The increasing size of cargo vessels poses significant challenges for ports in ensuring safe mooring, as larger ships result in higher mooring forces.
Most existing port infrastructure and mooring equipment were designed for smaller ships, making accurate estimation of mooring line forces increasingly critical.
Metamodels, machine learning models trained on numerically simulated data, offer a promising alternative to traditional, computationally expensive simulation-based methods by enabling rapid predictions with a useful level of accuracy.
This study proposes a metamodeling approach for the numerical Dynamic Mooring Analysis (DMA) to predict mooring line forces from input parameters that describe environmental conditions, mooring systems, and ship characteristics.
The methodology is demonstrated in a case study involving a 333-meter container vessel moored at a berth in the Port of Rotterdam.
A total of 11,520 scenarios were simulated using the DMA model aNySIM and used to train and test two candidate metamodels: Linear Regression (LR) and Multilayer Perceptron (MLP).
After evaluating both models on predictive accuracy, efficiency in terms of prediction speed and development effort, and interpretability, the MLP was selected as the preferred DMA metamodel.
It achieved high predictive performance, with an RMSE of 10 kN and an R² of 0.996, while offering prediction times measured in microseconds. This is more than seven orders of magnitude faster than the numerical DMA, thereby enabling large-batch predictions.
The metamodel revealed that pretension is clearly the most dominant feature for predicting the mean mooring line force, followed by MBL. For the maximum mooring line force, the most influential features were identified as pretension, windvelocity, and wind direction.