Strength-Driven Inverse Modelling of Agricultural waste Concrete Using Feasibility-Guided Generative Intelligence Operations

Strength-Driven Inverse Modelling of Agricultural waste Concrete Using Feasibility-Guided Generative Intelligence Operations | 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 Strength-Driven Inverse Modelling of Agricultural waste Concrete Using Feasibility-Guided Generative Intelligence Operations VARSHA D. KAPGATE, Samyak D. Parekar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8548423/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Rice husk ash and corn cob derivatives are popular in sustainable concrete systems, but the lack of dependable, performance-driven mix design procedures limits their use in structural applications. Most existing processes adjust mix proportions to desired strengths using forward prediction models or laborious trial-and-error methods. Such methods struggle with the inverse nature of real engineering issues, where compressive, tensile, and flexural strength needs are set and mix composition must obey material, durability, and sustainability constraints. The diversity of agricultural waste materials and the ill-posedness of this inverse problem make present design solutions unreliable. This study introduced a strength-driven inverse modeling method for agricultural waste concrete that handles mix design as a limited inverse job. A pipeline with five tightly coupled analytical modules is proposed. A Strength-Conditioned Mix Manifold Learning (SC-MML) module learns a continuous, constraint-aware latent representation of viable concrete mixes conditioned on goal mechanical attributes to assure initial physical plausibility. A Pareto Inverse Solver with Feasibility-Gated Diffusion (PIS-FGD) generates many candidates mix designs that meet strength, workability, and embodied carbon criteria from this manifold. A Microstructure-Consistent Forward Twin with Material Tokens (MiC-FTMT) assesses candidate mixes using rice husk ash and corn cob property descriptors to ensure hydration and porosity consistency and increase generalization across agricultural waste sources. To reduce shortcut learning and cement reliance, the Causal Robustness and Counterfactual Mix Auditing (CR-CMA) stage stress-tests mixes under controlled interventions. Finally, CL-ALV-BER refines inverse and forward models utilizing residuals from experimental input sets. The framework maintains structural performance with high objective fidelity, minimum design iterations, and cement substitutions. Accurate inverse mix designs and a systematic methodology for performance-centric, low-carbon concrete engineering enable confident use of agricultural waste materials in structural-grade applications and advance civil engineering inverse materials designs. Inverse Mix Design Agricultural waste Concrete Strength-Constrained Optimization Generative Modelling Sustainable Construction Materials Scenarios Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8548423","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":572236004,"identity":"2bf5950d-4db2-49d8-a65c-d14334e0b461","order_by":0,"name":"VARSHA D. 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