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AI in Civil Engineering: practical cases
* 1 , 2
1  Computer Science Department, Research Center on Information and Communication Technologies, University of A Coruña, 15071A Coruña, Spain
2  Computer Science Department, Center of Technological Innovations in Construction and Civil Engineering, University of A Coruña, 15071 A Coruña, Spain


This work focuses on port management and the hydrodynamic impact that waves, tides and wind have on moored cargo ships and port infrastructures. We present two examples where Artificial Intelligence (AI) was applied to port management tasks: monitoring the movement of a moored vessel and monitoring wave overtopping events.
Monitoring the movement of a moored vessel is a costly, and not always feasible, operation. We have recorded ship movements during two years utilizing a technique developed in a previous work of the group, and designed, trained and tested a deep neural network that classifies the movement of cargo ships given the sea state, weather conditions and ship dimensions. Using the model with forecast data for the weather conditions and sea state, we can predict when a ship is going to exceed the recommended movement, helping to decide when to stop the ship operations more precisely, thus minimizing the economic impact the hydrodynamic phenomena have due to cargo ship been unable to operate.
Wave overtopping is a dangerous phenomenon. When it occurs in a commercial port environment, the best case scenario will be the disruption of activities and even this best case scenario has a negative financial repercussion. We recorded three years of overtopping phenomena and created a database with overtopping events, sea state and weather conditions. This data is being use to create an overtopping predictor based on neural networks. Using the predictor with forecast data for the weather conditions and sea state, we will be able to predict when an overtopping event is going to take place.

Keywords: Deep Learning; overtopping predictor; AI; vessel movement