Publicacións

@article{carro_artificial_2026,

title = {Artificial intelligence to predict critical events in port operations},

author = {Humberto Carro-Fidalgo and Andrés Figuero-Pérez and José Sande-González-Cela and Enrique Peña-González and Alberto Alvarellos-González},

url = {https://doi.org/10.1007/s00773-026-01109-y},

doi = {10.1007/s00773-026-01109-y},

issn = {1437-8213},

year  = {2026},

date = {2026-02-01},

urldate = {2026-02-01},

journal = {Journal of Marine Science and Technology},

abstract = {Port downtime is one of the most important economic and safety issues. The objective of this paper is the design of a predictive tool based on machine learning, capable of identifying downtimes. We have compared two approaches: one that uses the moored ship motions and follows a more theoretical approach. It is based on the physics of the problem, but has the difficulty of using movements that are obtained in a costly and complex way. The other option directly estimates the probability of downtime from ocean-meteorological data and ignores the moored ship motions. This is a simplification, as it omits movement constraints, but it is also more realistic, as it is very complex to obtain data on the state of moorings or estimated tension. The dataset uses ocean-meteorological forecast data and downtimes recorded during the port operations of 799 ships obtained during 8 years. We applied regression models to obtain the variables related to infragravity wave and moored ship motions. We performed a multiclass random forest classification by adjusting the weighting of the dataset to identify the most promising approach. We evaluated techniques such as random forest or gradient boosting machine, selecting the GBM for better validation performance, with a log-loss of 0.0103. We obtained F2 and F1 scores of 0.971 and 0.941 respectively, with which the model correctly classified 98% of the anchorages and 88% of the berthing disruptions. Error analysis by sea state and by stay record indicates that some of them are due to the subjectivity inherent in the anchoring phenomenon. The tool offers an alternative to the traditional analysis of significant movements through an easily exportable methodology.},

keywords = {Downtimes, Field campaign, machine learning, Port operability},

pubstate = {published},

tppubtype = {article}

}

@article{carro_machine_2024,

title = {Machine learning tool for wave overtopping prediction based on the safety-operability ratio},

author = {Humberto Carro-Fidalgo and José Sande-González-Cela and Andrés Figuero-Pérez and Alberto Alvarellos-González and Enrique Peña-González and Juan R. Rabuñal-Dopico and Andrés Guerra and Juan Diego Pérez},

url = {https://www.sciencedirect.com/science/article/pii/S0029801824023448},

doi = {10.1016/j.oceaneng.2024.119006},

issn = {0029-8018},

year  = {2024},

date = {2024-11-01},

urldate = {2026-04-28},

journal = {Ocean Engineering},

volume = {312},

pages = {119006},

abstract = {The phenomenon of wave overtopping affects both the safety of people and port facilities, as well as the operability of a port. This work aims to develop a predictive tool based on artificial intelligence to predict wave overtopping over a breakwater, and to reach a compromise between safeguarding people and port efficiency. The data used came from an extensive field campaign lasting 6.5 years. During this research, the overtopping events were recorded and integrated with the forecast data available from the Port. The model used is the random forest, which was applied with the metrics AUC-PR and F2 score to a weighted dataset. The validation results show a recall for alignments of 0.91 and 0.89, while in the test it reaches 0.94 and 0.95. The dangerousness of the wave overtopping was also analyzed, reducing the dangerous false negatives to 2 and to 14 in the two alignments studied. Operability per alignment was unnecessarily affected during validation by 4 and 10 days over 26.5 months, and in the test by 4 and 3 days over 6.5 months. The results show a proven tool with real data, capable of predicting wave overtopping, and a methodology that can be exported to other ports.},

keywords = {Field campaign, machine learning, Random forest, Wave overtopping},

pubstate = {published},

tppubtype = {article}

}

@article{costas_wind_2024,

title = {Wind influence and bathymetric effects in wave overtopping based on a long-term field campaign in the Outer Port of Punta Langosteira (Spain)},

author = {Raquel Costas and Andrés Figuero-Pérez and José Sande-González-Cela and Enrique Peña-González and Alberto Alvarellos-González and Andrés Guerra},

url = {https://doi.org/10.1080/20464177.2023.2255330},

doi = {10.1080/20464177.2023.2255330},

issn = {2046-4177},

year  = {2024},

date = {2024-11-01},

urldate = {2026-08-13},

journal = {Journal of Marine Engineering & Technology},

volume = {23},

number = {6},

pages = {383–395},

publisher = {Taylor & Francis},

abstract = {The operability of harbour facilities and the safety of those working within them can be compromised by overtopping. The aim of the present study is to analyse this phenomenon through a long-term field campaign, of 5 years’ duration, in the Outer Port of Punta Langosteira (Spain). Image monitoring techniques were used to identify the events, which allowed to study the influence of climatic variables and the distribution of overtopping. Results show that the place with the highest density of events was located in the central area of the third alignment. This turned out to be the area of sea wave concentration generated by the bathymetry, as shown by the propagation with a numerical model. Furthermore, wind with a magnitude greater than 30 km/h perpendicular to the breakwater increased the number of events per sea state, that is, the probability that the phenomenon will occur. In addition, the volume of 29 overtopping events and the mean discharge of 13 sea states were estimated using measurements from two pressure sensors on the crest of the breakwater. The results of this study allow to design a breakwater access warning system integrated with open access into the Port Authority’s Environmental Control Panel (http://cma.puertocoruna.com).},

keywords = {Field campaign, mean discharge, Overtopping, port management},

pubstate = {published},

tppubtype = {article}

}

@article{sande_field_2022,

title = {Field Campaign on Pressure on the Crown Wall at the Outer Port of Punta Langosteira Breakwater},

author = {José Sande-González-Cela and Maria Graça Neves and José-Santos López-Gutiérrez and M. Dolores Esteban and Andrés Figuero-Pérez and Vicente Negro},

url = {https://www.mdpi.com/2077-1312/10/10/1377},

doi = {10.3390/jmse10101377},

issn = {2077-1312},

year  = {2022},

date = {2022-10-01},

urldate = {2026-08-10},

journal = {Journal of Marine Science and Engineering},

volume = {10},

number = {10},

pages = {1377},

publisher = {Multidisciplinary Digital Publishing Institute},

abstract = {Punta Langosteira port, located in A Coruña (Spain), was monitored during the winters of 2017 and 2018, measuring wave pressure in the crown wall structure. Furthermore, the metocean variables were measured on a buoy located very close to the breakwater. This paper presents the real pressures measured at the crown wall of the breakwater during different storm events. These values are compared with the results of the application of state-of-the-art equations for the calculation of pressures on crown walls. The results obtained show the behaviour of the pressures with a crown wall fully protected by the armour, as is the case of Langosteira breakwater. Finally, several conclusions are made on the methodology for measuring the pressures using physical models and the relevance of the armour roughness.},

keywords = {crown wall, Field campaign, pressure, rubble mound breakwater},

pubstate = {published},

tppubtype = {article}

}