A study on mechanical properties of inorganic binders used in textile reinforced concrete using artificial neural network
Abstract Textile-reinforced concrete (TRC) is a new and high-performance material made by embedding textile fibers into a matrix. Unlike traditional concrete, TRC doesn’t contain coarse aggregate and has a higher proportion of cement paste. In this study, three different types of binders organic, in...
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| Autor*in: |
A, Mohan [verfasserIn] Madhavi, T. Ch [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Asian journal of civil engineering - Springer International Publishing, 2017, 25(2024), 6 vom: 16. Mai, Seite 4513-4527 |
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| Übergeordnetes Werk: |
volume:25 ; year:2024 ; number:6 ; day:16 ; month:05 ; pages:4513-4527 |
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| DOI / URN: |
10.1007/s42107-024-01062-4 |
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| Katalog-ID: |
SPR056687389 |
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| 520 | |a Abstract Textile-reinforced concrete (TRC) is a new and high-performance material made by embedding textile fibers into a matrix. Unlike traditional concrete, TRC doesn’t contain coarse aggregate and has a higher proportion of cement paste. In this study, three different types of binders organic, inorganic geopolymer, and epoxy were examined for their impact on compressive and split tensile strengths. These binders were compared by testing mortar cubes (sized 70.6 mm) and cylinders (sized 75 × 150 mm) to see how they affect the properties of TRC. Various types of textile fabrics, including ARG, Basalt, and Hybrid, were experimented with to see how they influence compressive strength and failure modes. The study also examined how the thickness of the textile reinforcement (ranging from 5 to 15 mm) affects TRC performance. The results show that using a hybrid textile fabric with a cementitious binder leads to higher compressive strength. The experimental data underwent verification through the ANN method, where the neural network was trained akin to standard experimental research methodologies. With an impressive accuracy ($ R^{2} $ > 0.99), the ANN model successfully predicted mechanical properties, establishing its reliability in estimating responses and identifying parameters. Additionally, ANN techniques were instrumental in determining the optimal parameters for the study. | ||
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10.1007/s42107-024-01062-4 doi (DE-627)SPR056687389 (SPR)s42107-024-01062-4-e DE-627 ger DE-627 rakwb eng 624 VZ 624 VZ A, Mohan verfasserin aut A study on mechanical properties of inorganic binders used in textile reinforced concrete using artificial neural network 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Textile-reinforced concrete (TRC) is a new and high-performance material made by embedding textile fibers into a matrix. Unlike traditional concrete, TRC doesn’t contain coarse aggregate and has a higher proportion of cement paste. In this study, three different types of binders organic, inorganic geopolymer, and epoxy were examined for their impact on compressive and split tensile strengths. These binders were compared by testing mortar cubes (sized 70.6 mm) and cylinders (sized 75 × 150 mm) to see how they affect the properties of TRC. Various types of textile fabrics, including ARG, Basalt, and Hybrid, were experimented with to see how they influence compressive strength and failure modes. The study also examined how the thickness of the textile reinforcement (ranging from 5 to 15 mm) affects TRC performance. The results show that using a hybrid textile fabric with a cementitious binder leads to higher compressive strength. The experimental data underwent verification through the ANN method, where the neural network was trained akin to standard experimental research methodologies. With an impressive accuracy ($ R^{2} $ > 0.99), the ANN model successfully predicted mechanical properties, establishing its reliability in estimating responses and identifying parameters. Additionally, ANN techniques were instrumental in determining the optimal parameters for the study. Textile reinforced concrete (dpeaa)DE-He213 Inorganic binder (dpeaa)DE-He213 Organic binder (dpeaa)DE-He213 ANN (dpeaa)DE-He213 AR glass (dpeaa)DE-He213 Basalt textiles (dpeaa)DE-He213 Madhavi, T. Ch verfasserin aut Enthalten in Asian journal of civil engineering Springer International Publishing, 2017 25(2024), 6 vom: 16. Mai, Seite 4513-4527 (DE-627)101384565X (DE-600)2919928-1 2522-011X nnns volume:25 year:2024 number:6 day:16 month:05 pages:4513-4527 https://dx.doi.org/10.1007/s42107-024-01062-4 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 25 2024 6 16 05 4513-4527 |
