RETRACTED ARTICLE: Utilizing ANFIS for prediction water absorption of lightweight geopolymers produced from waste materials
Abstract In the present work, water absorption of lightweight geopolymers produced by fine fly ash and rice husk–bark ash together with palm oil clinker (POC) aggregates has been investigated experimentally and modeled by adaptive network-based fuzzy inference systems (ANFIS). Different specimens ma...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Nazari, Ali [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2012 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag London Limited 2012 |
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| Übergeordnetes Werk: |
Enthalten in: Neural computing & applications - Springer London, 1993, 23(2012), 2 vom: 22. Apr., Seite 417-427 |
|---|---|
| Übergeordnetes Werk: |
volume:23 ; year:2012 ; number:2 ; day:22 ; month:04 ; pages:417-427 |
| Links: |
|---|
| DOI / URN: |
10.1007/s00521-012-0934-1 |
|---|
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OLC2025589697 |
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| 520 | |a Abstract In the present work, water absorption of lightweight geopolymers produced by fine fly ash and rice husk–bark ash together with palm oil clinker (POC) aggregates has been investigated experimentally and modeled by adaptive network-based fuzzy inference systems (ANFIS). Different specimens made from a mixture of fine fly ash and rice husk–bark ash with and without POC were subjected to water absorption tests at 2, 7, and 28 days of curing. The specimens were oven cured for 36 h at 80 °C and then cured at room temperature until 2, 7, and 28 days. The results showed that high amount of POC particles improve the percentage of water absorption at the early age of curing. In addition, the ratio of “the percentage of water absorption” to “weight” of the POC-contained specimens at all ages of curing was much higher than that of POC-free specimens, which make them suitable for lightweight applications. To build the model, training, validating, and testing using experimental results from 144 specimens were conducted. The used data in the ANFIS models are arranged in a format of six input parameters that cover the quantity of fine POC particles, the quantity of coarse POC particles, the quantity of FA + RHBA mixture, the ratio of alkali activator to ashes mixture, the age of curing, and the test trial number. According to these input parameters, the water absorption of each specimen was predicted. The training, validating, and testing results in the ANFIS models showed a strong potential for predicting the water absorption of the geopolymer specimens. | ||
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10.1007/s00521-012-0934-1 doi (DE-627)OLC2025589697 (DE-He213)s00521-012-0934-1-p DE-627 ger DE-627 rakwb eng 004 VZ Nazari, Ali verfasserin aut RETRACTED ARTICLE: Utilizing ANFIS for prediction water absorption of lightweight geopolymers produced from waste materials 2012 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Limited 2012 Abstract In the present work, water absorption of lightweight geopolymers produced by fine fly ash and rice husk–bark ash together with palm oil clinker (POC) aggregates has been investigated experimentally and modeled by adaptive network-based fuzzy inference systems (ANFIS). Different specimens made from a mixture of fine fly ash and rice husk–bark ash with and without POC were subjected to water absorption tests at 2, 7, and 28 days of curing. The specimens were oven cured for 36 h at 80 °C and then cured at room temperature until 2, 7, and 28 days. The results showed that high amount of POC particles improve the percentage of water absorption at the early age of curing. In addition, the ratio of “the percentage of water absorption” to “weight” of the POC-contained specimens at all ages of curing was much higher than that of POC-free specimens, which make them suitable for lightweight applications. To build the model, training, validating, and testing using experimental results from 144 specimens were conducted. The used data in the ANFIS models are arranged in a format of six input parameters that cover the quantity of fine POC particles, the quantity of coarse POC particles, the quantity of FA + RHBA mixture, the ratio of alkali activator to ashes mixture, the age of curing, and the test trial number. According to these input parameters, the water absorption of each specimen was predicted. The training, validating, and testing results in the ANFIS models showed a strong potential for predicting the water absorption of the geopolymer specimens. Geopolymer Water absorption Ashes mixture Palm oil clinker ANFIS Enthalten in Neural computing & applications Springer London, 1993 23(2012), 2 vom: 22. Apr., Seite 417-427 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:23 year:2012 number:2 day:22 month:04 pages:417-427 https://doi.org/10.1007/s00521-012-0934-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 AR 23 2012 2 22 04 417-427 |
