[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124135-en":3,"doc-seo-124135-105":30,"detail-sidebar-cat-0-en-105":83},{"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},124135,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine Learning Applications in Structural Engineering - Prediction Models for Moment-Rotation Characteristics in Boltless Steel Connections","This study applies machine learning to predict the moment-rotation behavior of boltless steel connections widely used in pallet rack structures. Using extensive experimental data, Support Vector Machines (SVM) and Deep Learning (DL) models are built as predictive tools for connection performance. Results show the models support accurate forecasting of key structural characteristics, enabling design optimization and improved safety and functionality of pallet rack systems. The generalized framework provides a basis for future design improvements, with SVM delivering the highest predictive accuracy among tested models.","Machine Learning Applications in Structural Engineering: Prediction Models for Moment-Rotation Characteristics in Boltless Steel Connections  \nReventheran Ganasan1,2,3*, Muhamad Faiz Abd Latif 1,3, Mohd Eizzuddin Mahyeddin1,3, Karthigesu Nagarajoo1,3, Md . Akter Hosen4, Ramesh Nayaka5  \n1 Department of Transportation Engineering Technology, Faculty of Engineering Technology, Universiti Tun Hussein Onn Malaysia, Pagoh, Johor, 84600, MALAYSIA  \n2 Sustainable Engineering Technology Research Centre (SETechRC), Faculty of Engineering Technology, Universiti Tun Hussein Onn Malaysia, Pagoh, Johor, 84600, MALAYSIA  \n3 Industry Centre of Excellence for Railway (ICOE-REL),  \nUniversiti Tun Hussien Onn Malaysia, Parit Raja, Batu Pahat, Johor, 86400, MALAYSIA  \n4 Department of Civil and Environmental Engineering,  \nDhofar University, Salalah, 211, OMAN  \n5 Department of Civil and Infrastructure Engineering  \nIndian Institute of Technology (IIT) Dharwad, Karnataka, 580011, INDIA  \n*Corresponding Author: [reven@uthm.edu.my](reven@uthm.edu.my)  \nDOI: [https://doi.org/10.30880/jaita.2024.05.02.008](https://doi.org/10.30880/jaita.2024.05.02.008)  \n\n| Article Info | Abstract |\n| --- | --- |\n| Received: 15 May 2024\u003Cbr>Accepted: 22 November 2024\u003Cbr>Available online: 8 December 2024 | This article presents a study on the application of machine learning for predicting the moment-rotation behavior of boltless steel connections commonly used in pallet rack structures. Through extensive experimental data collection, Support Vector Machines (SVM) and Deep |\n| Keywords | Learning (DL) models were developed to serve as predictive tools for these connections. The analysis demonstrates that these models enable |\n| Machine learning, boltless steel connections, deep learning, support vector machine, steel pallet rack | engineers to accurately forecast structural characteristics, optimize boltless connection designs, and enhance the stability and functionality of pallet rack systems. The generalized model framework established here offers a robust foundation for future studies and design improvements, with SVM achieving the highest predictive accuracy among the models tested. |\n\n1. Introduction  \nSteel pallet racks (as shown in Figure 1) are indispensable components within warehouse and distribution centers, forming a critical foundation for modular storage solutions. Their effectiveness and operational safety are underpinned by their structural integrity and resilience, particularly in regions prone to seismic activity, where they must withstand dynamic forces and absorb impacts during seismic events [1-2] . Traditional structural analysis methods, however, often fall short in accurately capturing the complex interplay of forces, especially within boltless connections, where interaction forces between bolts, beams, and braces are difficult to elastically model [3-4]. This paper introduces an advanced analytical approach for assessing connection reliability based on moment-rotation relationships. This method leverages machine learning (ML) techniques to develop precise predictive models for these connections, offering a robust alternative to traditional approaches. By integrating ML into this design space, the study aims to quantify and explain the variability in materials, load distribution patterns, and seismic performance factors not fully addressed in conventional design models.  \nThe integration of machine learning into structural engineering has grown increasingly valuable in recent years. ML's sophisticated computational and data-processing capabilities allow engineers to analyze extensive datasets and uncover latent structural patterns that would be difficult to capture through conventional methods. This capability becomes crucial for developing enhanced models for boltless connections, which exhibit unique behaviors under various loading conditions and seismic forces. Notably, prior research has highlighted the need for improved design tools to captu","cbCaiuqxefo1wkc4","https://ap.wps.com/l/cbCaiuqxefo1wkc4","pdf",686181,1,10,"English","en",105,"# Introduction\n## Structural role of steel pallet racks\n## Limitations of traditional analysis for boltless connections\n## Goal and contribution of ML-driven predictive framework\n# Literature Review\n## Seismic and structural requirements of pallet rack systems","[{\"question\":\"How do the proposed models support engineering decisions?\",\"answer\":\"The models help engineers forecast structural characteristics, optimize boltless connection designs, and improve the stability and functionality of steel pallet rack systems, including under seismic-related loading scenarios.\"}]","Machine Learning Applications in Structural Engineering - Prediction Models for Moment-Rotation Characteristics in Boltless Steel Connections | PDF",1785820639,25,{"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":78,"head_meta":80,"extra_data":82,"updated_unix":28},"machine-learning-applications-in-structural-engineering-prediction-models-for-moment-rotation-characteristics-in-boltless-steel-connections","",{"@graph":36,"@context":77},[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-applications-in-structural-engineering-prediction-models-for-moment-rotation-characteristics-in-boltless-steel-connections/124135/",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],{"name":72,"@type":73,"acceptedAnswer":74},"How do the proposed models support engineering decisions?","Question",{"text":75,"@type":76},"The models help engineers forecast structural characteristics, optimize boltless connection designs, and improve the stability and functionality of steel pallet rack systems, including under seismic-related loading scenarios.","Answer","https://schema.org",{"og:url":52,"og:type":79,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":81,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":84},[85,89,93,97,102,107,112,115,120,123,126],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":86,"show_sort_weight":87,"slug":88},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":90,"show_sort_weight":91,"slug":92},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Exam",70,"exam",{"id":98,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},5,"Comic",60,"comic",{"id":103,"doc_module":4,"doc_module_name":46,"category_name":104,"show_sort_weight":105,"slug":106},6,"Technology",50,"technology",{"id":108,"doc_module":4,"doc_module_name":46,"category_name":109,"show_sort_weight":110,"slug":111},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":113,"slug":114},30,"research-report",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":21,"slug":125},"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":98,"slug":129},19,"General","general"]