Prediction of compressive strength of two-stage (preplaced aggregate) concrete using gene expression programming and random forest
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 aggr...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Case Studies in Construction Materials |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214509523007611 |
| _version_ | 1797454578787024896 |
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| author | Hisham Jahangir Qureshi Mana Alyami R. Nawaz Ibrahim Y. Hakeem Fahid Aslam Bawar Iftikhar Yaser Gamil |
| author_facet | Hisham Jahangir Qureshi Mana Alyami R. Nawaz Ibrahim Y. Hakeem Fahid Aslam Bawar Iftikhar Yaser Gamil |
| author_sort | Hisham Jahangir Qureshi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T15:39:11Z |
| format | Article |
| id | doaj.art-a43aece682a54ca6af1232debae99151 |
| institution | Directory Open Access Journal |
| issn | 2214-5095 |
| language | English |
| last_indexed | 2024-03-09T15:39:11Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Construction Materials |
| spelling | doaj.art-a43aece682a54ca6af1232debae991512023-11-25T04:49:22ZengElsevierCase Studies in Construction Materials2214-50952023-12-0119e02581Prediction of compressive strength of two-stage (preplaced aggregate) concrete using gene expression programming and random forestHisham Jahangir Qureshi0Mana Alyami1R. Nawaz2Ibrahim Y. Hakeem3Fahid Aslam4Bawar Iftikhar5Yaser Gamil6Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi Arabia; Corresponding authors.Department of Civil Engineering, College of Engineering, Najran University, Najran, Saudi ArabiaCenter for Applied Mathematics and Bioinformatics (CAMB), Gulf University for Science and Technology, 32093 Hawally, KuwaitDepartment of Civil Engineering, College of Engineering, Najran University, Najran, Saudi ArabiaDepartment of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi ArabiaDepartment of Civil Engineering, COMSATS University Islamabad, Abbottabad Campus, 22060, Pakistan; Corresponding authors.Department of Civil, Environmental and Natural Resources Engineering, Luleå University of Technology, Sweden; Department of Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500 Bandar Sunway, Selangor, Malaysia; Corresponding author at: Department of Civil, Environmental and Natural Resources Engineering, Luleå University of Technology, Sweden.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.http://www.sciencedirect.com/science/article/pii/S2214509523007611Preplaced-aggregate concreteTwo-stage concreteCompressive strengthMachine learningSustainabilityEnvironment |
| spellingShingle | Hisham Jahangir Qureshi Mana Alyami R. Nawaz Ibrahim Y. Hakeem Fahid Aslam Bawar Iftikhar Yaser Gamil Prediction of compressive strength of two-stage (preplaced aggregate) concrete using gene expression programming and random forest Case Studies in Construction Materials Preplaced-aggregate concrete Two-stage concrete Compressive strength Machine learning Sustainability Environment |
| title | Prediction of compressive strength of two-stage (preplaced aggregate) concrete using gene expression programming and random forest |
| title_full | Prediction of compressive strength of two-stage (preplaced aggregate) concrete using gene expression programming and random forest |
| title_fullStr | Prediction of compressive strength of two-stage (preplaced aggregate) concrete using gene expression programming and random forest |
| title_full_unstemmed | Prediction of compressive strength of two-stage (preplaced aggregate) concrete using gene expression programming and random forest |
| title_short | Prediction of compressive strength of two-stage (preplaced aggregate) concrete using gene expression programming and random forest |
| title_sort | prediction of compressive strength of two stage preplaced aggregate concrete using gene expression programming and random forest |
| topic | Preplaced-aggregate concrete Two-stage concrete Compressive strength Machine learning Sustainability Environment |
| url | http://www.sciencedirect.com/science/article/pii/S2214509523007611 |
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