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Assaad, R and El-adaway, I H (2020) Enhancing the Knowledge of Construction Business Failure: A Social Network Analysis Approach. Journal of Construction Engineering and Management, 146(06), 04020052.
Chew, Y T E, Atay, E and Bayraktaroglu, S (2020) Female Engineers’ Happiness and Productivity in Organizations with Paternalistic Culture. Journal of Construction Engineering and Management, 146(06).
Dhal, M (2020) Labor Stand: Face of Precarious Migrant Construction Workers in India. Journal of Construction Engineering and Management, 146(06).
Herrera, R F, Mourgues, C, Alarcón, L F and Pellicer, E (2020) Understanding Interactions between Design Team Members of Construction Projects Using Social Network Analysis. Journal of Construction Engineering and Management, 146(06).
Hoseini, E, Bosch-Rekveldt, M and Hertogh, M (2020) Cost Contingency and Cost Evolvement of Construction Projects in the Preconstruction Phase. Journal of Construction Engineering and Management, 146(06).
Jang, Y, Song, K, Park, M and Ahn, Y (2020) Classifying the Business Model Types of International Construction Contractors. Journal of Construction Engineering and Management, 146(06).
Jin, Z and Gambatese, J (2020) Exploring the Potential of Technological Innovations for Temporary Structures: A Survey Study. Journal of Construction Engineering and Management, 146(06).
Li, G, Zhang, G, Chen, C and Martek, I (2020) Empirical Bid or No Bid Decision Process in International Construction Projects: Structural Equation Modeling Framework. Journal of Construction Engineering and Management, 146(06).
Li, S, Wu, X, Wang, X and Hu, S (2020) Relationship between Social Capital, Safety Competency, and Safety Behaviors of Construction Workers. Journal of Construction Engineering and Management, 146(06).
Liang, Y, Ashuri, B and Sun, W (2020) Analysis of the Variability of Project Cost and Schedule Performance in the Design-Build Environment. Journal of Construction Engineering and Management, 146(06).
Liu, X, Wang, X, Zhao, Y and Xia, N (2020) Solving Workplace Deviant Behavior in Construction by Leader–Member Exchange and Leader–Member . Journal of Construction Engineering and Management, 146(06), 04020061.
Lu, H, Behbahani, S, Azimi, M, Matthews, J C, Han, S and Iseley, T (2020) Trenchless Construction Technologies for Oil and Gas Pipelines: State-of-the-Art Review. Journal of Construction Engineering and Management, 146(06).
Mohammadi, A, Amador-Jimenez, L and Nasiri, F (2020) Reliable, Effective, and Sustainable Urban Railways: A Model for Optimal Planning and Asset Management. Journal of Construction Engineering and Management, 146(06).
Sherafat, B, Ahn, C R, Akhavian, R, Behzadan, A H, Golparvar-Fard, M, Kim, H, Lee, Y, Rashidi, A and Azar, E R (2020) Automated Methods for Activity Recognition of Construction Workers and Equipment: State-of-the-Art Review. Journal of Construction Engineering and Management, 146(06).
Shoieb, K, Serror, M H and Marzouk, M (2020) Web-Based Tool for Interoperability among Structural Analysis Applications. Journal of Construction Engineering and Management, 146(06).
Yao, M, Wang, F, Chen, Z and Ye, H (2020) Optimal Incentive Contract with Asymmetric Cost Information. Journal of Construction Engineering and Management, 146(06).
Zhang, M, Cao, Z, Yang, Z and Zhao, X (2020) Utilizing Computer Vision and Fuzzy Inference to Evaluate Level of Collision Safety for Workers and Equipment in a Dynamic Environment. Journal of Construction Engineering and Management, 146(06).
- Type: Journal Article
- Keywords: Safety management; Collision accident; Construction worker; Computer vision; Fuzzy inference;
- ISBN/ISSN: 0733-9364
- URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0001802
- Abstract:
The construction industry is facing unique problems in accident prevention. The existing management method for detecting workers’ unsafe behaviors and unsafe states of objects relies primarily on manual monitoring, which does not only consume large amounts of time and money but also cannot cover all workers in the entire construction site. Meanwhile, the workers’ perception of being at risk of injury decreases when they are concentrated in a crowded and noisy environment. In this case, it is difficult for them to take essential measures to protect themselves in the face of danger. In view of the aforementioned issues, this study proposes a method of evaluating the collision safety level of construction workers based on computer vision and fuzzy inference. Specifically, the proposed model works via two modules: vision extraction and safety assessment. The vision extraction module identifies construction workers and equipment through computer vision; centroid pixel coordinates and crowdedness are then extracted from a detection box. Afterward, the spatial relationship between moving devices and workers is calculated by a pixel calibration process. In the safety assessment module, the collected status information is analyzed by evaluating the safety level of each worker and conducting accident prevention through a fuzzy inference system. The safety level, which indicates the comprehensive risk of collision between workers and equipment in a particular dynamic environment, will be displayed numerically, breaking through the limitations of conventional qualitative evaluation. Field experiments validate the feasibility of the proposed method of informing workers about potential danger situations in an objective way. Moreover, by setting a safety-level threshold, the onsite safety management personnel can take corresponding measures to avoid collision accidents when the worker’s safety level is lower than the threshold.
Zhang, X and Tariq, S (2020) Failure Mechanisms in International Water PPP Projects: A Public Sector Perspective. Journal of Construction Engineering and Management, 146(06).