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Gerassis, S, Martín, J E, García, J T, Saavedra, A and Taboada, J (2017) Bayesian Decision Tool for the Analysis of Occupational Accidents in the Construction of Embankments. Journal of Construction Engineering and Management, 143(02).

Guo, B H W, Yiu, T W, González, V A and Goh, Y M (2017) Using a Pressure-State-Practice Model to Develop Safety Leading Indicators for Construction Projects. Journal of Construction Engineering and Management, 143(02).

Hanna, A S, Mikhail, G and Iskandar, K A (2017) State of Prefab Practice in the Electrical Construction Industry: Qualitative Assessment. Journal of Construction Engineering and Management, 143(02).

Kadry, M, Osman, H and Georgy, M (2017) Causes of Construction Delays in Countries with High Geopolitical Risks. Journal of Construction Engineering and Management, 143(02).

Kim, H, Ahn, C R and Yang, K (2017) Identifying Safety Hazards Using Collective Bodily Responses of Workers. Journal of Construction Engineering and Management, 143(02).

  • Type: Journal Article
  • Keywords: Hazard identification; Bodily response; Inertial measurement unit (IMU); Collective sensing;
  • ISBN/ISSN: 0733-9364
  • URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0001220
  • Abstract:
    Current construction hazard identification mostly relies on safety managers’ ability to identify hazards using their prior knowledge about them. Consequently, numerous latent hazards remain unidentified, which poses significant risks to construction workers. To advance current hazard identification capabilities, this study examines the feasibility of harnessing and analyzing collective patterns of workers’ bodily responses (balance, gait, etc.) to identify safety hazards on a jobsite. To test the hypothesis that the abnormality of workers’ bodily responses in one location highly correlates with the likelihood of a safety hazard in that location, this project collected data on the bodily responses of 10 subjects who participated in five experiments. These test subjects wore inertial measurement unit (IMU) sensors on their body. Then the collected response data were analyzed using three metrics [average, standard deviation, and Shapiro-Wilk statistic (W)]. The data showed that the normality of workers’ bodily response distributions—represented as a W statistic—highly correlated with hazard locations in every experiment, which implies that workers’ bodily responses in hazardous areas are more irregularly distributed than in nonhazardous areas. This outcome demonstrates an opportunity for utilizing workers’ collective bodily responses to identify safety hazards in diverse construction environments.

Love, P E D, Veli, S, Davis, P, Teo, P and Morrison, J (2017) {[}See the Difference{]} in a Precast Facility: Changing Mindsets with an Experiential Safety Program. Journal of Construction Engineering and Management, 143(02).

Ng, A W Y and Chan, A H S (2017) Mental Models of Construction Workers for Safety-Sign Representation. Journal of Construction Engineering and Management, 143(02).

Olaniran, O J, Love, P E D, Edwards, D J, Olatunji, O and Matthews, J (2017) Chaos Theory: Implications for Cost Overrun Research in Hydrocarbon Megaprojects. Journal of Construction Engineering and Management, 143(02).

Park, J, Kim, K and Cho, Y K (2017) Framework of Automated Construction-Safety Monitoring Using Cloud-Enabled BIM and BLE Mobile Tracking Sensors. Journal of Construction Engineering and Management, 143(02).

Park, Y, Gwak, H and Lee, D (2017) Dozer Workability Estimation Method for Economic Dozing. Journal of Construction Engineering and Management, 143(02).

Wang, C, Mohd-Rahim, F A, Chan, Y Y and Abdul-Rahman, H (2017) Fuzzy Mapping on Psychological Disorders in Construction Management. Journal of Construction Engineering and Management, 143(02).

Zhong, Y, Ling, F Y Y and Wu, P (2017) Using Multiple Attribute Value Technique for the Selection of Structural Frame Material to Achieve Sustainability and Constructability. Journal of Construction Engineering and Management, 143(02).