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Abbasnejad, B, Nasirian, A, Duan, S, Diro, A, Prasad Nepal, M and Song, Y (2024) Measuring BIM implementation: A mathematical modeling and artificial neural network approach. Journal of Construction Engineering and Management, 150(05).

Asadian, E, Azeez, S, Leicht, R M and Asadi, S (2024) Exploring the barriers to women in construction and the opportunities presented through lean. Journal of Construction Engineering and Management, 150(05).

Henao, Y, Grubert, E, Korey, M, Bank, L C and Gentry, R (2024) Life cycle assessment and life cycle cost analysis of repurposing decommissioned wind turbine blades as high-voltage transmission poles. Journal of Construction Engineering and Management, 150(05).

Huang, Y, Liu, D, Bell, F M, Yu, J and Pena-Mora, F (2024) Influences of intra- and interorganizational IT innovations on knowledge sharing and team creativity: Evidence from construction projects in China. Journal of Construction Engineering and Management, 150(05).

Min, Y and Lee, H W (2024) Adoption inequalities and causal relationship between residential electric vehicle chargers and heat pumps. Journal of Construction Engineering and Management, 150(05).

Nabi, M A, El-Adaway, I H and Assaad, R H (2024) Modeling inflation transmission among different construction materials. Journal of Construction Engineering and Management, 150(05).

Nguyen, P H D and Tran, D (2024) Exploring the use of quality control plans for alternative contracting methods in highway projects. Journal of Construction Engineering and Management, 150(05).

Ning, X, Ye, X, Li, H, Rajendra, D and Skitmore, M (2024) Evolutionary game analysis of optimal strategies for construction stakeholders in promoting the adoption of green building technology innovation. Journal of Construction Engineering and Management, 150(05).

Shi, M, Chen, C, Xiao, B and Seo, J (2024) Vision-based detection method for construction site monitoring by integrating data augmentation and semisupervised learning. Journal of Construction Engineering and Management, 150(05).

  • Type: Journal Article
  • Keywords: construction management; object detection; semisupervised learning; weather data augmentation
  • ISBN/ISSN: 0733-9364
  • URL: http://doi.org/10.1061/JCEMD4.COENG-14388
  • Abstract:
    Training deep learning models for vision-based monitoring of construction sites usually requires a large amount of labeled data. Semisupervised learning methods can efficiently obtain unlabeled data with substantial cost savings. Thus, this paper proposes a semisupervised object detection method for construction site monitoring. Weather as well as strong and weak data augmentation are integrated to cope with the complex construction site conditions (weather changes, camera view shifts, and so on) by integrating semisupervised learning to leverage the valid information in unlabeled construction site images. To validate its effectiveness, the proposed method was tested on the Alberta Construction Image Data Set (ACID), a public data set for the construction research community. The experimental results revealed that the proposed method achieves an average accuracy [mean average precision (mAP)] of 81.1% when trained on only 3% of the labeled images. This study helps to significantly reduce the development cost of vision-based object detection models for construction sites.

Tao, Y, Hu, H, Xu, F and Zhang, Z (2024) Work-rest schedule optimization of precast production considering workers' overexertion. Journal of Construction Engineering and Management, 150(05).

Turkoglu, H, Arditi, D and Polat, G (2024) Augmented time-cost trade-off optimization using particle swarm optimization. Journal of Construction Engineering and Management, 150(05).

Wang, H, Xu, S, Cui, D, Xu, H and Luo, H (2024) Information integration of regulation texts and tables for automated construction safety knowledge mapping. Journal of Construction Engineering and Management, 150(05).

Wang, J, Liang, M and Liao, P C (2024) Toward an intuitive device for construction hazard recognition management: Eye fixation-related potentials in reinvestigation of hazard recognition performance prediction. Journal of Construction Engineering and Management, 150(05).

Wang, L, Lee, J, Nimawat, J, Han, K and Gupta, A (2024) Integrated 4D design change management model for construction projects. Journal of Construction Engineering and Management, 150(05).

Wang, S, Hasan, M and Lu, M (2024) Global sensitivity analysis methodology for construction simulation models: Multiple linear regressions versus multilayer perceptions. Journal of Construction Engineering and Management, 150(05).

Zhang, S, Hua, X and Shi, X (2024) Measurement for risk perception ability. Journal of Construction Engineering and Management, 150(05).