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10.1007/s00521-012-0934-1 doi (DE-627)OLC2025589697 (DE-He213)s00521-012-0934-1-p DE-627 ger DE-627 rakwb eng 004 VZ Nazari, Ali verfasserin aut RETRACTED ARTICLE: Utilizing ANFIS for prediction water absorption of lightweight geopolymers produced from waste materials 2012 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Limited 2012 Abstract In the present work, water absorption of lightweight geopolymers produced by fine fly ash and rice husk–bark ash together with palm oil clinker (POC) aggregates has been investigated experimentally and modeled by adaptive network-based fuzzy inference systems (ANFIS). Different specimens made from a mixture of fine fly ash and rice husk–bark ash with and without POC were subjected to water absorption tests at 2, 7, and 28 days of curing. The specimens were oven cured for 36 h at 80 °C and then cured at room temperature until 2, 7, and 28 days. The results showed that high amount of POC particles improve the percentage of water absorption at the early age of curing. In addition, the ratio of “the percentage of water absorption” to “weight” of the POC-contained specimens at all ages of curing was much higher than that of POC-free specimens, which make them suitable for lightweight applications. To build the model, training, validating, and testing using experimental results from 144 specimens were conducted. The used data in the ANFIS models are arranged in a format of six input parameters that cover the quantity of fine POC particles, the quantity of coarse POC particles, the quantity of FA + RHBA mixture, the ratio of alkali activator to ashes mixture, the age of curing, and the test trial number. According to these input parameters, the water absorption of each specimen was predicted. The training, validating, and testing results in the ANFIS models showed a strong potential for predicting the water absorption of the geopolymer specimens. Geopolymer Water absorption Ashes mixture Palm oil clinker ANFIS Enthalten in Neural computing & applications Springer London, 1993 23(2012), 2 vom: 22. Apr., Seite 417-427 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:23 year:2012 number:2 day:22 month:04 pages:417-427 https://doi.org/10.1007/s00521-012-0934-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 AR 23 2012 2 22 04 417-427 |
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10.1007/s00521-012-0934-1 doi (DE-627)OLC2025589697 (DE-He213)s00521-012-0934-1-p DE-627 ger DE-627 rakwb eng 004 VZ Nazari, Ali verfasserin aut RETRACTED ARTICLE: Utilizing ANFIS for prediction water absorption of lightweight geopolymers produced from waste materials 2012 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Limited 2012 Abstract In the present work, water absorption of lightweight geopolymers produced by fine fly ash and rice husk–bark ash together with palm oil clinker (POC) aggregates has been investigated experimentally and modeled by adaptive network-based fuzzy inference systems (ANFIS). Different specimens made from a mixture of fine fly ash and rice husk–bark ash with and without POC were subjected to water absorption tests at 2, 7, and 28 days of curing. The specimens were oven cured for 36 h at 80 °C and then cured at room temperature until 2, 7, and 28 days. The results showed that high amount of POC particles improve the percentage of water absorption at the early age of curing. In addition, the ratio of “the percentage of water absorption” to “weight” of the POC-contained specimens at all ages of curing was much higher than that of POC-free specimens, which make them suitable for lightweight applications. To build the model, training, validating, and testing using experimental results from 144 specimens were conducted. The used data in the ANFIS models are arranged in a format of six input parameters that cover the quantity of fine POC particles, the quantity of coarse POC particles, the quantity of FA + RHBA mixture, the ratio of alkali activator to ashes mixture, the age of curing, and the test trial number. According to these input parameters, the water absorption of each specimen was predicted. The training, validating, and testing results in the ANFIS models showed a strong potential for predicting the water absorption of the geopolymer specimens. Geopolymer Water absorption Ashes mixture Palm oil clinker ANFIS Enthalten in Neural computing & applications Springer London, 1993 23(2012), 2 vom: 22. Apr., Seite 417-427 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:23 year:2012 number:2 day:22 month:04 pages:417-427 https://doi.org/10.1007/s00521-012-0934-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 AR 23 2012 2 22 04 417-427 |
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10.1007/s00521-012-0934-1 doi (DE-627)OLC2025589697 (DE-He213)s00521-012-0934-1-p DE-627 ger DE-627 rakwb eng 004 VZ Nazari, Ali verfasserin aut RETRACTED ARTICLE: Utilizing ANFIS for prediction water absorption of lightweight geopolymers produced from waste materials 2012 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Limited 2012 Abstract In the present work, water absorption of lightweight geopolymers produced by fine fly ash and rice husk–bark ash together with palm oil clinker (POC) aggregates has been investigated experimentally and modeled by adaptive network-based fuzzy inference systems (ANFIS). Different specimens made from a mixture of fine fly ash and rice husk–bark ash with and without POC were subjected to water absorption tests at 2, 7, and 28 days of curing. The specimens were oven cured for 36 h at 80 °C and then cured at room temperature until 2, 7, and 28 days. The results showed that high amount of POC particles improve the percentage of water absorption at the early age of