Tae Yeon Kim’s research while affiliated with Khalifa University and other places

This work presents a finite element model of the thermo-mechanical behaviour of tyre–pavement interaction, focusing on the effects of temperature variations in the UAE on skid resistance under various tyre operating conditions. The pavement is modelled as multiple layers to account for the stiffness contribution of each layer. The top asphalt layer is modelled at a microscale level to consider its various constituents such as air voids, aggregates and binder. Moreover, the model accounts for the viscoelastic properties of tyre and pavement, considering their dependence on time and temperature. The finite element simulations of a rolling tyre over the pavement have been carried out under different tyre operating conditions and various temperature cases reflecting the winter, spring, and summer seasons in the UAE. The simulation results show that the maximum level of skid resistance occurs during the winter season and thereafter drops by a significant amount during the summer. This research provides good insights about the seasonal variation of skid resistance in the UAE, which enhances road safety.

Introduction: The construction field is considered one of the most dangerous industries. Accidents and fatalities take place on a daily basis in construction projects. Globally, different levels of government have implemented strict rules and regulations to protect workers on job sites. However, despite the efforts to implement the rules and regulations, accidents occur frequently. Falling from heights is considered the most common cause of death in construction. This study developed a novel system integrating deep learning and drones to monitor workers in real-time when performing at-height activities. Method: Specifically, a pre-trained deep learning model was used to detect Personal Fall Arrest System components (e.g., safety harness, lifeline, and helmet). The drone was utilized to take images and videos from the construction site, and the data were relayed to the model to detect safety violations. The system was tested and validated in real construction sites and in a controlled lab environment to verify the model's effectiveness under different light and weather conditions. Results: The overall accuracy of the system was 90%. The model's precision and recall were 97.2 % and 90.2%, respectively. The average time taken to detect a violation was around 12 seconds. Conclusions: Moreover, the Area Under Curve - Receiver Operating Characteristics chart showed that the trained model was very good and precise in detecting and differentiating the desired objects. Practical applications: This fast, reliable, and economical system can aid in saving many lives if implemented and utilized properly in real construction sites.

The safety of workers at construction sites is one of the most important aspects that should be considered while performing their required tasks. Many rules and regulations have been implemented in the UAE to reduce injuries and fatalities in the jobsites. However, the number of accidents continues to increase. For instance, an accident category of fall-from-height is considered as the top cause of injuries and fatalities. Thus, this paper develops a novel technique that monitors the workers whether they are complying with a safety standard of the Personal Fall Arrest System (PFAS). This paper establishes a real time detection algorithm based on a Convolutional Neural Network (CNN) model in order to detect two main components of the PFAS that are, safety harness and life-line, in addition to a standard safety measure of using a safety helmet. The YOLOv3 algorithm is adopted for a deep learning network used to train the desired model. The model achieved an accuracy rate of 91.26% and around 99% precision. Moreover, the overall recall of the model was 90.2%. The obtained results verify the effectiveness of our proposed model in construction sites to control potential violations and to avoid unnecessary accidents. The main contribution of this paper is to provide an AI-based image detection framework to mitigate the likelihood of fall-from-height accidents.