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Costa, L, Barbosa, M B A, Baldam, R d L and Coelho, T d P (2019) Challenges of Process Modeling in Architecture and Engineering to Execute Projects and Public Works. Journal of Construction Engineering and Management, 145(01).
Han, Y, Feng, Z, Zhang, J, Jin, R and Aboagye-Nimo, E (2019) Employees’ Safety Perceptions of Site Hazard and Accident Scenes. Journal of Construction Engineering and Management, 145(01).
Jeelani, I, Albert, A, Han, K and Azevedo, R (2019) Are Visual Search Patterns Predictive of Hazard Recognition Performance? Empirical Investigation Using Eye-Tracking Technology. Journal of Construction Engineering and Management, 145(01).
Lee, C, Won, J and Lee, E (2019) Method for Predicting Raw Material Prices for Product Production over Long Periods. Journal of Construction Engineering and Management, 145(01).
Lee, J and Hyun, H (2019) Multiple Modular Building Construction Project Scheduling Using Genetic Algorithms. Journal of Construction Engineering and Management, 145(01).
Nasirian, A, Arashpour, M and Abbasi, B (2019) Critical Literature Review of Labor Multiskilling in Construction. Journal of Construction Engineering and Management, 145(01).
Ryu, J, Seo, J, Jebelli, H and Lee, S (2019) Automated Action Recognition Using an Accelerometer-Embedded Wristband-Type Activity Tracker. Journal of Construction Engineering and Management, 145(01).
Tang, W, Cui, Q, Zhang, F and Chen, Y (2019) Urban Rail-Transit Project Investment Benefits Based on Compound Real Options and Trapezoid Fuzzy Numbers. Journal of Construction Engineering and Management, 145(01).
Techera, U, Hallowell, M and Littlejohn, R (2019) Worker Fatigue in Electrical-Transmission and Distribution-Line Construction. Journal of Construction Engineering and Management, 145(01).
Zhang, M, Cao, T and Zhao, X (2019) Using Smartphones to Detect and Identify Construction Workers’ Near-Miss Falls Based on ANN. Journal of Construction Engineering and Management, 145(01).
- Type: Journal Article
- Keywords: Near-miss falls; Motion recognition; Construction safety; Smartphone; Machine learning; Artificial neural network (ANN);
- ISBN/ISSN: 0733-9364
- URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0001582
- Abstract:
In certain circumstances, near-miss falls can evolve into fall accidents in construction sites. Insight into near-miss falls offers an efficient way to better understand fall accidents. In this context, this paper explores potential applications of the smartphone as a data-acquisition tool to detect and identify near-miss falls on the basis of an artificial neural network (ANN). In training and evaluation experiments, a loss-of-balance (LOB) environment was artificially established by means of a balance board to simulate the scenarios in near-miss falls. Through a transition model between static and dynamic near-miss falls, the similarity between simulated and actual scenes of near-miss falls was improved. Furthermore, the feasibility of adopting ANN to correctly identify near-miss falls was verified. The results showed that the average precision, recall, and F1 score were 90.02%, 90.93%, and 90.42%, respectively, with an average error-detection rate of 16.26%. In test cases, the thresholds
Zhang, S, Liu, X, Gao, Y and Ma, P (2019) Effect of Level of Owner-Provided Design on Contractor’s Design Quality in DB/EPC Projects. Journal of Construction Engineering and Management, 145(01).
Zhang, Y, Luo, H, Skitmore, M, Li, Q and Zhong, B (2019) Optimal Camera Placement for Monitoring Safety in Metro Station Construction Work. Journal of Construction Engineering and Management, 145(01).