In naval engineering, particular attention has been given to containerships, as these structures are constantly exposed to potential damage during service hours and since they are essential for large-scale transportation. To assess the structural integrity of these ships and to ensure the safety of the crew and the cargo being transported, it is essential to adopt structural health monitoring (SHM) strategies that enable real-time evaluations of a ship’s status. To achieve this, this paper introduces an advancement in the field of smart sensing and SHM that improves ship monitoring and diagnostic capabilities. This is accomplished by a framework that combines the inverse finite element method (iFEM) with the definition of an optimal Fiber Bragg Gratings-based sensor network for the reconstruction of the full field of displacement; strain; and finally, cross-section internal forces. The optimization of the sensor network was performed by defining a multi-objective function that simultaneously considers the accuracy of the displacement field reconstruction and the associated cost of the sensor network. The framework was successfully applied to a mid-portion of a containership case, demonstrating its effective applicability in real and complex scenarios.

In practical applications, the prediction of the explosive mass of an underwater explosion represents a crucial aspect for defining extreme scenarios and for assessing damage, implementing defensive and security strategies, and ensuring the structural integrity of marine structures. In this study, a deep neural network (DNN) was developed to predict the mass of an underwater explosive charge, by means of the transfer learning technique (TL). Both DNN and TL methods utilized data collected through coupled Eulerian–Lagrangian numerical simulations performed through the suite MSC Dytran. Different positions and masses of the charge, seabed typology, and distance between the structure and seabed have been considered within the dataset. All the features considered as input for the machine learning model are information that the crew is aware of through onboard sensors and instrumentations, making the framework extremely useful in real-world scenarios. TL involves reconfiguring and retraining a new DNN model, starting from a pre-trained network model developed in a past study by the authors, which predicted the spatial position of the explosive. This study serves as a proof of concept that using transfer learning to create a DNN model from a pre-trained network requires less computational effort compared to building and training a model from scratch, especially considering the vast amount of data typically present in real-world scenarios.