PSI - Issue 77

ScienceDirect Available online at www.sciencedirect.com ScienceDirect Structural Integrity Procedia 00 (2026) 000–000 Available online at www.sciencedirect.com ScienceDirect Structural Integrity Procedia 00 (2026) 000–000 Available online at www.sciencedirect.com Procedia Structural Integrity 77 (2026) 601–610

www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia

© 2026 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of ICSI organizers Abstract Learning models based on hierarchical complexity reflect the way humans naturally acquire knowledge, and experimental evidence suggests they hold promise for improving the efficiency of model training in artificial intelligence (AI). This research presents an innovative approach to developing an AI model capable of classifying maritime acoustic signals, for ship identification or structural integrity assessment. Acoustic signal analysis is critical in maritime environments, as sound travels effectively underwater, offering potential for applications where above-water technologies are not possible. Nonetheless, decoding these acoustic signals is a complex task that presents significant computational challenges. This study applies the Model of Hierarchical Complexity (MHC) to maritime acoustic signal recognition. A domain-specific Order of Hierarchical Complexity was proposed, and three training configurations on a ResNet-18 backbone were evaluated under identical architecture and hyperparameters: traditional non structured learning, two-stage (binary, multiclass), and full three-stage MHC-structured training. The dataset was strongly imbalanced across the 12 classes (11 vessel types and background), reflecting real maritime traffic; this realism introduced constraints on rare categories, lowering the model performance. The full MHC configuration achieved the best overall metrics (accuracy 0.82 and the highest macro-averaged precision, recall, and F1) with 1% better training time, comparable to the other tests. Improvements were concentrated in well-represented classes, indicating that MHC-structured training can organize learning without additional computational cost but does not, by itself, overcome class imbalance. © 2026 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of ICSI organizers Keywords: Hierarchical Complexity; Acoustic Signal Recognition; Efficient Artificial Intelligence International Conference on Structural Integrity Hierarchical complexity-based AI model for efficient feature extraction in maritime acoustic signal recognition Hugo Mesquita Vasconcelos a *, Pedro J. S. C. P. Sousa a,b , António Silva a,b , Susana Dias a , J. P. Pinto c , I. D. van Golde c , Paulo J. Tavares a , Pedro M. G. J. Moreira a a INEGI, Rua Dr. Roberto Frias 400, 4200-465 Porto, Portugal b Faculty of Engineering of the University of Porto, 4200-465 Porto, Portugal c Instituto Hidrográfico, 1249-093 Lisboa, Portugal Abstract Learning models based on hierarchical complexity reflect the way humans naturally acquire knowledge, and experimental evidence suggests they hold promise for improving the efficiency of model training in artificial intelligence (AI). This research presents an innovative approach to developing an AI model capable of classifying maritime acoustic signals, for ship identification or structural integrity assessment. Acoustic signal analysis is critical in maritime environments, as sound travels effectively underwater, offering potential for applications where above-water technologies are not possible. Nonetheless, decoding these acoustic signals is a complex task that presents significant computational challenges. This study applies the Model of Hierarchical Complexity (MHC) to maritime acoustic signal recognition. A domain-specific Order of Hierarchical Complexity was proposed, and three training configurations on a ResNet-18 backbone were evaluated under identical architecture and hyperparameters: traditional non structured learning, two-stage (binary, multiclass), and full three-stage MHC-structured training. The dataset was strongly imbalanced across the 12 classes (11 vessel types and background), reflecting real maritime traffic; this realism introduced constraints on rare categories, lowering the model performance. The full MHC configuration achieved the best overall metrics (accuracy 0.82 and the highest macro-averaged precision, recall, and F1) with 1% better training time, comparable to the other tests. Improvements were concentrated in well-represented classes, indicating that MHC-structured training can organize learning without additional computational cost but does not, by itself, overcome class imbalance. © 2026 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of ICSI organizers Keywords: Hierarchical Complexity; Acoustic Signal Recognition; Efficient Artificial Intelligence International Conference on Structural Integrity Hierarchical complexity-based AI model for efficient feature extraction in maritime acoustic signal recognition Hugo Mesquita Vasconcelos a *, Pedro J. S. C. P. Sousa a,b , António Silva a,b , Susana Dias a , J. P. Pinto c , I. D. van Golde c , Paulo J. Tavares a , Pedro M. G. J. Moreira a a INEGI, Rua Dr. Roberto Frias 400, 4200-465 Porto, Portugal b Faculty of Engineering of the University of Porto, 4200-465 Porto, Portugal c Instituto Hidrográfico, 1249-093 Lisboa, Portugal

* Corresponding author. Tel.: +351 22 957 8710 E-mail address: hmesquita@inegi.up.pt * Corresponding author. Tel.: +351 22 957 8710 E-mail address: hmesquita@inegi.up.pt

2452-3216 © 2026 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of ICSI organizers 10.1016/j.prostr.2026.01.076 2452-3216 © 2026 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of ICSI organizers 2452-3216 © 2026 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of ICSI organizers