Eco-friendly concrete incorporating palm oil fuel ash: Fresh and mechanical properties with machine learning prediction, and sustainability assessment
Rising natural resource consumption leads to increased hazardous gas emissions, necessitating the concrete industry's focus on sustainable alternatives like palm oil fuel ash (POFA) to replace cement. Also, advanced machine learning (ML) techniques can uncover previously unreported insights abo...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-11-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S240584402309504X |
| _version_ | 1797429724171993088 |
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| author | Noor Md. Sadiqul Hasan Md. Habibur Rahman Sobuz Nur Mohammad Nazmus Shaurdho Md. Montaseer Meraz Shuvo Dip Datta Fahim Shahriyar Aditto Md. Kawsarul Islam Kabbo Md Jihad Miah |
| author_facet | Noor Md. Sadiqul Hasan Md. Habibur Rahman Sobuz Nur Mohammad Nazmus Shaurdho Md. Montaseer Meraz Shuvo Dip Datta Fahim Shahriyar Aditto Md. Kawsarul Islam Kabbo Md Jihad Miah |
| author_sort | Noor Md. Sadiqul Hasan |
| collection | DOAJ |
| description | Rising natural resource consumption leads to increased hazardous gas emissions, necessitating the concrete industry's focus on sustainable alternatives like palm oil fuel ash (POFA) to replace cement. Also, advanced machine learning (ML) techniques can uncover previously unreported insights about the effects of POFA that may be missing from the literature. Hence, this study investigates the influence of varying concentrations of POFA on fresh and mechanical characteristics with quantifying ML approaches and microstructural performance, as well as the environmental impact of structural concrete. For this, cement substitutions of 5 %, 15 %, 25 %, 35 %, and 45 % (by weight of cement) were utilized. POFA enhanced the overall concrete workability, with slump increments ranging from approximately 9 %–55 % and compacting factor increments of 4 %–12 %. Mechanical performance of POFA concrete improved up to 25 % replacement levels, with the highest enhancements observed in compressive (4.5 %), splitting tensile (36 %), and flexural (31 %) strength, for the mix containing 15 % POFA. The finer particle size of POFA improved microstructural performance by reducing porosity, aligning with the enhanced mechanical strength. The environmental impact of POFA was assessed by measuring eCO2 emissions, revealing a potential reduction of up to 44 %. Incorporating 5 %–15 % POFA yielded ideal mechanical performance results, significantly enhancing sustainability and cost-effectiveness. Regarding the ML approach, it can be observed that a low regression coefficient (R2) contrasts sharply with the higher R2 values for the random forest (RF) and the ensemble model, indicating satisfactory precision prediction with experimental results. |
| first_indexed | 2024-03-09T09:17:17Z |
| format | Article |
| id | doaj.art-e3c3226b895c48148602c86199b18d08 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-03-09T09:17:17Z |
| publishDate | 2023-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-e3c3226b895c48148602c86199b18d082023-12-02T07:05:41ZengElsevierHeliyon2405-84402023-11-01911e22296Eco-friendly concrete incorporating palm oil fuel ash: Fresh and mechanical properties with machine learning prediction, and sustainability assessmentNoor Md. Sadiqul Hasan0Md. Habibur Rahman Sobuz1Nur Mohammad Nazmus Shaurdho2Md. Montaseer Meraz3Shuvo Dip Datta4Fahim Shahriyar Aditto5Md. Kawsarul Islam Kabbo6Md Jihad Miah7Department of Civil Engineering, College of Engineering and Technology, International University of Business Agriculture and Technology, Dhaka 1230, BangladeshDepartment of Building Engineering and Construction Management, Khulna University of Engineering & Technology, Khulna 9203, Bangladesh; Corresponding author. habib@becm.kuet.ac.bd (M.H.R.S).Department of Civil Engineering, College of Engineering and Technology, International University of Business Agriculture and Technology, Dhaka 1230, BangladeshDepartment of Building Engineering and Construction Management, Khulna University of Engineering & Technology, Khulna 