Truss structure optimization via hierarchical tree search

Truss structure optimization via hierarchical tree search | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Truss structure optimization via hierarchical tree search Arvan Sedighzadeh, Matteo Torzoni, Alberto Corigliano This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8406478/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Truss design is a highly constrained problem due to mechanical requirements and practical limitations related to fabrication and assembly. This study formulates truss design synthesis as a discrete Markov decision process, in which grammar-constrained actions generate feasible intermediate layouts and Monte Carlo Tree Search (MCTS) learns an optimal design policy. Previous work has shown that MCTS outperforms both metaheuristic methods, such as genetic algorithms, and alternative reinforcement learning approaches, including Q-learning and deep Q-learning. However, its computational scalability is limited by the rapid growth of admissible configurations in dense grid design domains. To address these limitations, we propose a Hierarchical MCTS (H-MCTS) framework in which staged grid refinements focus computational resources on promising regions of the domain, thereby alleviating the curse of dimensionality. Benchmark evaluations show that H-MCTS consistently improves design quality and reduces computational cost compared to single-stage MCTS. To accommodate variable design conditions, H-MCTS is further applied to on-the-fly structural adaptivity through an offline–online strategy that precomputes optimal solutions and interpolates them in real time. The effectiveness of the computational procedure is demonstrated on a bridge-like truss structure that is progressively constructed and then morphed to accommodate moving loads and localized damage. Truss optimization Hierarchical Monte Carlo tree search Reinforcement learning Structural adaptivity Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 08 Mar, 2026 Reviews received at journal 06 Mar, 2026 Reviewers agreed at journal 06 Mar, 2026 Reviewers agreed at journal 04 Mar, 2026 Reviewers agreed at journal 02 Jan, 2026 Reviewers invited by journal 29 Dec, 2025 Editor assigned by journal 26 Dec, 2025 Submission checks completed at journal 24 Dec, 2025 First submitted to journal 19 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8406478","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":566795829,"identity":"95b1cf2c-52c7-4de7-a757-a54152f75a7a","order_by":0,"name":"Arvan Sedighzadeh","email":"","orcid":"","institution":"Politecnico di Milano","correspondingAuthor":false,"prefix":"","firstName":"Arvan","middleName":"","lastName":"Sedighzadeh","suffix":""},{"id":566795831,"identity":"bab6bd3d-d379-40f5-9dac-0f0c7fdf0202","order_by":1,"name":"Matteo Torzoni","email":"","orcid":"","institution":"Politecnico di 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This study formulates truss design synthesis as a discrete Markov decision process, in which grammar-constrained actions generate feasible intermediate layouts and Monte Carlo Tree Search (MCTS) learns an optimal design policy. Previous work has shown that MCTS outperforms both metaheuristic methods, such as genetic algorithms, and alternative reinforcement learning approaches, including Q-learning and deep Q-learning. However, its computational scalability is limited by the rapid growth of admissible configurations in dense grid design domains. To address these limitations, we propose a Hierarchical MCTS (H-MCTS) framework in which staged grid refinements focus computational resources on promising regions of the domain, thereby alleviating the curse of dimensionality. Benchmark evaluations show that H-MCTS consistently improves design quality and reduces computational cost compared to single-stage MCTS. 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