Management, 145(12).

Luo, S, Liu, Z, Yang, X, Lu, Q and Yin, J (2019) Construction Technology of Warm and Hot Mix Epoxy Asphalt Paving for Long-Span Steel Bridge. Journal of Construction Engineering and Management, 145(12).

Olivieri, H, Seppänen, O, Alves, T d C L, Scala, N M, Schiavone, V, Liu, M and Granja, A D (2019) Survey Comparing Critical Path Method, Last Planner System, and Location-Based Techniques. Journal of Construction Engineering and Management, 145(12).

Voordijk, J T (2019) Technological Mediation in Construction: Postphenomenological Inquiry into Digital Technologies. Journal of Construction Engineering and Management, 145(12).

Yeganeh, A A, Azizi, M and Falsafi, R (2019) Root Causes of Design-Construction Interface Problems in Iranian Design-Build Projects. Journal of Construction Engineering and Management, 145(12).