[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124254-en":3,"doc-seo-124254-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124254,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","MACHINE LEARNING (ML) APPROACHES TO MODEL INTERDEPENDENCIES BETWEEN DYNAMIC LOADS AND CRACK PROPAGATION - PhD Thesis","Machine learning for structural health and crack prediction is central to improving damage detection and prediction accuracy, efficiency, and reliability. This research investigates ML-based Structural Health Monitoring across composites, metals, and polymers, highlighting key implementation challenges such as limited data, limited model interpretability, overfitting, and the lack of clear guidance for selecting ML models. Using dynamic response data, the study derives crack-depth predictors for materials including aluminum, concrete, and 3D-printed ABS, validates selected ML models, and assesses performance with Mean Squared Error on training and test sets. The work proposes an automated crack-propagation model for ABS using the H2O library and concludes with recommendations for future ML advances, feature expansion, and broader real-world evaluation.","Intisar Omar  \nMACHINE LEARNING (ML) APPROACHES TO MODEL INTERDEPENDENCIES BETWEEN DYNAMIC LOADS AND  \nCRACK PROPAGATION  \nSCHOOL OF AEROSPACE, TRANSPORT AND MANUFACTURING, CRANFIELD UNIVERSITY Mechanical Engineering  \nA Degree of Doctor of Philosophy in the Artificial Intelligence for  \nStructural Health Monitoring  \nAcademic Year: 2020-2023  \nSupervisor:Muhammad Khan Associate Supervisor: Andrew Starr September 2023  \nSCHOOL OF AEROSPACE, TRANSPORT AND MANUFACTURING, CRANFIELD UNIVERSITY Mechanical Engineering  \nPhD  \nAcademic Year 2020-2023  \nIntisar Omar  \nMACHINE LEARNING (ML) APPROACHES TO MODEL INTERDEPENDENCIES BETWEEN DYNAMIC LOADS AND  \nCRACK PROPAGATION  \nSupervisor: Muhammad Khan Associate Supervisor: Andrew Starr September 2023  \nThis thesis is submitted in partial fulfilment of the requirements for  \nthe degree of PhD.  \n© Cranfield University 2023. All rights reserved. No part of this publication may be reproduced without the written permission of the  \ncopyright owner.  \nAcademic integrity declaration  \nI declare that:  \n• the thesis submitted has been written by me alone.  \n• the thesis submitted has not been previously submitted to this university or any other.  \n• that all content, including primary and/or secondary data, is true to the best of my knowledge.  \n• that all quotations and references have been duly acknowledged according to the requirements of academic research.  \nI understand that to knowingly submit work in violation of the above statement will be considered by examiners as academic misconduct.  \nAbstract  \nThe application of machine learning in structural health and crack prediction is of paramount importance, as it offers the potential to enhance the accuracy, efficiency, and reliability of detecting and predicting damage in various materials and structures. This research presents an in-depth exploration of machine learning (ML) applications in the field of Structural Health Monitoring (SHM) across various materials, including composites, metals, and polymers. The study identifies the current challenges in implementing ML in SHM, such as data sparsity, interpretability of ML models, overfitting, and the absence of general guidelines for ML model selection.  \nThe research analyses the dynamic response data of different materials and establishes significant crack depth predictors for materials such as aluminum, concrete, and 3D-printed Acrylonitrile Butadiene Styrene (ABS) . It further investigates and validates selected ML models to predict crack depth in different materials. The models' performance is evaluated using Mean Squared Error (MSE) on both training and test sets, demonstrating their ability to capture meaningful patterns within the data and make reasonably accurate predictions. A significant contribution of this study is the proposal of an automated model utilizing the H2O library for crack propagation prediction in ABS materials. This model demonstrates the potential of automation in SHM, offering substantial benefits for structural integrity assessment, maintenance strategies, and materials design in various industries. This research concludes with recommendations for future research, including the exploration of advanced ML algorithms, investigation of additional predictive features, and evaluation of the models in different real-world scenarios.  \nKeywords:  \nMachine learning, Structural health monitoring and assessment, ABS, Aluminum, Concrete, Automated Machine learning, H2O library, Python.  \nAcknowledgements  \nWith a sorrowful heart, I dedicate this endeavour to the memory of my dear brother, who departed from this world on the 18th of July 2023. As I present my research, I am reminded of his unwavering encouragement and his belief in my abilities. Though he is no longer with us, his spirit continues to inspire me to strive for excellence.  \nI would like to express my sincere gratitude to my supervisors, Dr. Muhammad Khan, and Prof. Andrew Starr, for their invaluable guidan","cbCaiqhNQDlrsGnU","https://ap.wps.com/l/cbCaiqhNQDlrsGnU","pdf",5597235,1,221,"English","en",105,"# Abstract\n# Keywords\n# Acknowledgements\n# List of Publication","[{\"question\":\"What is the main objective of this research?\",\"answer\":\"To explore machine learning approaches for Structural Health Monitoring, especially for predicting crack propagation and crack depth from dynamic response data.\"},{\"question\":\"Which materials and prediction targets are studied?\",\"answer\":\"The study focuses on crack depth and crack propagation for materials including aluminum, concrete, and 3D-printed ABS.\"},{\"question\":\"How are the machine learning models evaluated?\",\"answer\":\"Model performance is evaluated using Mean Squared Error (MSE) on both training and test sets to measure how well meaningful patterns are captured and predictions are made.\"}]","MACHINE LEARNING (ML) APPROACHES TO MODEL INTERDEPENDENCIES BETWEEN DYNAMIC LOADS AND CRACK PROPAGATION - PhD Thesis | PDF",1785821245,557,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-ml-approaches-to-model-interdependencies-between-dynamic-loads-and-crack-propagation-phd-thesis","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-ml-approaches-to-model-interdependencies-between-dynamic-loads-and-crack-propagation-phd-thesis/124254/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main objective of this research?","Question",{"text":75,"@type":76},"To explore machine learning approaches for Structural Health Monitoring, especially for predicting crack propagation and crack depth from dynamic response data.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which materials and prediction targets are studied?",{"text":80,"@type":76},"The study focuses on crack depth and crack propagation for materials including aluminum, concrete, and 3D-printed ABS.",{"name":82,"@type":73,"acceptedAnswer":83},"How are the machine learning models evaluated?",{"text":84,"@type":76},"Model performance is evaluated using Mean Squared Error (MSE) on both training and test sets to measure how well meaningful patterns are captured and predictions are made.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]