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Al-Mhdawi, M K S, Brito, M, Onggo, B S, Qazi, A, O'Connor, A and Namian, M (2023) Construction risk management in Iraq during the COVID-19 pandemic: Challenges to implementation and efficacy of practices. Journal of Construction Engineering and Management, 149(09).
Alankarage, S, Chileshe, N, Samaraweera, A, Rameezdeen, R and Edwards, D J (2023) Guidelines for using a case study approach in construction culture research: Application to BIM-enabled organizations. Journal of Construction Engineering and Management, 149(09).
Alikhani, H, Le, C, Jeong, H D and Damnjanovic, I (2023) Sequential machine learning for activity sequence prediction from daily work report data. Journal of Construction Engineering and Management, 149(09).
Cuciniello, G, Inzerillo, G, Corazziari, L, Ciampini, A, Degni, R, Torresi, M and Leandri, P (2023) Modeling the tire-pavement noise as a regression function of speed, mixture properties, and age of the layer. Journal of Construction Engineering and Management, 149(09).
Deng, J, Zhao, Y, Li, X, Wang, Y and Zhou, Y (2023) Network embeddedness, relationship norms, and cooperative behavior: Analysis based on evolution of construction project network. Journal of Construction Engineering and Management, 149(09).
Dhanshyam, M and Srivastava, S K (2023) Decision framework for efficient risk mitigation in BOT highway infrastructure service projects. Journal of Construction Engineering and Management, 149(09).
Hochscheid, E, Falardeau, M, Lapalme, J, Boton, C and Rivest, L (2023) Practitioners' concerns about their liability toward BIM collaborative digital mockups: Case study in civil engineering. Journal of Construction Engineering and Management, 149(09).
Jiang, F, Lyu, Y, Zhang, Y and Guo, Y (2023) Research on the differences between risk-factor attention and risk losses in PPP projects. Journal of Construction Engineering and Management, 149(09).
Kong, Z, Ma, H, Lv, K and Shi, J J (2023) Liability of foreignness in public-private partnership projects. Journal of Construction Engineering and Management, 149(09).
Lei, Z, Sadiq Altaf, M, Cheng, Z, Liu, H and Tang, S (2023) Measurement of information loss and transfer impacts of technology systems in offsite construction processes. Journal of Construction Engineering and Management, 149(09).
Li, J, Yang, M, Liu, C, Li, A and Guo, B (2023) Listen to the companies: Exploring BIM job competency requirements by text mining of recruitment information in China. Journal of Construction Engineering and Management, 149(09).
Liang, R, Li, R and Chong, H Y (2023) Decision-making model for evaluating joint venture contractors in construction of complex infrastructure megaprojects. Journal of Construction Engineering and Management, 149(09).
Ma, H, Cao, S, Wang, Y and Zhang, H (2023) The moderating effect of optimism bias on ambivalence of workers' unsafe behaviors. Journal of Construction Engineering and Management, 149(09).
Ma, J, Li, H, Yu, X, Fang, X, Fang, B, Zhao, Z, Huang, X, Anwer, S and Xing, X (2023) Sweat analysis-based fatigue monitoring during construction rebar bending tasks. Journal of Construction Engineering and Management, 149(09).
- Type: Journal Article
- Keywords: construction rebar workers; fatigue assessment; machine learning; sweat biomarkers; sweat glucose; sweat lactate; sweat rate; sweat sodium
- ISBN/ISSN: 0733-9364
- URL: http://doi.org/10.1061/JCEMD4.COENG-13233
- Abstract:
This study proposes a novel approach to monitor the fatigue levels of construction rebar benders by measuring chemical biomarkers using sweat sensors. Fatigue resulting from dehydration and energy depletion can severely endanger the safety and health of construction workers. Sodium, lactate, glucose, and sweat rate were chosen as detectable biomarkers in this study, as their concentrations can indicate hydration status, energy consumption, and electrolyte balance, making them suitable for fatigue monitoring. The results were used to construct a fatigue model using supervised machine learning approaches. Construction rebar experiments were conducted while the sweat-based biosensors were applied to rebar workers to evaluate their fatigue with five different classifiers, demonstrating accuracy rates ranging from 71.43% to 96.43%. The results suggested that sweat-based biomarkers offer a noninvasive and accessible fatigue monitoring alternative. This can potentially help alleviate fatigue-related adverse ill effects like dehydration or cramping by enabling instant fluid or nutrient supply recommendations during construction manual tasks. It also provides valuable insights into the physiological effects of rebar work. Besides, this study presents a valuable model for predicting workers' fatigue levels, which could be applied in the construction industry to improve workers' safety and productivity. Furthermore, the study highlights the importance of maintaining appropriate hydration, nutrition, and electrolyte balance during physically demanding tasks like construction manual work.
Montalbán-Domingo, L, Torres-Machi, C, Sanz-Benlloch, A, Pellicer, E and Molenaar, K R (2023) Green public procurement in civil infrastructure construction: Current performance and main project characteristics. Journal of Construction Engineering and Management, 149(09).
Mostofi, F, Tokdemir, O B and Toǧan, V (2023) Comprehensive root cause analysis of construction defects using semisupervised graph representation learning. Journal of Construction Engineering and Management, 149(09).
Patil, K R, Bhandari, S, Agrawal, A, Ayer, S K, Perry, L A and Hallowell, M R (2023) Analysis of youtube comments to inform the design of virtual reality training simulations to target emotional arousal. Journal of Construction Engineering and Management, 149(09).
Prieto, A J and Alarcón, L F (2023) Using fuzzy inference systems for lean management strategies in construction project delivery. Journal of Construction Engineering and Management, 149(09).
Pu, H, Fan, X, Li, W, Zhang, W, Schonfeld, P, Wei, F and Xu, Z (2023) Realizing a quick partial BIM update of subgrade in railway stations. Journal of Construction Engineering and Management, 149(09).
Qin, Y and Bulbul, T (2023) An EEG-based mental workload evaluation for ar head-mounted display use in construction assembly tasks. Journal of Construction Engineering and Management, 149(09).
Ranasinghe, U, Jefferies, M, Davis, P and Pillay, M (2023) Enabling a resilient work environment: An analysis of causal relationships between resilience engineering factors in construction refurbishment projects. Journal of Construction Engineering and Management, 149(09).
Ren, X, Li, Y and Guo, M (2023) Dynamically identifying and evaluating key barriers to promoting prefabricated buildings: Text mining approach. Journal of Construction Engineering and Management, 149(09).
Xiang, Z, Rashidi, A and Ou, G (2023) Integrating inverse photogrammetry and a deep learning-based point cloud segmentation approach for automated generation of BIM models. Journal of Construction Engineering and Management, 149(09).
Zhou, X and Liao, P C (2023) Weighing votes in human-machine collaboration for hazard recognition: Inferring a hazard-based perceptual threshold and decision confidence from electroencephalogram wavelets. Journal of Construction Engineering and Management, 149(09).