Optimizing mechanical properties of recycled aggregate concrete with graphene oxide and steel fibers: A predictive approach using ANN and RSM
The rapid increase in construction and demolition waste (CDW) has prompted considerable ecological apprehensions, steering research towards sustainable concrete alternatives. This study investigates the synergistic effects of graphene oxide (GO) and steel fibers (SF) on enhancing the mechanical prop...
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Elsevier
2025-06-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S259012302500951X |
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| author | S. Azhagarsamy N. Pannirselvam |
| author_facet | S. Azhagarsamy N. Pannirselvam |
| author_sort | S. Azhagarsamy |
| collection | DOAJ |
| description | The rapid increase in construction and demolition waste (CDW) has prompted considerable ecological apprehensions, steering research towards sustainable concrete alternatives. This study investigates the synergistic effects of graphene oxide (GO) and steel fibers (SF) on enhancing the mechanical properties of recycled aggregate concrete (RAC). Due to its extensive surface area and crack-bridging properties, GO improves hydration and microstructure and diminishes porosity, augmenting strength and durability. SF improve post-cracking performance, energy absorption, and stress redistribution. Concrete mixtures were developed by replacing natural coarse aggregate (NA) with recycled aggregate (RA) at proportions of 25, 50, and 100 % while using GO at 0.03, 0.05, and 0.1 wt percent of cement. The Two-Stage Mixing Approach (TSMA) was employed with untreated RA. Evaluations of workability, compressive strength, tensile strength, and flexural strength were performed and contrasted with control concrete. The ideal combination (25 % RA and 0.03 % GO) showed enhancements at 28 days: 15.62 % in compressive strength, 68.47 % in splitting tensile strength, and 38.21 % in flexural strength. The amalgamation of GO and SF presents a promising method for generating resilient, high-performance RAC. Predictive modeling employing Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) enhances mix design optimization for sustainable construction. |
| format | Article |
| id | doaj-art-6c0a6e3694dc4bf0ace093f0195b157f |
| institution | OA Journals |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-6c0a6e3694dc4bf0ace093f0195b157f2025-08-20T02:26:40ZengElsevierResults in Engineering2590-12302025-06-012610487510.1016/j.rineng.2025.104875Optimizing mechanical properties of recycled aggregate concrete with graphene oxide and steel fibers: A predictive approach using ANN and RSMS. Azhagarsamy0N. Pannirselvam1Department of Civil Engineering, SRM Institute of Science and Technology, Kattankulathur, Tamilnadu, 603 203, IndiaCorresponding author.; Department of Civil Engineering, SRM Institute of Science and Technology, Kattankulathur, Tamilnadu, 603 203, IndiaThe rapid increase in construction and demolition waste (CDW) has prompted considerable ecological apprehensions, steering research towards sustainable concrete alternatives. This study investigates the synergistic effects of graphene oxide (GO) and steel fibers (SF) on enhancing the mechanical properties of recycled aggregate concrete (RAC). Due to its extensive surface area and crack-bridging properties, GO improves hydration and microstructure and diminishes porosity, augmenting strength and durability. SF improve post-cracking performance, energy absorption, and stress redistribution. Concrete mixtures were developed by replacing natural coarse aggregate (NA) with recycled aggregate (RA) at proportions of 25, 50, and 100 % while using GO at 0.03, 0.05, and 0.1 wt percent of cement. The Two-Stage Mixing Approach (TSMA) was employed with untreated RA. Evaluations of workability, compressive strength, tensile strength, and flexural strength were performed and contrasted with control concrete. The ideal combination (25 % RA and 0.03 % GO) showed enhancements at 28 days: 15.62 % in compressive strength, 68.47 % in splitting tensile strength, and 38.21 % in flexural strength. The amalgamation of GO and SF presents a promising method for generating resilient, high-performance RAC. Predictive modeling employing Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) enhances mix design optimization for sustainable construction.http://www.sciencedirect.com/science/article/pii/S259012302500951XGraphene OxideMachine learningMechanical propertiesRecycled aggregate concreteSteel fiber |
| spellingShingle | S. Azhagarsamy N. Pannirselvam Optimizing mechanical properties of recycled aggregate concrete with graphene oxide and steel fibers: A predictive approach using ANN and RSM Results in Engineering Graphene Oxide Machine learning Mechanical properties Recycled aggregate concrete Steel fiber |
| title | Optimizing mechanical properties of recycled aggregate concrete with graphene oxide and steel fibers: A predictive approach using ANN and RSM |
| title_full | Optimizing mechanical properties of recycled aggregate concrete with graphene oxide and steel fibers: A predictive approach using ANN and RSM |
| title_fullStr | Optimizing mechanical properties of recycled aggregate concrete with graphene oxide and steel fibers: A predictive approach using ANN and RSM |
| title_full_unstemmed | Optimizing mechanical properties of recycled aggregate concrete with graphene oxide and steel fibers: A predictive approach using ANN and RSM |
| title_short | Optimizing mechanical properties of recycled aggregate concrete with graphene oxide and steel fibers: A predictive approach using ANN and RSM |
| title_sort | optimizing mechanical properties of recycled aggregate concrete with graphene oxide and steel fibers a predictive approach using ann and rsm |
| topic | Graphene Oxide Machine learning Mechanical properties Recycled aggregate concrete Steel fiber |
| url | http://www.sciencedirect.com/science/article/pii/S259012302500951X |
| work_keys_str_mv | AT sazhagarsamy optimizingmechanicalpropertiesofrecycledaggregateconcretewithgrapheneoxideandsteelfibersapredictiveapproachusingannandrsm AT npannirselvam optimizingmechanicalpropertiesofrecycledaggregateconcretewithgrapheneoxideandsteelfibersapredictiveapproachusingannandrsm |