EVA and SEBS-MA copolymers incorporated silicone rubber/SEBS blends: improvement of mechanical and thermal properties.

Enhancing the mechanical and thermal properties of Silicone rubber (SR)/SEBS blends using various compatibilizers opens the opportunity for such new blends to meet the market desire. For this purpose, blends with a 1:1 ratio of SR and SEBS are prepared with different amounts of EVA or SEBS-MA copolymers as compatibilizer. Mechanical properties of the blend are enhanced by adding EVA and SEBS-MA. Addition of 6 phr EVA profoundly improves the tensile strength from 7.70 to 10.06 MPa. Thermogravimetric analysis reveals that the presence of compatibilizer can improve the thermal stability of the blend, especially its initial degradation temperature (T5%). T5% of the blend increases from 376 to 390 °C when comprising 6 phr SEBS-MA. Morphology of the blends is investigated using SEM and AFM. Results of the relaxation modulus curves obtained by rubber process analyzer (RPA) demonstrate that the amount of relaxation in the uncured blends is higher than the cured ones. A comparison of the relaxation of the blends indicates that the relaxation modulus of the SEBS-MA compatibilized blends is enhanced more than other blends after curing. Further investigations indicate that the compatibilized blends exhibit higher tear energy and lower compression set.

An Automatic System to Monitor the Physical Distance and Face Mask Wearing of Construction Workers in COVID-19 Pandemic.

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. In this paper, we 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, we collected and annotated 1000 images, including different types of face mask wearing, and added them to a pre-existing face mask dataset to develop a dataset of 1853 images and increased the dataset to 3300 images by data augmentation. Then, we 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, we 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. We also used transfer learning for training the model. The final model was applied on four 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.