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This work analyzes compatibility between dynamic response data and prominent machine learning approaches to identify suitable, effective, and understandable models for crack propagation.",{"@graph":14,"@context":65},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/suitability-analysis-of-machine-learning-algorithms-for-crack-growth-prediction-based-on-dynamic-response-data/290298/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/suitability-analysis-of-machine-learning-algorithms-for-crack-growth-prediction-based-on-dynamic-response-data/290298.png","ImageObject",300,407,{"name":42,"@type":43},"Lucas Martin","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-11-06","2026-09-17",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",9,{"@type":57,"mainEntity":58},"FAQPage",[59],{"name":60,"@type":61,"acceptedAnswer":62},"What was the impact of crack location along the samples?","Question",{"text":63,"@type":64},"Crack location along samples had no significant impact on either material in the reported results.","Answer","https://schema.org",{"og:url":32,"og:type":67,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":69,"canonical":32},"index,follow",{"doc_id":71,"site_id":7},290298,1790170088,{"code":4,"msg":74,"data":75},"success",[76,80,84,88,93,98,103,107,111,114,118],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":77,"show_sort_weight":78,"slug":79},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":81,"show_sort_weight":82,"slug":83},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Exam",70,"exam",{"id":89,"doc_module":4,"doc_module_name":25,"category_name":90,"show_sort_weight":91,"slug":92},5,"Comic",60,"comic",{"id":94,"doc_module":4,"doc_module_name":25,"category_name":95,"show_sort_weight":96,"slug":97},6,"Technology",50,"technology",{"id":99,"doc_module":4,"doc_module_name":25,"category_name":100,"show_sort_weight":101,"slug":102},7,"Healthcare",40,"healthcare",{"id":104,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":105,"slug":106},8,30,"research-report",{"id":55,"doc_module":4,"doc_module_name":25,"category_name":108,"show_sort_weight":109,"slug":110},"Religion & Spirituality",20,"religion-spirituality",{"id":109,"doc_module":4,"doc_module_name":25,"category_name":112,"show_sort_weight":109,"slug":113},"World Cup","world-cup",{"id":115,"doc_module":4,"doc_module_name":25,"category_name":116,"show_sort_weight":115,"slug":117},10,"Lifestyle","lifestyle",{"id":119,"doc_module":4,"doc_module_name":25,"category_name":120,"show_sort_weight":89,"slug":121},19,"General","general",{"code":4,"msg":74,"data":123},{"doc_id":71,"user_id":124,"nickname":42,"user_avatar":125,"doc_module":4,"category_id":104,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":126,"file_id":127,"file_url":128,"file_type":129,"file_size":130,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":131,"language":132,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":133,"faqs":134,"seo_title":135,"seo_description":12,"update_tm":136,"read_time":137,"preview_page_count":4},8796095360427,"https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d","sensors   \nArticle  \nSuitability Analysis of Machine Learning Algorithms for Crack Growth Prediction Based on Dynamic Response Data  \nIntisar Omar *, Muhammad Khan * and Andrew Starr   \nCitation: Omar, I.; Khan, M.; Starr, A. Suitability Analysis of Machine Learning Algorithms for Crack Growth Prediction Based on  \nDynamic Response Data. Sensors 2023, 23, 1074. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/s23031074](10.3390/s23031074)  \nAcademic Editor: Hossam A. Gabbar  \nReceived: 10 December 2022  \nRevised: 9 January 2023  \nAccepted: 12 January 2023  \nPublished: 17 January 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \nSchool of Aerospace, Transport and Manufacturing, Cran􀀂eld University, Bedford MK43 0AL, UK  \n* Correspondence: i.omar@cran􀀂eld.ac.uk (I.O.); muhammad.a.khan@cran􀀂eld.ac.uk (M.K.)  \nAbstract: Machine learning has the potential to enhance damage detection and prediction in materials science. Machine learning also has the ability to produce highly reliable and accurate representations, which can improve the detection and prediction of damage compared to the traditional knowledgebased approaches. These approaches can be used for a wide range of applications, including material design; predicting material properties; identifying hidden relationships; and classifying microstructures, defects, and damage. However, researchers must carefully consider the appropriateness of various machine learning algorithms, based on the available data, material being studied, and desired knowledge outcomes. In addition, the interpretability of certain machine learning models can be alimitation in materials science, as it may be dif􀀂cult to understand the reasoning behind predictions. This paper aims to make novel contributions to the 􀀂eld of material engineering by analyzing the compatibility of dynamic response data from various material structures with prominent machine learning approaches. The purpose of this is to help researchers choose models that are both effective and understandable, while also enhancing their understanding of the model's predictions. To achieve this, this paper analyzed the requirements and characteristics of commonly used machine learning algorithms for crack propagation in materials. This analysis assisted the authors in selecting machine learning algorithms (K nearest neighbor, Ridge, and Lasso regression) to evaluate the dynamic response of aluminum and ABS materials, using experimental data from previous studies to train the models. The results showed that natural frequency was the most signi􀀂cant predictor for ABS material, while temperature, natural frequency, and amplitude were the most important predictors for aluminum. Crack location along samples had no signi􀀂cant impact on either material. Future work could involve applying the discussed techniques to a wider range of materials under dynamic loading conditions.  \nKeywords: machine learning; K nearest neighbor; support vector machine; ridge regression; arti􀀂cial neural network; least absolute shrinkage and selection operator (LASSO) regression; suitable machine learning model  \n1. Introduction  \nMachine learning approaches are crucial for allowing material engineers and scientists to accelerate the development of new materials, techniques, and processes. One of the main goals of using these approaches in materials science is to quickly identify and quantify key features along the process􀂖performance􀂖property􀂖structure chain. The selection of the most suitable machine learning approaches for predicting structural damage should be carefully considered, taking into account the type of data available, the material ","cbCaid20ZtGRKDUe","https://ap.wps.com/l/cbCaid20ZtGRKDUe","pdf",4285673,26,"English","# 1. Introduction\n## Machine learning in materials science\n## Goals and feature identification\n## Algorithm selection considerations\n## Applications to crack detection and prediction","[{\"question\":\"What was the impact of crack location along the samples?\",\"answer\":\"Crack location along samples had no significant impact on either material in the reported results.\"}]","Suitability Analysis of Machine Learning Algorithms for Crack Growth Prediction Based on Dynamic Response Data | PDF",1789641263,66]