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- Qureshi, Hisham JahangirDepartment of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi Arabia (författare)
Prediction of compressive strength of two-stage (preplaced aggregate) concrete using gene expression programming and random forest
- Artikel/kapitelEngelska2023
Förlag, utgivningsår, omfång ...
- Elsevier Ltd,2023
- electronicrdacarrier
Nummerbeteckningar
- LIBRIS-ID:oai:DiVA.org:ltu-101963
- https://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-101963URI
- https://doi.org/10.1016/j.cscm.2023.e02581DOI
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- Språk:engelska
- Sammanfattning på:engelska
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Anmärkningar
- Validerad;2023;Nivå 2;2023-11-14 (marisr);Funder: Najran University (NU/RG/SERC/12/10); King Faisal University (4536);License full text: CC BY
- The aim of this research is to predict preplaced-aggregate concrete (PAC) compressive strength (CS) by using machine learning approaches such as gene expression programming (GEP) and random forest (RF). PAC requires injecting a portland cement-sand grout with admixtures into a mold after coarse aggregate has been deposited, making CS prediction complicated and requiring substantial study. Machine learning methods were used to cut down on the time and money needed for extensive experimental testing. The database includes 135 values for CS with eleven input variables. There is an acceptable degree of agreement between predicted and experimental values, as shown by the CS R2 values of 0.94 for GEP and 0.96 for RF. When comparing RF with GEP, RF performed better as measured by R2. The lower values displayed by the statistical error also showed that RF performed better than GEP. To compare, the GEP model's COV, MAE, RSME, and RMSLE were 0.527, 1.569, 2.706, and 0.133, whereas those for RF were 0.450, 1.648, 2.17, and 0.092. The SHAP analysis showed the effects of each input parameter, illuminating the positive effect of increasing the superplasticizer content on strength and the negative effect of raising the water-to-binder ratio. Using machine learning approaches to forecast the CS of PAC, this study has the potential to boost environmental protection and economic advantage.
Ämnesord och genrebeteckningar
- NATURVETENSKAP Data- och informationsvetenskap Datavetenskap hsv//swe
- NATURAL SCIENCES Computer and Information Sciences Computer Sciences hsv//eng
- Compressive strength
- Environment
- Machine learning
- Preplaced-aggregate concrete
- Shap analysis
- Sustainability
- Two-stage concrete
- Byggmaterial
- Building Materials
Biuppslag (personer, institutioner, konferenser, titlar ...)
- Alyami, ManaDepartment of Civil Engineering, College of Engineering, Najran University, Najran, Saudi Arabia (författare)
- Nawaz, R.Center for Applied Mathematics and Bioinformatics (CAMB), Gulf University for Science and Technology, 32093 Hawally, Kuwait (författare)
- Hakeem, Ibrahim Y.Department of Civil Engineering, College of Engineering, Najran University, Najran, Saudi Arabia (författare)
- Aslam, FahidDepartment of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia (författare)
- Iftikhar, BawarDepartment of Civil Engineering, COMSATS University Islamabad, Abbottabad Campus 22060, Pakistan (författare)
- Gamil, YaserLuleå tekniska universitet,Byggkonstruktion och brand,Department of Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500 Bandar Sunway, Selangor, Malaysia(Swepub:ltu)yasgam (författare)
- Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi ArabiaDepartment of Civil Engineering, College of Engineering, Najran University, Najran, Saudi Arabia (creator_code:org_t)
Sammanhörande titlar
- Ingår i:Case Studies in Construction Materials: Elsevier Ltd192214-5095
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