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10.1007/s42107-024-01062-4 doi (DE-627)SPR056687389 (SPR)s42107-024-01062-4-e DE-627 ger DE-627 rakwb eng 624 VZ 624 VZ A, Mohan verfasserin aut A study on mechanical properties of inorganic binders used in textile reinforced concrete using artificial neural network 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Textile-reinforced concrete (TRC) is a new and high-performance material made by embedding textile fibers into a matrix. Unlike traditional concrete, TRC doesn’t contain coarse aggregate and has a higher proportion of cement paste. In this study, three different types of binders organic, inorganic geopolymer, and epoxy were examined for their impact on compressive and split tensile strengths. These binders were compared by testing mortar cubes (sized 70.6 mm) and cylinders (sized 75 × 150 mm) to see how they affect the properties of TRC. Various types of textile fabrics, including ARG, Basalt, and Hybrid, were experimented with to see how they influence compressive strength and failure modes. The study also examined how the thickness of the textile reinforcement (ranging from 5 to 15 mm) affects TRC performance. The results show that using a hybrid textile fabric with a cementitious binder leads to higher compressive strength. The experimental data underwent verification through the ANN method, where the neural network was trained akin to standard experimental research methodologies. With an impressive accuracy ($ R^{2} $ > 0.99), the ANN model successfully predicted mechanical properties, establishing its reliability in estimating responses and identifying parameters. Additionally, ANN techniques were instrumental in determining the optimal parameters for the study. Textile reinforced concrete (dpeaa)DE-He213 Inorganic binder (dpeaa)DE-He213 Organic binder (dpeaa)DE-He213 ANN (dpeaa)DE-He213 AR glass (dpeaa)DE-He213 Basalt textiles (dpeaa)DE-He213 Madhavi, T. Ch verfasserin aut Enthalten in Asian journal of civil engineering Springer International Publishing, 2017 25(2024), 6 vom: 16. Mai, Seite 4513-4527 (DE-627)101384565X (DE-600)2919928-1 2522-011X nnns volume:25 year:2024 number:6 day:16 month:05 pages:4513-4527 https://dx.doi.org/10.1007/s42107-024-01062-4 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 25 2024 6 16 05 4513-4527 |
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10.1007/s42107-024-01062-4 doi (DE-627)SPR056687389 (SPR)s42107-024-01062-4-e DE-627 ger DE-627 rakwb eng 624 VZ 624 VZ A, Mohan verfasserin aut A study on mechanical properties of inorganic binders used in textile reinforced concrete using artificial neural network 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Textile-reinforced concrete (TRC) is a new and high-performance material made by embedding textile fibers into a matrix. Unlike traditional concrete, TRC doesn’t contain coarse aggregate and has a higher proportion of cement paste. In this study, three different types of binders organic, inorganic geopolymer, and epoxy were examined for their impact on compressive and split tensile strengths. These binders were compared by testing mortar cubes (sized 70.6 mm) and cylinders (sized 75 × 150 mm) to see how they affect the properties of TRC. Various types of textile fabrics, including ARG, Basalt, and Hybrid, were experimented with to see how they influence compressive strength and failure modes. The study also examined how the thickness of the textile reinforcement (ranging from 5 to 15 mm) affects TRC performance. The results show that using a hybrid textile fabric with a cementitious binder leads to higher compressive strength. The experimental data underwent verification through the ANN method, where the neural network was trained akin to standard experimental research methodologies. With an impressive accuracy ($ R^{2} $ > 0.99), the ANN model successfully predicted mechanical properties, establishing its reliability in estimating responses and identifying parameters. Additionally, ANN techniques were instrumental in determining the optimal parameters for the study. Textile reinforced concrete (dpeaa)DE-He213 Inorganic binder (dpeaa)DE-He213 Organic binder (dpeaa)DE-He213 ANN (dpeaa)DE-He213 AR glass (dpeaa)DE-He213 Basalt textiles (dpeaa)DE-He213 Madhavi, T. Ch verfasserin aut Enthalten in Asian journal of civil engineering Springer International Publishing, 2017 25(2024), 6 vom: 16. Mai, Seite 4513-4527 (DE-627)101384565X (DE-600)2919928-1 2522-011X nnns volume:25 year:2024 number:6 day:16 month:05 pages:4513-4527 https://dx.doi.org/10.1007/s42107-024-01062-4 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 25 2024 6 16 05 4513-4527 |