9203, BangladeshDepartment of Building Engineering and Construction Management, Khulna University of Engineering & Technology, Khulna 9203, BangladeshDepartment of Building Engineering and Construction Management, Khulna University of Engineering & Technology, Khulna 9203, BangladeshDepartment of Building Engineering and Construction Management, Khulna University of Engineering & Technology, Khulna 9203, BangladeshDepartment of Civil Engineering, University of Asia Pacific, Dhaka 1205, BangladeshRising natural resource consumption leads to increased hazardous gas emissions, necessitating the concrete industry's focus on sustainable alternatives like palm oil fuel ash (POFA) to replace cement. Also, advanced machine learning (ML) techniques can uncover previously unreported insights about the effects of POFA that may be missing from the literature. Hence, this study investigates the influence of varying concentrations of POFA on fresh and mechanical characteristics with quantifying ML approaches and microstructural performance, as well as the environmental impact of structural concrete. For this, cement substitutions of 5 %, 15 %, 25 %, 35 %, and 45 % (by weight of cement) were utilized. POFA enhanced the overall concrete workability, with slump increments ranging from approximately 9 %–55 % and compacting factor increments of 4 %–12 %. Mechanical performance of POFA concrete improved up to 25 % replacement levels, with the highest enhancements observed in compressive (4.5 %), splitting tensile (36 %), and flexural (31 %) strength, for the mix containing 15 % POFA. The finer particle size of POFA improved microstructural performance by reducing porosity, aligning with the enhanced mechanical strength. The environmental impact of POFA was assessed by measuring eCO2 emissions, revealing a potential reduction of up to 44 %. Incorporating 5 %–15 % POFA yielded ideal mechanical performance results, significantly enhancing sustainability and cost-effectiveness. Regarding the ML approach, it can be observed that a low regression coefficient (R2) contrasts sharply with the higher R2 values for the random forest (RF) and the ensemble model, indicating satisfactory precision prediction with experimental results.http://www.sciencedirect.com/science/article/pii/S240584402309504XConcreteCost and eCO2 emissionsEco-friendlyFresh characteristicsMachine learning techniquesMechanical performance |
| spellingShingle | Noor Md. Sadiqul Hasan Md. Habibur Rahman Sobuz Nur Mohammad Nazmus Shaurdho Md. Montaseer Meraz Shuvo Dip Datta Fahim Shahriyar Aditto Md. Kawsarul Islam Kabbo Md Jihad Miah Eco-friendly concrete incorporating palm oil fuel ash: Fresh and mechanical properties with machine learning prediction, and sustainability assessment Heliyon Concrete Cost and eCO2 emissions Eco-friendly Fresh characteristics Machine learning techniques Mechanical performance |
| title | Eco-friendly concrete incorporating palm oil fuel ash: Fresh and mechanical properties with machine learning prediction, and sustainability assessment |
| title_full | Eco-friendly concrete incorporating palm oil fuel ash: Fresh and mechanical properties with machine learning prediction, and sustainability assessment |
| title_fullStr | Eco-friendly concrete incorporating palm oil fuel ash: Fresh and mechanical properties with machine learning prediction, and sustainability assessment |
| title_full_unstemmed | Eco-friendly concrete incorporating palm oil fuel ash: Fresh and mechanical properties with machine learning prediction, and sustainability assessment |
| title_short | Eco-friendly concrete incorporating palm oil fuel ash: Fresh and mechanical properties with machine learning prediction, and sustainability assessment |
| title_sort | eco friendly concrete incorporating palm oil fuel ash fresh and mechanical properties with machine learning prediction and sustainability assessment |
| topic | Concrete Cost and eCO2 emissions Eco-friendly Fresh characteristics Machine learning techniques Mechanical performance |
| url | http://www.sciencedirect.com/science/article/pii/S240584402309504X |
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