Dr Umair Iqbal

Blockage of cross-drainage hydraulic structures is a significant concern in water resources and civil engineering projects, particularly in urban areas experiencing increased debris supply. During storms or floods, debris can accumulate and restrict the flow capacity of these structures, leading to potential failures and adverse impacts on flood levels. While some argue that blockage at culverts is a non-issue, scientific research supports its significance in specific regions. However, in context of rivers and dams, blockage by Large Wood (LW) is an established issue with plenty of research in terms of its hydraulic impacts, dynamics, modeling and scouring impacts. Specifically in Australasia the Australian Rainfall and Runoff (ARR) initiative recognized the importance of studying blockage at culverts and introduced guidelines incorporating it into design and modeling. These guidelines also included post flood visual inspections of structures to understand blockage, however, this approach has been criticized by hydraulic engineers arguing that post flood visuals can not be considered as the representation of the peak floods blockage. Recently, an approach of using visual information to interpret the blockage has been adopted as a new dimension to the problem. This paper, therefore, highlights the advances, challenges, and opportunities in studying blockage, emphasizing the need for data-driven approaches and interdisciplinary collaboration. Understanding and addressing blockage are crucial for ensuring the efficient operation and longevity of hydraulic structures and promoting the resilience of infrastructure systems in the face of evolving environmental conditions.

Public transportation systems play a vital role in modern cities, but they face growing security challenges, particularly related to incidents of violence. Detecting and responding to violence in real time is crucial for ensuring passenger safety and the smooth operation of these transport networks. To address this issue, we propose an advanced artificial intelligence (AI) solution for identifying unsafe behaviours in public transport. The proposed approach employs deep learning action recognition models and utilises technologies like NVIDIA DeepStream SDK, Amazon Web Services (AWS) DirectConnect, local edge computing server, ONNXRuntime and MQTT to accelerate the end-to-end pipeline. The solution captures video streams from remote train stations closed circuit television (CCTV) networks, processes the data in the cloud, applies the action recognition model, and transmits the results to a live web application. A temporal pyramid network (TPN) action recognition model was trained on a newly curated video dataset mixing open-source resources and live simulated trials to identify the unsafe behaviours. The base model was able to achieve a validation accuracy of 93% when trained using open-source dataset samples and was improved to 97% when live simulated dataset was included during the training. The developed AI system was deployed at Wollongong Train Station (NSW, Australia) and showcased impressive accuracy in detecting violence incidents during an 8-week test period, achieving a reliable false-positive (FP) rate of 23%. While the AI correctly identified 30 true-positive incidents, there were 6 cases of false negatives (FNs) where violence incidents were missed during the rainy weather suggesting more data in the training dataset related to bad weather. The AI model's continuous retraining capability ensures its adaptability to various real-world scenarios, making it a valuable tool for enhancing safety and the overall passenger experience in public transport settings.