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10.1007/s42107-024-01062-4 doi (DE-627)SPR056687389 (SPR)s42107-024-01062-4-e DE-627 ger DE-627 rakwb eng 624 VZ 624 VZ A, Mohan verfasserin aut A study on mechanical properties of inorganic binders used in textile reinforced concrete using artificial neural network 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Textile-reinforced concrete (TRC) is a new and high-performance material made by embedding textile fibers into a matrix. Unlike traditional concrete, TRC doesn’t contain coarse aggregate and has a higher proportion of cement paste. In this study, three different types of binders organic, inorganic geopolymer, and epoxy were examined for their impact on compressive and split tensile strengths. These binders were compared by testing mortar cubes (sized 70.6 mm) and cylinders (sized 75 × 150 mm) to see how they affect the properties of TRC. Various types of textile fabrics, including ARG, Basalt, and Hybrid, were experimented with to see how they influence compressive strength and failure modes. The study also examined how the thickness of the textile reinforcement (ranging from 5 to 15 mm) affects TRC performance. The results show that using a hybrid textile fabric with a cementitious binder leads to higher compressive strength. The experimental data underwent verification through the ANN method, where the neural network was trained akin to standard experimental research methodologies. With an impressive accuracy ($ R^{2} $ > 0.99), the ANN model successfully predicted mechanical properties, establishing its reliability in estimating responses and identifying parameters. Additionally, ANN techniques were instrumental in determining the optimal parameters for the study. Textile reinforced concrete (dpeaa)DE-He213 Inorganic binder (dpeaa)DE-He213 Organic binder (dpeaa)DE-He213 ANN (dpeaa)DE-He213 AR glass (dpeaa)DE-He213 Basalt textiles (dpeaa)DE-He213 Madhavi, T. Ch verfasserin aut Enthalten in Asian journal of civil engineering Springer International Publishing, 2017 25(2024), 6 vom: 16. Mai, Seite 4513-4527 (DE-627)101384565X (DE-600)2919928-1 2522-011X nnns volume:25 year:2024 number:6 day:16 month:05 pages:4513-4527 https://dx.doi.org/10.1007/s42107-024-01062-4 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 25 2024 6 16 05 4513-4527 |
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10.1007/s42107-024-01062-4 doi (DE-627)SPR056687389 (SPR)s42107-024-01062-4-e DE-627 ger DE-627 rakwb eng 624 VZ 624 VZ A, Mohan verfasserin aut A study on mechanical properties of inorganic binders used in textile reinforced concrete using artificial neural network 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Textile-reinforced concrete (TRC) is a new and high-performance material made by embedding textile fibers into a matrix. Unlike traditional concrete, TRC doesn’t contain coarse aggregate and has a higher proportion of cement paste. In this study, three different types of binders organic, inorganic geopolymer, and epoxy were examined for their impact on compressive and split tensile strengths. These binders were compared by testing mortar cubes (sized 70.6 mm) and cylinders (sized 75 × 150 mm) to see how they affect the properties of TRC. Various types of textile fabrics, including ARG, Basalt, and Hybrid, were experimented with to see how they influence compressive strength and failure modes. The study also examined how the thickness of the textile reinforcement (ranging from 5 to 15 mm) affects TRC performance. The results show that using a hybrid textile fabric with a cementitious binder leads to higher compressive strength. The experimental data underwent verification through the ANN method, where the neural network was trained akin to standard experimental research methodologies. With an impressive accuracy ($ R^{2} $ > 0.99), the ANN model successfully predicted mechanical properties, establishing its reliability in estimating responses and identifying parameters. Additionally, ANN techniques were instrumental in determining the optimal parameters for the study. Textile reinforced concrete (dpeaa)DE-He213 Inorganic binder (dpeaa)DE-He213 Organic binder (dpeaa)DE-He213 ANN (dpeaa)DE-He213 AR glass (dpeaa)DE-He213 Basalt textiles (dpeaa)DE-He213 Madhavi, T. Ch verfasserin aut Enthalten in Asian journal of civil engineering Springer International Publishing, 2017 25(2024), 6 vom: 16. Mai, Seite 4513-4527 (DE-627)101384565X (DE-600)2919928-1 2522-011X nnns volume:25 year:2024 number:6 day:16 month:05 pages:4513-4527 https://dx.doi.org/10.1007/s42107-024-01062-4 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 25 2024 6 16 05 4513-4527 |
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Textile-reinforced concrete (TRC) is a new and high-performance material made by embedding textile fibers into a matrix. Unlike traditional concrete, TRC doesn’t contain coarse aggregate and has a higher proportion of cement paste. In this study, three different types of binders organic, inorganic geopolymer, and epoxy were examined for their impact on compressive and split tensile strengths. These binders were compared by testing mortar cubes (sized 70.6 mm) and cylinders (sized 75 × 150 mm) to see how they affect the properties of TRC. 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a study on mechanical properties of inorganic binders used in textile reinforced concrete using artificial neural network |
