Automated prediction of crack propagation using H2O AutoML

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dc.contributor.authorOmar, Intisar
dc.contributor.authorKhan, Muhammad
dc.contributor.authorStarr, Andrew
dc.contributor.authorAbou Rok Ba, Khaled
dc.date.accessioned2023-10-18T10:17:33Z
dc.date.available2023-10-18T10:17:33Z
dc.date.issued2023-10-12
dc.description.abstractCrack propagation is a critical phenomenon in materials science and engineering, significantly impacting structural integrity, reliability, and safety across various applications. The accurate prediction of crack propagation behavior is paramount for ensuring the performance and durability of engineering components, as extensively explored in prior research. Nevertheless, there is a pressing demand for automated models capable of efficiently and precisely forecasting crack propagation. In this study, we address this need by developing a machine learning-based automated model using the powerful H2O library. This model aims to accurately predict crack propagation behavior in various materials by analyzing intricate crack patterns and delivering reliable predictions. To achieve this, we employed a comprehensive dataset derived from measured instances of crack propagation in Acrylonitrile Butadiene Styrene (ABS) specimens. Rigorous evaluation metrics, including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R2) values, were applied to assess the model’s predictive accuracy. Cross-validation techniques were utilized to ensure its robustness and generalizability across diverse datasets. Our results underscore the automated model’s remarkable accuracy and reliability in predicting crack propagation. This study not only highlights the immense potential of the H2O library as a valuable tool for structural health monitoring but also advocates for the broader adoption of Automated Machine Learning (AutoML) solutions in engineering applications. In addition to presenting these findings, we define H2O as a powerful machine learning library and AutoML as Automated Machine Learning to ensure clarity and understanding for readers unfamiliar with these terms. This research not only demonstrates the significance of AutoML in future-proofing our approach to structural integrity and safety but also emphasizes the need for comprehensive reporting and understanding in scientific discourse.en_UK
dc.identifier.citationOmar I, Khan M, Starr A, Abou Rok Ba K. (2023) Automated prediction of crack propagation using H2O AutoML. Sensors, Volume 23, Issue 20, October 2023, Article number 8419en_UK
dc.identifier.issn1424-8220
dc.identifier.urihttps://doi.org/10.3390/s23208419
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/20392
dc.language.isoenen_UK
dc.publisherMDPIen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectAcrylonitrile Butadiene Styrene (ABS)en_UK
dc.subjectautomated machine learningen_UK
dc.subjectH2Oen_UK
dc.subjectcrack propagation predictionen_UK
dc.subjecthyperparameter tunningen_UK
dc.titleAutomated prediction of crack propagation using H2O AutoMLen_UK
dc.typeArticleen_UK

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