Performance of Artificial Neural Network and Physics-Informed Neural Networks for Flexural Strength Using Sugarcane Bagasse Ash and Distilled Sewage Water 

Performance of Artificial Neural Network and Physics-Informed Neural Networks for Flexural Strength Using Sugarcane Bagasse Ash and Distilled Sewage Water | 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 Performance of Artificial Neural Network and Physics-Informed Neural Networks for Flexural Strength Using Sugarcane Bagasse Ash and Distilled Sewage Water Mayuri Ahirrao, Rakesh Patel, Chaitanya Mishra This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9189154/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 An increasing number of eco-conscious construction projects are looking to agricultural waste products, such as sugarcane bagasse ash (SCBA) and Distilled Sewage Water, to partially replace cement and Pure Water in their mixes. This study evaluates the predictive power of Artificial Neural Networks (ANNs) and Physics-Informed Neural Networks (PINNs) by examining the increase in flexural strength of SCBA-based concrete at 7, 14, and 28 days. The results of the experiment showed a steady increase in flexural strength with curing age, indicating constant hydration and good matrix densification. The nonlinear correlation between mix design factors and strength was anticipated using ANN and PINN methods and tested and validated through training, cross-validation (OOF), testing and full-dataset appraisals. The ANN demonstrated relatively high training accuracy, but it showed evidence of overtraining and poor generalisation, as indicated by negative R2 values on the validation and test datasets. Conversely, the PINN, where constraints are included in the loss function based on the physics, demonstrated a relatively higher stability and reduced error values (MSE, MAE, RMSE) on every data split. Reduced variance dispersion in PINN was demonstrated using residual analysis, and SHAP-based interpretability showed that cement content, percentage of SCBA and ratio of water-binder were the strongest predictors of flexural strength. Despite the fact that both models still need additional improvement to be more generalisable, the findings indicate that physics-informed learning can improve the robustness and interpretability of strength prediction. The suggested integrated experimental-PINN system presents a potential solution to the sustainable concrete performance modelling and the intelligent mix design optimisation. Sugarcane Bagasse Ash (SCBA) Sustainable Concrete Flexural Strength Artificial Neural Network (ANN) Physics-Informed Neural Network (PINN) Machine Learning SHAP Analysis Strength Prediction Eco-Efficient Materials Explainable Artificial Intelligence (XAI) 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-9189154","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":612215556,"identity":"4e5dac44-9c6b-4c3f-a0cf-4363eefaef56","order_by":0,"name":"Mayuri Ahirrao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYFCCBDaGBBDN3pD4AEjx8BGvhefAYwMQxUaUFjCQSHwmAaIJatFtT3724OGee/L8DMlplV9z7GTYGJgfPrqBR4vZmWfmBgnPig1nNhxLuy27LRnoMDZj4xx8Wm4kmEkkHEhg3HCwJ+225DZmoBYeNmn8WtK/gbTYbzjM/61Ycls9MVpywLYkbjjGkMb4cdthIrSceVMG0pI8s4chWZpx23EeNmZCfjmevk3yx4EE2375B4kff26rtudnb374GJ8WFMDMAyaJVQ4CjD9IUT0KRsEoGAUjBgAAuaFLrm82hQMAAAAASUVORK5CYII=","orcid":"","institution":"Oriental University, India","correspondingAuthor":true,"prefix":"","firstName":"Mayuri","middleName":"","lastName":"Ahirrao","suffix":""},{"id":612215557,"identity":"248a9eb4-d63c-42ce-9768-b0a22f19f4c8","order_by":1,"name":"Rakesh Patel","email":"","orcid":"","institution":"Oriental University, India","correspondingAuthor":false,"prefix":"","firstName":"Rakesh","middleName":"","lastName":"Patel","suffix":""},{"id":612215558,"identity":"f136a2de-cf6c-4f25-bd25-31d59ffa98b3","order_by":2,"name":"Chaitanya Mishra","email":"","orcid":"","institution":"Oriental University, India","correspondingAuthor":false,"prefix":"","firstName":"Chaitanya","middleName":"","lastName":"Mishra","suffix":""}],"badges":[],"createdAt":"2026-03-22 05:23:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9189154/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9189154/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106205794,"identity":"ccd8008c-a510-4451-907a-ae2a03e025b2","added_by":"auto","created_at":"2026-04-06 05:25:38","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1149030,"visible":true,"origin":"","legend":"","description":"","filename":"ResearchPaper25022026.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9189154/v1_covered_73bfe6c4-6301-418e-a8f1-b3408ab008ae.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Performance of Artificial Neural Network and Physics-Informed Neural Networks for Flexural Strength Using Sugarcane Bagasse Ash and Distilled Sewage Water ","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Sugarcane Bagasse Ash (SCBA), Sustainable Concrete, Flexural Strength, Artificial Neural Network (ANN), Physics-Informed Neural Network (PINN), Machine Learning, SHAP Analysis, Strength Prediction, Eco-Efficient Materials, Explainable Artificial Intelligence (XAI)","lastPublishedDoi":"10.21203/rs.3.rs-9189154/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9189154/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAn increasing number of eco-conscious construction projects are looking to agricultural waste products, such as sugarcane bagasse ash (SCBA) and Distilled Sewage Water, to partially replace cement and Pure Water in their mixes. This study evaluates the predictive power of Artificial Neural Networks (ANNs) and Physics-Informed Neural Networks (PINNs) by examining the increase in flexural strength of SCBA-based concrete at 7, 14, and 28 days. The results of the experiment showed a steady increase in flexural strength with curing age, indicating constant hydration and good matrix densification. The nonlinear correlation between mix design factors and strength was anticipated using ANN and PINN methods and tested and validated through training, cross-validation (OOF), testing and full-dataset appraisals. The ANN demonstrated relatively high training accuracy, but it showed evidence of overtraining and poor generalisation, as indicated by negative R2 values on the validation and test datasets. Conversely, the PINN, where constraints are included in the loss function based on the physics, demonstrated a relatively higher stability and reduced error values (MSE, MAE, RMSE) on every data split. Reduced variance dispersion in PINN was demonstrated using residual analysis, and SHAP-based interpretability showed that cement content, percentage of SCBA and ratio of water-binder were the strongest predictors of flexural strength. Despite the fact that both models still need additional improvement to be more generalisable, the findings indicate that physics-informed learning can improve the robustness and interpretability of strength prediction. The suggested integrated experimental-PINN system presents a potential solution to the sustainable concrete performance modelling and the intelligent mix design optimisation.\u003c/p\u003e","manuscriptTitle":"Performance of Artificial Neural Network and Physics-Informed Neural Networks for Flexural Strength Using Sugarcane Bagasse Ash and Distilled Sewage Water ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-26 04:50:55","doi":"10.21203/rs.3.rs-9189154/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2ac51345-ea32-4e1b-b722-08ad0a9a1f68","owner":[],"postedDate":"March 26th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-06T05:25:07+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-26 04:50:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9189154","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9189154","identity":"rs-9189154","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}