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A study on mechanical properties of inorganic binders used in textile reinforced concrete using artificial neural network |
| abstract |
Abstract Textile-reinforced concrete (TRC) is a new and high-performance material made by embedding textile fibers into a matrix. Unlike traditional concrete, TRC doesn’t contain coarse aggregate and has a higher proportion of cement paste. In this study, three different types of binders organic, inorganic geopolymer, and epoxy were examined for their impact on compressive and split tensile strengths. These binders were compared by testing mortar cubes (sized 70.6 mm) and cylinders (sized 75 × 150 mm) to see how they affect the properties of TRC. Various types of textile fabrics, including ARG, Basalt, and Hybrid, were experimented with to see how they influence compressive strength and failure modes. The study also examined how the thickness of the textile reinforcement (ranging from 5 to 15 mm) affects TRC performance. The results show that using a hybrid textile fabric with a cementitious binder leads to higher compressive strength. The experimental data underwent verification through the ANN method, where the neural network was trained akin to standard experimental research methodologies. With an impressive accuracy ($ R^{2} $ > 0.99), the ANN model successfully predicted mechanical properties, establishing its reliability in estimating responses and identifying parameters. Additionally, ANN techniques were instrumental in determining the optimal parameters for the study. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Abstract Textile-reinforced concrete (TRC) is a new and high-performance material made by embedding textile fibers into a matrix. Unlike traditional concrete, TRC doesn’t contain coarse aggregate and has a higher proportion of cement paste. In this study, three different types of binders organic, inorganic geopolymer, and epoxy were examined for their impact on compressive and split tensile strengths. These binders were compared by testing mortar cubes (sized 70.6 mm) and cylinders (sized 75 × 150 mm) to see how they affect the properties of TRC. Various types of textile fabrics, including ARG, Basalt, and Hybrid, were experimented with to see how they influence compressive strength and failure modes. The study also examined how the thickness of the textile reinforcement (ranging from 5 to 15 mm) affects TRC performance. The results show that using a hybrid textile fabric with a cementitious binder leads to higher compressive strength. The experimental data underwent verification through the ANN method, where the neural network was trained akin to standard experimental research methodologies. With an impressive accuracy ($ R^{2} $ > 0.99), the ANN model successfully predicted mechanical properties, establishing its reliability in estimating responses and identifying parameters. Additionally, ANN techniques were instrumental in determining the optimal parameters for the study. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract Textile-reinforced concrete (TRC) is a new and high-performance material made by embedding textile fibers into a matrix. Unlike traditional concrete, TRC doesn’t contain coarse aggregate and has a higher proportion of cement paste. In this study, three different types of binders organic, inorganic geopolymer, and epoxy were examined for their impact on compressive and split tensile strengths. These binders were compared by testing mortar cubes (sized 70.6 mm) and cylinders (sized 75 × 150 mm) to see how they affect the properties of TRC. Various types of textile fabrics, including ARG, Basalt, and Hybrid, were experimented with to see how they influence compressive strength and failure modes. The study also examined how the thickness of the textile reinforcement (ranging from 5 to 15 mm) affects TRC performance. The results show that using a hybrid textile fabric with a cementitious binder leads to higher compressive strength. The experimental data underwent verification through the ANN method, where the neural network was trained akin to standard experimental research methodologies. With an impressive accuracy ($ R^{2} $ > 0.99), the ANN model successfully predicted mechanical properties, establishing its reliability in estimating responses and identifying parameters. Additionally, ANN techniques were instrumental in determining the optimal parameters for the study. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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A study on mechanical properties of inorganic binders used in textile reinforced concrete using artificial neural network |
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https://dx.doi.org/10.1007/s42107-024-01062-4 |
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Madhavi, T. Ch |
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10.1007/s42107-024-01062-4 |
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2024-07-23T04:49:55.177Z |
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|
| score |
7.400218 |