The COVID-19 pandemic has caused many shutdowns in different industries around the world. Sectors such as infrastructure construction and maintenance projects have not been suspended due to their significant effect on people's routine life. In such projects, workers work close together that makes a high risk of infection. The World Health Organization recommends wearing a face mask and practicing physical distancing to mitigate the virus's spread. This paper developed a computer vision system to automatically detect the violation of face mask wearing and physical distancing among construction workers to assure their safety on infrastructure projects during the pandemic. For the face mask detection, the paper collected and annotated 1,000 images, including different types of face mask wearing, and added them to a pre-existing face mask dataset to develop a dataset of 1,853 images. Then trained and tested multiple TensorFlow state-of-the-art object detection models on the face mask dataset and chose the Faster R-CNN Inception ResNet V2 network that yielded the accuracy of 99.8%. For physical distance detection, the paper employed the Faster R-CNN Inception V2 to detect people. A transformation matrix was used to eliminate the camera angle's effect on the object distances on the image. The Euclidian distance used the pixels of the transformed image to compute the actual distance between people. A threshold of six feet was considered to capture physical distance violation. The paper also used transfer learning for training the model. The final model was applied on several videos of road maintenance projects in Houston, TX, that effectively detected the face mask and physical distance. We recommend that construction owners use the proposed system to enhance construction workers' safety in the pandemic situation.

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Inefficient project planning and control have been identified as the main contributor to the reduced performance of the construction industry in Iran. Meanwhile, improper use of planning and control software packages (PPCSPs) in these projects can be a key factor in this reduced performance. This study investigates different aspects of the PPCSP applications to draw the role of PPCSPs in the planning and control processes of construction projects in the country using a survey-based method. It is found that only 32.5% of the construction companies in Iran highly or very highly use PPCSPs. The low level of skill and the lack of management support are two main contributing factors to this reduced PPCSP application. The quality of the academic and vocational project management training programs and the lack of dependable PPCSP technical support are argued as possible sources of the issue. The identified PPCSP pattern in Iran is compared with the results reported for several developed and in-transition countries. This comparison reveals that Iran and in-transition countries fall deeply behind in employing PPCSPs in their construction projects compared to the developed countries.