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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).
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).
- Type: Journal Article
- Keywords: Bayesian inference; decision confidence; electroencephalogram; hazard recognition; human-machine collaboration; perceptual threshold
- ISBN/ISSN: 0733-9364
- URL: http://doi.org/10.1061/JCEMD4.COENG-13351
- Abstract:
Human-machine collaboration is a promising approach to improve on-site hazard inspection because it can complement the inherent limitations of human cognitive functions. Nevertheless, research on the effective integration of opinions from humans and machines to form optimal group decision-making is lacking. Prior work suggests that a confidence-weighted voting strategy is superior, but self-reported decision confidence is often unreliable. Thus, this study proposes an innovative methodological framework to predict workers' hazard response choices and decision confidence from brain activities captured by a wearable electroencephalogram (EEG) device. First, we developed a Bayesian inference-based algorithm to ascertain the decision threshold above which a hazard is reported characterized by the power of human brain activity. Furthermore, we describe hazard recognition as a process of probabilistic inference involving a decision uncertainty evaluation. Benchmarking against an optimal Bayesian observer, the optimal criteria to differentiate between low-, medium-, and high-confidence levels were obtained based on numerical simulations. The proposed method was tested empirically with a predesigned experiment in which 77 construction workers participated in a hazard recognition task while their EEG data were simultaneously collected. Cross-validating with behavioral indexes of the signal detection theory, the results confirmed the possibility of EEG measurement to observe workers' internal representations when discriminating hazards. Parietal α-band EEG power was chosen as a proxy for confidence-level evaluation prior to responses. Theoretically, this framework characterizes workers' mental model when recognizing hazards. Practically, it enables the prediction of workers' hazard responses and decision uncertainty, supporting the design of future hazard confirmation mechanisms in the collaborative human-machine systems research field.