PSI - Issue 68

ScienceDirect Structural Integrity Procedia 00 (2025) 000–000 Structural Integrity Procedia 00 (2025) 000–000 Available online at www.sciencedirect.com Available online at www.sciencedirect.com ScienceDirect Available online at www.sciencedirect.com ScienceDirect

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

Procedia Structural Integrity 68 (2025) 1066–1073

European Conference on Fracture 2024 Hydrogen-assisted fatigue crack growth in pipeline steels: a machine learning approach Hamidreza Rohani Raftar a , Mahdieh Safyari a , Masoud Moshtaghi a * a Laboratory of Steel Structures, LUT University, P.O. Box 20, 53851 Lappeenranta, Finland Abstract This study presents a machine learning methodology aimed at predicting the fatigue crack growth rate and examining the impact of alloying elements on hydrogen environment embrittlement in pipeline steels. The research marks the inaugural exploration of such predictive techniques in this specific domain. A predictive model was constructed using the bagged trees method, chosen for its ability to minimize metric errors like Mean Squared Error and Root Mean Squared Error. The accuracy of the model was confirmed, demonstrating its reliability in predicting outcomes effectively. Feature importance scores reveal that hydrogen pressure is a significant environmental factor, aligning with expectations. Moreover, the analysis identifies Mn and S as primary features that exert a strong negative influence on hydrogen environment embrittlement in pipeline steels. The machine learning approach is anticipated to be valuable for comprehending the behavior of pipeline steel under hydrogenous conditions and can be applied to enhance fatigue resistance in hydrogen applications. © 2025 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 ECF24 organizers Keywords: Hydrogen embrittlement; Fatigue; Pipeline steel; Fracture mechanics; Hydrogen gas transport; Machine learning. 1. Introduction Hydrogen energy has caught significant attention as a renewable energy option for the future, primarily due to its potential to decrease CO 2 emissions [1–4]. Nevertheless, the advancement of hydrogen energy into commercial European Conference on Fracture 2024 Hydrogen-assisted fatigue crack growth in pipeline steels: a machine learning approach Hamidreza Rohani Raftar a , Mahdieh Safyari a , Masoud Moshtaghi a * a Laboratory of Steel Structures, LUT University, P.O. Box 20, 53851 Lappeenranta, Finland Abstract This study presents a machine learning methodology aimed at predicting the fatigue crack growth rate and examining the impact of alloying elements on hydrogen environment embrittlement in pipeline steels. The research marks the inaugural exploration of such predictive techniques in this specific domain. A predictive model was constructed using the bagged trees method, chosen for its ability to minimize metric errors like Mean Squared Error and Root Mean Squared Error. The accuracy of the model was confirmed, demonstrating its reliability in predicting outcomes effectively. Feature importance scores reveal that hydrogen pressure is a significant environmental factor, aligning with expectations. Moreover, the analysis identifies Mn and S as primary features that exert a strong negative influence on hydrogen environment embrittlement in pipeline steels. The machine learning approach is anticipated to be valuable for comprehending the behavior of pipeline steel under hydrogenous conditions and can be applied to enhance fatigue resistance in hydrogen applications. © 2025 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 ECF24 organizers Keywords: Hydrogen embrittlement; Fatigue; Pipeline steel; Fracture mechanics; Hydrogen gas transport; Machine learning. 1. Introduction Hydrogen energy has caught significant attention as a renewable energy option for the future, primarily due to its potential to decrease CO 2 emissions [1–4]. Nevertheless, the advancement of hydrogen energy into commercial © 2025 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 ECF24 organizers

* Corresponding author. Tel.: +358-505650973. E-mail address: masoud.moshtaghi@lut.fi * Corresponding author. Tel.: +358-505650973. E-mail address: masoud.moshtaghi@lut.fi

2452-3216 © 2025 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 ECF24 organizers 2452-3216 © 2025 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 ECF24 organizers

2452-3216 © 2025 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 ECF24 organizers 10.1016/j.prostr.2025.06.171