Machine learning for predicting strength properties of waste iron slag concrete
This study investigates the utilization of waste iron slag (WIS) as a sustainable alternative in concrete production to reduce environmental impact and preserve natural resources. The experimental investigation of WIS-incorporated concrete focused on compressive and tensile strength with machine lea...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844025005134 |
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| author | Matiur Rahman Raju Syed Ishtiaq Ahmad Md Mehedi Hasan Noor Md. Sadiqul Hasan Md Monirul Islam Md. Abdul Basit Ishraq Tasnim Hossain Saif Ahmed Santo Md Shahrior Alam Mahfuzur Rahman |
| author_facet | Matiur Rahman Raju Syed Ishtiaq Ahmad Md Mehedi Hasan Noor Md. Sadiqul Hasan Md Monirul Islam Md. Abdul Basit Ishraq Tasnim Hossain Saif Ahmed Santo Md Shahrior Alam Mahfuzur Rahman |
| author_sort | Matiur Rahman Raju |
| collection | DOAJ |
| description | This study investigates the utilization of waste iron slag (WIS) as a sustainable alternative in concrete production to reduce environmental impact and preserve natural resources. The experimental investigation of WIS-incorporated concrete focused on compressive and tensile strength with machine learning (ML) models for prediction. Among the tested ML algorithms, Decision Tree (DT) and XGBoost showed the highest accuracy (R2 = 0.95135) in predicting concrete strength properties, while models like SVM and Symbolic Regression underperformed. Experimental results indicate that up to 20 % WIS replacement maintains adequate strength, whereas higher proportions reduce structural integrity. A ranking score index and cost analysis confirmed the technical and economic feasibility of using WIS in concrete. Cost analysis demonstrated substantial cost savings with 25 % WIS incorporation, confirming its economic feasibility. Integrating experimental data with ML predictions highlights WIS's potential for sustainable concrete applications, enabling optimized mix designs and reduced reliance on physical testing. Future work should address limitations, including dataset expansion and the exploration of additional durability and mechanical properties to validate WIS's practicality in construction further. |
| format | Article |
| id | doaj-art-cefc57a615e74ecf9698910e11a0c9ce |
| institution | Kabale University |
| issn | 2405-8440 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-cefc57a615e74ecf9698910e11a0c9ce2025-01-27T04:22:04ZengElsevierHeliyon2405-84402025-02-01113e42133Machine learning for predicting strength properties of waste iron slag concreteMatiur Rahman Raju0Syed Ishtiaq Ahmad1Md Mehedi Hasan2Noor Md. Sadiqul Hasan3Md Monirul Islam4Md. Abdul Basit5Ishraq Tasnim Hossain6Saif Ahmed Santo7Md Shahrior Alam8Mahfuzur Rahman9Department of Civil Engineering, International University of Business Agriculture and Technology, Dhaka, 1230, Bangladesh; Department of Civil Engineering, Bangladesh University of Engineering and Technology, Dhaka, BangladeshDepartment of Civil Engineering, Bangladesh University of Engineering and Technology, Dhaka, BangladeshDepartment of Computer Science and Engineering, International University of Business Agriculture and Technology, Dhaka, 1230, BangladeshDepartment of Civil Engineering, International University of Business Agriculture and Technology, Dhaka, 1230, BangladeshDepartment of Civil Engineering, International University of Business Agriculture and Technology, Dhaka, 1230, BangladeshDepartment of Civil Engineering, International University of Business Agriculture and Technology, Dhaka, 1230, BangladeshDepartment of Civil Engineering, International University of Business Agriculture and Technology, Dhaka, 1230, BangladeshDepartment of Civil Engineering, International University of Business Agriculture and Technology, Dhaka, 1230, BangladeshLocal Government Engineering Department (LGED), LGED Bhaban, Dhaka, 1207, BangladeshDepartment of Civil Engineering, International University of Business Agriculture and Technology, Dhaka, 1230, Bangladesh; Corresponding author.This study investigates the utilization of waste iron slag (WIS) as a sustainable alternative in concrete production to reduce environmental impact and preserve natural resources. The experimental investigation of WIS-incorporated concrete focused on compressive and tensile strength with machine learning (ML) models for prediction. Among the tested ML algorithms, Decision Tree (DT) and XGBoost showed the highest accuracy (R2 = 0.95135) in predicting concrete strength properties, while models like SVM and Symbolic Regression underperformed. Experimental results indicate that up to 20 % WIS replacement maintains adequate strength, whereas higher proportions reduce structural integrity. A ranking score index and cost analysis confirmed the technical and economic feasibility of using WIS in concrete. Cost analysis demonstrated substantial cost savings with 25 % WIS incorporation, confirming its economic feasibility. Integrating experimental data with ML predictions highlights WIS's potential for sustainable concrete applications, enabling optimized mix designs and reduced reliance on physical testing. Future work should address limitations, including dataset expansion and the exploration of additional durability and mechanical properties to validate WIS's practicality in construction further.http://www.sciencedirect.com/science/article/pii/S2405844025005134ConcreteCompressive strengthSplit tensile strengthWaste iron slagMachine learningCost analysis |
| spellingShingle | Matiur Rahman Raju Syed Ishtiaq Ahmad Md Mehedi Hasan Noor Md. Sadiqul Hasan Md Monirul Islam Md. Abdul Basit Ishraq Tasnim Hossain Saif Ahmed Santo Md Shahrior Alam Mahfuzur Rahman Machine learning for predicting strength properties of waste iron slag concrete Heliyon Concrete Compressive strength Split tensile strength Waste iron slag Machine learning Cost analysis |
| title | Machine learning for predicting strength properties of waste iron slag concrete |
| title_full | Machine learning for predicting strength properties of waste iron slag concrete |
| title_fullStr | Machine learning for predicting strength properties of waste iron slag concrete |
| title_full_unstemmed | Machine learning for predicting strength properties of waste iron slag concrete |
| title_short | Machine learning for predicting strength properties of waste iron slag concrete |
| title_sort | machine learning for predicting strength properties of waste iron slag concrete |
| topic | Concrete Compressive strength Split tensile strength Waste iron slag Machine learning Cost analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2405844025005134 |
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