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SDS variations are evaluated by changing the geometric parameters of bottom elements, focusing on static stress and displacement while keeping fixed dimensions, thickness, and material. Static stress dependencies are derived for channel wall and bottom sheet thickness, supported by generated safety margin plots. RandomForest machine learning in Python predicts static stress from multiple factors and is validated against test-sample measurements.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/innovative-stress-analysis-and-machine-learning-forecasting-for-semi-trailer-truck-body-durability/291205/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/innovative-stress-analysis-and-machine-learning-forecasting-for-semi-trailer-truck-body-durability/291205.png","ImageObject",300,407,{"name":92,"@type":93},"Valentina","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-18","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":29},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the article address?","Question",{"text":112,"@type":113},"It analyzes the stress-deformation state of the bottom structure of a semi-trailer truck body and predicts static stress changes to support efficient element sizing.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How is the engineering analysis performed?",{"text":117,"@type":113},"The study uses SolidWorks with a CAD model of the semi-trailer truck body and evaluates how SDS parameters vary when geometric parameters of bottom elements change.",{"name":119,"@type":110,"acceptedAnswer":120},"How does machine learning contribute to the study?",{"text":121,"@type":113},"A RandomForest model in Python predicts static stress based on multiple factors, and its predictions are validated by comparing predicted values with actual values from a test sample.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},291205,1790557987,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":29,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145,"preview_page_count":4},13056703020460,"https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923","Innovative stress analysis and machine learning forecasting for semi-trailer truck body durability  \nOleh Lyashuk *, Mykhailo Levkovych *, Mykola Stashkiv *,  \nOleh Pastukh **, Volodymyr Martyniuk ***, Dmytro Mironov *,  \nMarcin Rabe ****, YuriyVovk *  \n* Department of Automobiles, Ternopil Ivan Puluj National Technical University, 56, Ruska Str., 46001 Ternopil, Ukraine  \n[oleglashuk@ukr.net](oleglashuk@ukr.net); [levkovmin@gmail.com](levkovmin@gmail.com); [stam77@ukr.net](stam77@ukr.net); [vovkyuriy@ukr.net](vovkyuriy@ukr.net)  \n** Department of Software Engineering, Ternopil Ivan Puluj National Technical University, 56, Ruska Str., 46001 Ternopil, Ukraine  \n[oleg.pastuh@gmail.com](oleg.pastuh@gmail.com)  \n*** Department of Administrative and Financial Management, Lviv Polytechnic National University, Stepana Bandera Str., 12, 79013 Lviv, Ukraine;  \nWSEI University, ul. Projektowa 4, 20-209 Lublin, Poland  \n[wlodzimierz.martyniuk@wsei.lublin.pl](wlodzimierz.martyniuk@wsei.lublin.pl)  \n**** Institute of Management, University of Szczecin, al. PapieżaJana Pawła II 22a, 70 – 453 Szczecin, Poland  \n[marcin.rabe@usz.edu.pl](marcin.rabe@usz.edu.pl)  \nArticle history:  \nReceived: March 14, 2023  \n1st Revision: September 08, 2023  \nAccepted: October 28, 2023  \nDOI:  \n10.14254/jsdtl.2023.8-2.3  \nAbstract: This article presents an in-depth analysis of the stress-deformation state (SDS) in the bottom structure of a semi-trailer truck body. Engineering analysis was conducted utilizing the SolidWorks software, focusing on a comprehensive CAD model of the semi-trailer truck body. The study explored variations in SDS parameters resulting from alterations in the geometric parameters of the body bottom elements. The research investigated alterations in static stress and displacement relative to changes in the proportions of the cross-section of the channel while maintaining fixed geometric dimensions of the workpiece, thickness of the workpiece, and the material of the body bottom. Graphical representations were generated to illustrate the variations in static stress, displacement, and safety margin concerning the thickness of the shelf and channel. Additionally, dependencies were derived that correlate static stresses in the channel with the thickness of the channel wall and the thickness of the body bottom sheet. The study results were compiled and summarized, offering valuable insights into the stress-deformation state of the semi-trailer truck body's bottom. Furthermore, machine learning techniques, specifically the RandomForest algorithm, were  \nimplemented in a Python environment to predict changes in  \nstatic stress based on various factors. The model's predictions were validated by comparing predicted static stress values with actual values on a test sample. These findings facilitate efficient  \nselection of appropriately sized elements by predicting static stress values, employing the RandomForest machine learning algorithm.  \nKeywords: transport, energy resources, semi-trailer truck body, static stress, static displacement, CAD model, algorithm, machine learning  \n1. Introduction  \nTrucks are widely used to transport all types of goods both within the country and internationally. Numerous methods of their classification make it possible to highlight the main parameters of vehicles, thanks to which the best option for the safe transportation of goods by road is selected (Ukraine State Statistic Service, n.d.) .  \nTrucks differ in a large number of parameters and design features. Types of cars and truck bodies are a primary factor in their classification. Truck body types also determine the consumer purpose of each type of truck. The types of truck bodies are one of the decisive factors in favor of a specific type of vehicle for planning the process of cargo delivery by vehicle.  \nIn the automobile industry, truck chassis are designed according to design standards, but bodies made by various manufacturers are designed without referen","cbCair1FTRu3EU3s","https://ap.wps.com/l/cbCair1FTRu3EU3s","pdf",1835221,15,"English","# Introduction\n## Stress-deformation and vibration/resonance background\n## Truck body design considerations and materials\n## Purpose and classification of truck bodies","[{\"question\":\"What problem does the article address?\",\"answer\":\"It analyzes the stress-deformation state of the bottom structure of a semi-trailer truck body and predicts static stress changes to support efficient element sizing.\"},{\"question\":\"How is the engineering analysis performed?\",\"answer\":\"The study uses SolidWorks with a CAD model of the semi-trailer truck body and evaluates how SDS parameters vary when geometric parameters of bottom elements change.\"},{\"question\":\"How does machine learning contribute to the study?\",\"answer\":\"A RandomForest model in Python predicts static stress based on multiple factors, and its predictions are validated by comparing predicted values with actual values from a test sample.\"}]","Innovative stress analysis and machine learning forecasting for semi-trailer truck body durability | PDF",1789643735,38]