Balancing Lightweight Design and Structural Integrity in Tall Composite Masts: A Data-Driven Study on ANN-Genetic Algorithm Optimization, Stress Concentration Mitigation, and Performance Validation for Naval Applications

Balancing Lightweight Design and Structural Integrity in Tall Composite Masts: A Data-Driven Study on ANN-Genetic Algorithm Optimization, Stress Concentration Mitigation, and Performance Validation for Naval Applications | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 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Data may be preliminary. 28 April 2025 V1 Latest version Share on Balancing Lightweight Design and Structural Integrity in Tall Composite Masts: A Data-Driven Study on ANN-Genetic Algorithm Optimization, Stress Concentration Mitigation, and Performance Validation for Naval Applications Authors : Mohammadreza Hadavi [email protected] , Karim Akbarivakilabadi , Saeid Nikabadi , and Mahdi Ghasri 0000-0002-5086-2878 Authors Info & Affiliations https://doi.org/10.22541/au.174585814.47011228/v1 315 views 166 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The pursuit of lightweight, structurally sound, and cost-effective designs is paramount in contemporary engineering, particularly in the development of advanced enclosed mast/sensor systems (AEM/S). A significant challenge in this field is the limited availability of comprehensive data and systematic classification of studies pertaining to these specific mast configurations. This research addresses this gap by initially developing a model of a steel and anti-radar composite mast, subsequently employing Abaqus/CFD and Abaqus/FEM to conduct aerodynamic and finite element analyses, respectively. This initial phase aims to characterize the structural behavior of this mast type. In the second stage, a neural network-based method, coupled with a genetic optimization algorithm, is implemented to determine the optimal dimensions for the mast and its associated casing. This optimization process culminated in a composite mast design, standing 22 meters tall with a 9.59-degree slope, achieving a substantial 50% weight reduction compared to the original steel mast design (reducing the mass from 118 to 27.51 tons). Finite element analysis (FEA) was utilized to assess the mechanical behavior of the initial and optimized designs. The results demonstrate that the optimized design exhibits a more evenly distributed stress profile, with reduced stress concentrations in the lower sections and increased stress levels in the upper antenna region. This stress redistribution suggests improved material utilization and enhanced structural integrity. By minimizing the potential for localized failure, the optimized design demonstrates the feasibility of achieving significant weight reductions without compromising structural performance. These findings underscore the effectiveness of artificial neural network (ANN)-based optimization in creating lightweight and efficient composite structures, providing valuable insights for designing AEM/S systems and other tall structures subjected to dynamic loads. The study highlights the potential of advanced optimization techniques and composite materials in achieving sustainable and high-performance engineering solutions. Supplementary Material File (manuscript-4-4-2025.docx) Download 65.86 MB Information & Authors Information Version history V1 Version 1 28 April 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Collection Engineering Reports Keywords artificial neural network enclosed mast finite element analysis mechanical behavior Authors Affiliations Mohammadreza Hadavi [email protected] Imam Khomeini Naval University of Noshahr View all articles by this author Karim Akbarivakilabadi Imam Khomeini Naval University of Noshahr View all articles by this author Saeid Nikabadi Imam Khomeini Naval University of Noshahr View all articles by this author Mahdi Ghasri 0000-0002-5086-2878 Imam Khomeini Naval University of Noshahr View all articles by this author Metrics & Citations Metrics Article Usage 315 views 166 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Mohammadreza Hadavi, Karim Akbarivakilabadi, Saeid Nikabadi, et al. Balancing Lightweight Design and Structural Integrity in Tall Composite Masts: A Data-Driven Study on ANN-Genetic Algorithm Optimization, Stress Concentration Mitigation, and Performance Validation for Naval Applications. Authorea . 28 April 2025. DOI: https://doi.org/10.22541/au.174585814.47011228/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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