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Birgonul, Z (2021) A receptive-responsive tool for customizing occupant's thermal comfort and maximizing energy efficiency by blending BIM data with real-time information. Smart and Sustainable Built Environment, 10(3), 504-35.

Brandín, R and Abrishami, S (2021) Information traceability platforms for asset data lifecycle: blockchain-based technologies. Smart and Sustainable Built Environment, 10(3), 364-86.

Eiris, R, Albeaino, G, Gheisari, M, Benda, W and Faris, R (2021) InDrone: a 2D-based drone flight behavior visualization platform for indoor building inspection. Smart and Sustainable Built Environment, 10(3), 438-56.

Faris, E, Matarneh, S, Talebi, S, Kagioglou, M, Hosseini, M R and Abrishami, S (2021) Toward digitalization in the construction industry with immersive and drones technologies: a critical literature review. Smart and Sustainable Built Environment, 10(3), 345-63.

Hosseini, M R, Jupp, J, Papadonikolaki, E, Mumford, T, Joske, W and Nikmehr, B (2021) Position paper: digital engineering and building information modelling in Australia. Smart and Sustainable Built Environment, 10(3), 331-44.

Karsten Winther, J, Nielsen, R, Schultz, C and Teizer, J (2021) Automated activity and progress analysis based on non-monotonic reasoning of construction operations. Smart and Sustainable Built Environment, 10(3), 457-86.

Lamptey, T, De-Graft, O-M, Acheampong, A, Adesi, M and Ghansah, F A (2021) A framework for the adoption of green business models in the Ghanaian construction industry. Smart and Sustainable Built Environment, 10(3), 536-53.

Mahmoudi, E, Stepien, M and König, M (2021) Optimisation of geotechnical surveys using a BIM-based geostatistical analysis. Smart and Sustainable Built Environment, 10(3), 420-37.

Oke, A E and Arowoiya, V A (2021) Evaluation of internet of things (IoT) application areas for sustainable construction. Smart and Sustainable Built Environment, 10(3), 387-402.

Xiong, R and Tang, P (2021) Machine learning using synthetic images for detecting dust emissions on construction sites. Smart and Sustainable Built Environment, 10(3), 487-503.

  • Type: Journal Article
  • Keywords: construction dust emissions; object detection; deep learning; virtual rendering engine; internet of things; machine learning; computer vision; dust control; demolition; construction sites; health risks; United Kingdom
  • ISBN/ISSN:
  • URL: http://dx.doi.org/10.1108/SASBE-04-2021-0066
  • Abstract:
    Automated dust monitoring in workplaces helps provide timely alerts to over-exposed workers and effective mitigation measures for proactive dust control. However, the cluttered nature of construction sites poses a practical challenge to obtain enough high-quality images in the real world. The study aims to establish a framework that overcomes the challenges of lacking sufficient imagery data (“data-hungry problem”) for training computer vision algorithms to monitor construction dust. This study develops a synthetic image generation method that incorporates virtual environments of construction dust for producing training samples. Three state-of-the-art object detection algorithms, including Faster-RCNN, you only look once (YOLO) and single shot detection (SSD), are trained using solely synthetic images. Finally, this research provides a comparative analysis of object detection algorithms for real-world dust monitoring regarding the accuracy and computational efficiency. This study creates a construction dust emission (CDE) dataset consisting of 3,860 synthetic dust images as the training dataset and 1,015 real-world images as the testing dataset. The YOLO-v3 model achieves the best performance with a 0.93 F1 score and 31.44 fps among all three object detection models. The experimental results indicate that training dust detection algorithms with only synthetic images can achieve acceptable performance on real-world images This study provides insights into two questions: (1) how synthetic images could help train dust detection models to overcome data-hungry problems and (2) how well state-of-the-art deep learning algorithms can detect nonrigid construction dust.