Structural integrity, health and safety of hydraulic structures including dams.

My research area delves into the issues surrounding the structural health and safety of hydraulic structures such as dams and levees. These structures provide a plethora of benefits; the fundamental purpose of a dam is to facilitate the storage of water. These benefits include, but are not limited to, flood control, hydropower, and human consumption. The structural health should be ensured to avoid the catastrophic event of dam failure, which can lead to severe consequences such as loss of property and even death. The focal point of my research will lie within predicting failure mechanisms of real-life hydraulic structures within the United Kingdom. The main failure mechanism I wish to focus on is breach by overtopping because dating back from 1799 to 1925, the British Dam Society attributes the most common modes of dam failure in the United Kingdom to overtopping during flooding. The Environment Agency (2018) reports in UKCP18 that the projections show an increase in winter precipitation over the United Kingdom, as a consequence of climate change. There will be times during the year where dams will be subjected to store more water due to the increased precipitation, increasing the risk of overtopping. As the years go on, the trend of rainfall events is expected to further add to the risk of overtopping.
Currently, the accuracy in prediction of natural disasters such as flooding events present as a major challenge to the engineering world. A range of techniques have been used to aid in prediction of flooding. Some of these techniques included variants of the Monte Carlo method or digital modelling simulations, by Mohamed (2018) and Fenech et al (2019) respectively. Literature suggests there is still much room for development within the accuracy of prediction methods for flood estimation. Over recent years, cognitive tasks thought to be only mastered by humans have been developed through artificial intelligence, namely neural networks. Neural networks are set by algorithms to detect and recognise certain patterns. Cichy and Kaiser's study in 2019 explored the powerful algorithms that are deep neural networks (DNN), which are modelled based on the brain. It was found that DNNs were able to predict human brain responses better than other models. The authors of this study encourage embracing DNNs as a useful model as their capacity for prediction is powerful. This positive notion regarding DNNs is further supported by Jin et al. in 2019. The study on projection neural network with robotics and model predictive control yielded results that favoured the neural network model.
I aim to incorporate neural networks to enhance the accuracy and precision of the prediction of increased water levels and as well as any other anomalies within the hydraulic structure in the future. The neural network would also consider the history of the chosen hydraulic structure. Therefore, to accomplish this task, I aim to create a neural network architecture to aid in time series prediction and forecasting. As current literature strongly supports the use of a hybrid of CNNs and LSTMs for forecasting, I would use this architecture type to create a model that can help predict failure mechanisms of hydraulic structures. This would be carried out by installing sensors on a hydraulic structure to obtain real-time data. The sensors will be installed on to a real-world dam regulated by the Environment Agency. This will be achieved through communications and the link the supervisor has with the agency and other industrial partners, such as Costain ltd who own many of these structures. The data will be fed into a deep learning model for training purposes which will then be used to evaluate the structural health safety of the dam and predict its performance under extreme weather events. The monitoring process would be carried out over an 18-month period to obtain a range of data to account for seasonal variation.