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PurposeDespite the importance of cognitive monitoring, limited studies attempted to continuously monitor cognitive status of workers regarding mental fatigue effects on fall hazard. Thus, the objective of this study is to investigate and understand the effects of working at height on mental fatigue development for fall hazard prevention.Design/methodology/approachA quantitative framework using two well-known methods, i.e. Wavelet Packet Decomposition and Sample entropy, is developed to analyze the captured brain signals from Electroencephalography (EEG) to quantitatively assess mental fatigue levels, and seven mental fatigue indices were obtained. Between-subjects lab experiment was designed and conducted to assess mental fatigue in Virtual Reality (VR) environment.FindingsBoth of the quantitative methods confirmed that height exposure can adversely affect subjects' vigilance levels and indicated higher levels of mental fatigue. Significant differences were found between the two tested groups (i.e. working at height or on the ground) for six out of seven indices. The results suggested that working-at-height group had higher mental fatigue levels.Research limitations/implicationsOne limitation of this study is the limited number of subjects recruited for the experiment. Overall, this study is a preliminary and exploratory work towards mental fatigue monitoring and assessment in subjects exposed to fall risk.Originality/valueThis is the first study to explore and focus on mental fatigue assessment, particularly for construction falling-from-height hazard prevention by continuously monitoring mental fatigue levels of workers. The research provides insight into construction safety enhancement using smart technologies.

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PurposeDespite the proven evidence of ever-growing productivity gains in the manufacturing industry as a result of years of research and investment in advanced technologies, such as robotics, the adoption of robots in construction is still lagging. The existing literature lacks technical frameworks and guidelines that account for the one-of-a-kind nature of construction projects and the myriad of materials and dimensional components in construction activities. This study seeks to address existing technical uncertainty and productivity issues associated with the application of robotics in the assembly-type manufacturing of industrialized construction.Design/methodology/approachTo facilitate the selection of suitable robotic arms for industrialized construction activities, primarily assembly-type manufacturing tasks of offsite production processes, an activity-based ranking system based on axiomatic design principles is proposed. The proposed ranking system utilizes five functional requirements derived from robot characteristics—speed, payload, reach, degrees of freedom and position repeatability—to evaluate robot performance in an industrialized construction task using simulations of a framing station.FindingsBased on design parameters obtained from activity-based simulations, seventy six robotic arms suitable for the framing task were scored and ranked. According to the sensitivity analysis of proposed functional requirements, speed is the key functional requirement that has a notable effect on productivity of a framing station and is thus the determinant in robot performance assessment for framing tasks.Originality/valueThe proposed ranking system is expected to augment automation in construction and serve as a preliminary guideline to help construction professionals in making informed decisions regarding the adoption of robotic arms.