[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118088-en":3,"doc-seo-118088-105":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},118088,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","LOAD-INDUCED CRACK PREDICTION USING HYBRID MECHANISTIC MACHINE LEARNING MODELS - A Thesis","This thesis implements hybrid mechanistic-machine learning models to predict load-induced cracking in concrete beams without transverse reinforcement. Accurate prediction of load-induced cracking supports robust shear capacity assessment. Pure mechanistic approaches cannot represent load-induced cracking flexibility, while data-driven machine learning models require larger datasets than typically available. The study develops Hybrid Learning theory, determines optimal mechanistic–learning model combinations, and proposes a modeling framework that minimizes mechanistic bias while maintaining sufficient constraint. The framework enables mechanistically consistent predictions with flexibility and interpretability.","LOAD-INDUCED CRACK PREDICTION USING HYBRID MECHANISTIC  \nMACHINE LEARNING MODELS  \nA Thesis  \nby  \nJACOB IAN PAVELKA  \nSubmitted to the Graduate and Professional School of Texas A&M University  \nin partial fulfillment of the requirements for the degree of  \nMASTER OF SCIENCE  \nChair of Committee, Committee Members,  \nHead of Department,  \nStephanie Paal Maria Koliou Raymundo Arróyave Zachary Grasley  \nDecember 2022  \nMajor Subject: Civil Engineering  \nCopyright 2022 Jacob Pavelka  \nABSTRACT  \nThis thesis implements hybrid mechanistic-machine learning models to predict load-induced cracking in concrete beams without transverse (applied along the depth of the beam) reinforcement. Predicting load-induced cracking is crucial for robustly predicting shear capacity. Mechanistic models lack the flexibility to represent load-induced cracking, and machine learning models lack a sufficient data set to learn load-induced cracking relationships. Hybrid models have the best chance of accurately predicting load-induced cracking. To implement hybrid modeling, we developed the Hybrid Learning theory and identified optimal combinations of mechanistic and machine learning models. Additionally, we developed a framework that has low mechanistic bias and sufficient constraint. This framework will allow for mechanistically consistent predictions. Hybrid models have great potential for modeling in Structural Engineering because of their flexibility and interpretability, and robust prediction of shear capacity will lead to increased design efficiency and understanding of concrete beam failure mechanics.  \nCONTRIBUTORS AND FUNDING SOURCES  \nThis work was supported by a thesis committee consisting of Professor Stephanie Paal of the Department of Civil Engineering and Professors Maria Koliou of the Department of Civil Engineering and Raymundo Arróyave of the Department of Materials Science & Engineering. The data used to train machine learning models were provided by Hongrak Pak. All other work conducted for the thesis was completed by the student independently.  \nTABLE OF CONTENTS  \nPage  \nABSTRACT ....................................................................................................................................................................................................... ii  \nCONTRIBUTORS AND FUNDING SOURCES .................................................................................................................... iii  \nTABLE OF CONTENTS ......................................................................................................................................................................... iv  \nLIST OF [FIGURES........................................................................................................................................................................................vi](FIGURES........................................................................................................................................................................................vi)  \n[LIST OF TABLES...............................](LIST OF TABLES...............................)........................................................................................................................................................ viii  \nCHAPTER I: INTRODUCTION ........................................................................................................................................................ 1  \nCHAPTER II: OVERVIEW OF RELATED WORK ................................................................................................................. 5  \nExperimental Programs: Shear Failure, Explained .............................................................................................................. 6  \nMechanistic Models....................................................................................................................................................................","cbCaicNsTMighMgo","https://ap.wps.com/l/cbCaicNsTMighMgo","pdf",2680046,1,158,"English","en",105,"# ABSTRACT\n# CONTRIBUTORS AND FUNDING SOURCES\n# TABLE OF CONTENTS\n# LIST OF FIGURES\n# LIST OF TABLES\n# CHAPTER I: INTRODUCTION\n# CHAPTER II: OVERVIEW OF RELATED WORK\n## Experimental Programs: Shear Failure, Explained\n## Mechanistic Models\n## Machine Learning Models\n# CHAPTER III: DESCRIPTION OF THE PROBLEM\n# CHAPTER IV: METHODOLOGY\n# CHAPTER V: INVESTIGATION OF HYBRID MODELING METHODS\n## Mechanics as a means of structure\n## Mechanics as a means of data\n## Parameter-Free Data-Driven Modeling\n## Discussion\n## Hybrid Learning\n## Application to Shear Failure\n# CHAPTER VI: RECREATING MECHANISTIC SHEAR MODELS\n## Shear Crack Propagation Theory","[{\"question\":\"What is the main goal of the thesis?\",\"answer\":\"To predict load-induced cracking in concrete beams without transverse reinforcement using hybrid mechanistic-machine learning models.\"},{\"question\":\"Why are hybrid models needed instead of purely mechanistic or purely machine learning models?\",\"answer\":\"Mechanistic models lack flexibility for load-induced cracking, while machine learning models struggle because available data sets are insufficient to learn the cracking relationships reliably.\"},{\"question\":\"How does the thesis support mechanistically consistent predictions?\",\"answer\":\"It develops Hybrid Learning theory, identifies optimal model combinations, and introduces a framework with low mechanistic bias and adequate constraint to ensure consistency.\"}]","LOAD-INDUCED CRACK PREDICTION USING HYBRID MECHANISTIC MACHINE LEARNING MODELS - 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