AI-Driven Optimization of Fly Ash-Based Geopolymer Concrete for Sustainable High Strength and CO<sub>2</sub> Reduction: An Application of Hybrid Taguchi–Grey–ANN Approach
The construction industry urgently requires sustainable alternatives to conventional concrete to reduce its environmental impact. This study addresses this challenge by developing machine learning-optimized geopolymer concrete (GPC) using industrial waste fly ash as cement replacement. An integrated...
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MDPI AG
2025-06-01
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| author | Muhammad Usman Siddiq Muhammad Kashif Anwar Faris H. Almansour Muhammad Ahmed Qurashi Muhammad Adeel |
| author_facet | Muhammad Usman Siddiq Muhammad Kashif Anwar Faris H. Almansour Muhammad Ahmed Qurashi Muhammad Adeel |
| author_sort | Muhammad Usman Siddiq |
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| description | The construction industry urgently requires sustainable alternatives to conventional concrete to reduce its environmental impact. This study addresses this challenge by developing machine learning-optimized geopolymer concrete (GPC) using industrial waste fly ash as cement replacement. An integrated Taguchi–Grey relational analysis (GRA) and artificial neural network (ANN) approach was developed to simultaneously optimize mechanical properties and environmental performance. The methodology analyzes over 1000 data points from 83 studies to identify key mix parameters including fly ash content, NaOH/Na<sub>2</sub>SiO<sub>3</sub> ratio, and curing conditions. Results indicate that the optimized FA-GPC formulation achieves a 78% reduction in CO<sub>2</sub> emissions, decreasing from 252.09 kg/m<sup>3</sup> (GRC rank 1) to 55.0 kg/m<sup>3</sup>, while maintaining a compressive strength of 90.9 MPa. The ANN model demonstrates strong predictive capability, with R<sup>2</sup> > 0.95 for strength and environmental impact. Life cycle assessment reveals potential savings of 3941 tons of CO<sub>2</sub> over 20 years for projects using 1000 m<sup>3</sup> annually. This research provides a data-driven framework for sustainable concrete design, offering practical mix design guidelines and demonstrating the viability of fly ash-based GPC as high-performance, low-carbon construction material. |
| format | Article |
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| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Buildings |
| spelling | doaj-art-d0a25ff9e12c44568fba03ef08ad73cd2025-08-20T02:24:29ZengMDPI AGBuildings2075-53092025-06-011512208110.3390/buildings15122081AI-Driven Optimization of Fly Ash-Based Geopolymer Concrete for Sustainable High Strength and CO<sub>2</sub> Reduction: An Application of Hybrid Taguchi–Grey–ANN ApproachMuhammad Usman Siddiq0Muhammad Kashif Anwar1Faris H. Almansour2Muhammad Ahmed Qurashi3Muhammad Adeel4Civil and Building Services Engineering Division, School of Built Environment and Architecture, London South Bank University, 103 Borough Road, London SE1 0AA, UKKey Laboratory of Road and Traffic Engineering of the Ministry of Education, College of Transportation Engineering, Tongji University, Shanghai 201804, ChinaCarbon Management Technologies Institute, King Abdulaziz City for Science and Technology, Riyadh 11442, Saudi ArabiaDepartment of Bridge Engineering, College of Civil Engineering, Tongji University, Siping Road 1239, Shanghai 200092, ChinaSchool of Mechanical Engineering and Automation, Bei Hang University, 37 Xueyuan Road, Haidian District, Beijing 100191, ChinaThe construction industry urgently requires sustainable alternatives to conventional concrete to reduce its environmental impact. This study addresses this challenge by developing machine learning-optimized geopolymer concrete (GPC) using industrial waste fly ash as cement replacement. An integrated Taguchi–Grey relational analysis (GRA) and artificial neural network (ANN) approach was developed to simultaneously optimize mechanical properties and environmental performance. The methodology analyzes over 1000 data points from 83 studies to identify key mix parameters including fly ash content, NaOH/Na<sub>2</sub>SiO<sub>3</sub> ratio, and curing conditions. Results indicate that the optimized FA-GPC formulation achieves a 78% reduction in CO<sub>2</sub> emissions, decreasing from 252.09 kg/m<sup>3</sup> (GRC rank 1) to 55.0 kg/m<sup>3</sup>, while maintaining a compressive strength of 90.9 MPa. The ANN model demonstrates strong predictive capability, with R<sup>2</sup> > 0.95 for strength and environmental impact. Life cycle assessment reveals potential savings of 3941 tons of CO<sub>2</sub> over 20 years for projects using 1000 m<sup>3</sup> annually. This research provides a data-driven framework for sustainable concrete design, offering practical mix design guidelines and demonstrating the viability of fly ash-based GPC as high-performance, low-carbon construction material.https://www.mdpi.com/2075-5309/15/12/2081sustainabilityfly ashcompressive strengthgreen and sustainable concretehigh performance concreteCO<sub>2</sub> reduction |
| spellingShingle | Muhammad Usman Siddiq Muhammad Kashif Anwar Faris H. Almansour Muhammad Ahmed Qurashi Muhammad Adeel AI-Driven Optimization of Fly Ash-Based Geopolymer Concrete for Sustainable High Strength and CO<sub>2</sub> Reduction: An Application of Hybrid Taguchi–Grey–ANN Approach Buildings sustainability fly ash compressive strength green and sustainable concrete high performance concrete CO<sub>2</sub> reduction |
| title | AI-Driven Optimization of Fly Ash-Based Geopolymer Concrete for Sustainable High Strength and CO<sub>2</sub> Reduction: An Application of Hybrid Taguchi–Grey–ANN Approach |
| title_full | AI-Driven Optimization of Fly Ash-Based Geopolymer Concrete for Sustainable High Strength and CO<sub>2</sub> Reduction: An Application of Hybrid Taguchi–Grey–ANN Approach |
| title_fullStr | AI-Driven Optimization of Fly Ash-Based Geopolymer Concrete for Sustainable High Strength and CO<sub>2</sub> Reduction: An Application of Hybrid Taguchi–Grey–ANN Approach |
| title_full_unstemmed | AI-Driven Optimization of Fly Ash-Based Geopolymer Concrete for Sustainable High Strength and CO<sub>2</sub> Reduction: An Application of Hybrid Taguchi–Grey–ANN Approach |
| title_short | AI-Driven Optimization of Fly Ash-Based Geopolymer Concrete for Sustainable High Strength and CO<sub>2</sub> Reduction: An Application of Hybrid Taguchi–Grey–ANN Approach |
| title_sort | ai driven optimization of fly ash based geopolymer concrete for sustainable high strength and co sub 2 sub reduction an application of hybrid taguchi grey ann approach |
| topic | sustainability fly ash compressive strength green and sustainable concrete high performance concrete CO<sub>2</sub> reduction |
| url | https://www.mdpi.com/2075-5309/15/12/2081 |
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