New random intelligent chemometric techniques for sustainable geopolymer concrete: Low-energy and carbon-footprint initiatives

New random intelligent chemometric techniques for sustainable geopolymer concrete: Low-energy and carbon-footprint initiatives | 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 New random intelligent chemometric techniques for sustainable geopolymer concrete: Low-energy and carbon-footprint initiatives Mahmud M. Jibril, Salim Malami, Hauwa Jibrin, umar jibrin, Mohammed Duhu, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3369502/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract The construction industry, being a significant contributor to greenhouse gas emissions, facing considerable attention and demand on account of the increasing global apprehension regarding climate change and its adverse impacts on environments. Geopolymer shows itself as a viable and sustainable alternative to the Portland cement binder in civil infrastructure applications, offering a low-energy, low-carbon footprint solution. This study evaluates five models: Random Forest (RF), Robust Linear Regression (RL), Recurrent Neural Network (RNN), Response Surface Methodology (RSM), and Regression Tree (RT). The RL and RT models were utilized in the prediction of GPC Compressive strength (CS), employing the Matlab R19a regression learner APP. The RNN model was implemented using the Matlab R19a toolkit. Furthermore, the RF model was developed using R studio version 4.2.2 programming code, and the RSM model was constructed using the Minitab 18 toolbox. EViews 12 software was utilized for both pre-processing and post-processing of the data. Additionally, it was employed to convert the non-stationary data into stationary data in order to obtain accurate results. The input variables included SiO 2 /Na 2 O (S/N), Na 2 O (N), Water/Binder Ratio (W/B), Curing Time (CT), Ultrasonic Pulse Velocity (UPV), and 28-day Compressive Strength (Mpa) (CS) as the target variable. The findings of the study indicate that the RMS-M3 model exhibited superior performance compared to all other models, demonstrating a high level of accuracy. Specifically, the Pearson correlation coefficient (PCC) was calculated to be 0.994, while the mean absolute percentage error (MAPE) was found to be 0.708 during the verification phase. Geopolymer Concrete Artificial intelligence Machine Learning Nitrogen Oxide Compressive strength Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major revision 22 Sep, 2023 Reviews received at journal 22 Sep, 2023 Reviewers agreed at journal 22 Sep, 2023 Reviewers agreed at journal 21 Sep, 2023 Reviewers invited by journal 21 Sep, 2023 Editor assigned by journal 21 Sep, 2023 Submission checks completed at journal 20 Sep, 2023 First submitted to journal 19 Sep, 2023 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-3369502","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":235087067,"identity":"10cef92f-52ad-43c4-bd19-a2ad6c90232f","order_by":0,"name":"Mahmud M. 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Geopolymer shows itself as a viable and sustainable alternative to the Portland cement binder in civil infrastructure applications, offering a low-energy, low-carbon footprint solution. This study evaluates five models: Random Forest (RF), Robust Linear Regression (RL), Recurrent Neural Network (RNN), Response Surface Methodology (RSM), and Regression Tree (RT). The RL and RT models were utilized in the prediction of GPC Compressive strength (CS), employing the Matlab R19a regression learner APP. The RNN model was implemented using the Matlab R19a toolkit. Furthermore, the RF model was developed using R studio version 4.2.2 programming code, and the RSM model was constructed using the Minitab 18 toolbox. EViews 12 software was utilized for both pre-processing and post-processing of the data. Additionally, it was employed to convert the non-stationary data into stationary data in order to obtain accurate results. The input variables included SiO\u003csub\u003e2\u003c/sub\u003e/Na\u003csub\u003e2\u003c/sub\u003eO (S/N), Na\u003csub\u003e2\u003c/sub\u003eO (N), Water/Binder Ratio (W/B), Curing Time (CT), Ultrasonic Pulse Velocity (UPV), and 28-day Compressive Strength (Mpa) (CS) as the target variable. The findings of the study indicate that the RMS-M3 model exhibited superior performance compared to all other models, demonstrating a high level of accuracy. 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