curing. In addition, the ratio of “the percentage of water absorption” to “weight” of the POC-contained specimens at all ages of curing was much higher than that of POC-free specimens, which make them suitable for lightweight applications. To build the model, training, validating, and testing using experimental results from 144 specimens were conducted. The used data in the ANFIS models are arranged in a format of six input parameters that cover the quantity of fine POC particles, the quantity of coarse POC particles, the quantity of FA + RHBA mixture, the ratio of alkali activator to ashes mixture, the age of curing, and the test trial number. According to these input parameters, the water absorption of each specimen was predicted. The training, validating, and testing results in the ANFIS models showed a strong potential for predicting the water absorption of the geopolymer specimens. Geopolymer Water absorption Ashes mixture Palm oil clinker ANFIS Enthalten in Neural computing & applications Springer London, 1993 23(2012), 2 vom: 22. Apr., Seite 417-427 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:23 year:2012 number:2 day:22 month:04 pages:417-427 https://doi.org/10.1007/s00521-012-0934-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 AR 23 2012 2 22 04 417-427 |
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10.1007/s00521-012-0934-1 doi (DE-627)OLC2025589697 (DE-He213)s00521-012-0934-1-p DE-627 ger DE-627 rakwb eng 004 VZ Nazari, Ali verfasserin aut RETRACTED ARTICLE: Utilizing ANFIS for prediction water absorption of lightweight geopolymers produced from waste materials 2012 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Limited 2012 Abstract In the present work, water absorption of lightweight geopolymers produced by fine fly ash and rice husk–bark ash together with palm oil clinker (POC) aggregates has been investigated experimentally and modeled by adaptive network-based fuzzy inference systems (ANFIS). Different specimens made from a mixture of fine fly ash and rice husk–bark ash with and without POC were subjected to water absorption tests at 2, 7, and 28 days of curing. The specimens were oven cured for 36 h at 80 °C and then cured at room temperature until 2, 7, and 28 days. The results showed that high amount of POC particles improve the percentage of water absorption at the early age of curing. In addition, the ratio of “the percentage of water absorption” to “weight” of the POC-contained specimens at all ages of curing was much higher than that of POC-free specimens, which make them suitable for lightweight applications. To build the model, training, validating, and testing using experimental results from 144 specimens were conducted. The used data in the ANFIS models are arranged in a format of six input parameters that cover the quantity of fine POC particles, the quantity of coarse POC particles, the quantity of FA + RHBA mixture, the ratio of alkali activator to ashes mixture, the age of curing, and the test trial number. According to these input parameters, the water absorption of each specimen was predicted. The training, validating, and testing results in the ANFIS models showed a strong potential for predicting the water absorption of the geopolymer specimens. Geopolymer Water absorption Ashes mixture Palm oil clinker ANFIS Enthalten in Neural computing & applications Springer London, 1993 23(2012), 2 vom: 22. Apr., Seite 417-427 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:23 year:2012 number:2 day:22 month:04 pages:417-427 https://doi.org/10.1007/s00521-012-0934-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 AR 23 2012 2 22 04 417-427 |
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retracted article: utilizing anfis for prediction water absorption of lightweight geopolymers produced from waste materials |
| title_auth |
RETRACTED ARTICLE: Utilizing ANFIS for prediction water absorption of lightweight geopolymers produced from waste materials |
| abstract |
Abstract In the present work, water absorption of lightweight geopolymers produced by fine fly ash and rice husk–bark ash together with palm oil clinker (POC) aggregates has been investigated experimentally and modeled by adaptive network-based fuzzy inference systems (ANFIS). Different specimens made from a mixture of fine fly ash and rice husk–bark ash with and without POC were subjected to water absorption tests at 2, 7, and 28 days of curing. The specimens were oven cured for 36 h at 80 °C and then cured at room temperature until 2, 7, and 28 days. The results showed that high amount of POC particles improve the percentage of water absorption at the early age of curing. In addition, the ratio of “the percentage of water absorption” to “weight” of the POC-contained specimens at all ages of curing was much higher than that of POC-free specimens, which make them suitable for lightweight applications. To build the model, training, validating, and testing using experimental results from 144 specimens were conducted. The used data in the ANFIS models are arranged in a format of six input parameters that cover the quantity of fine POC particles, the quantity of coarse POC particles, the quantity of FA + RHBA mixture, the ratio of alkali activator to ashes mixture, the age of curing, and the test trial number. According to these input parameters, the water absorption of each specimen was predicted. The training, validating, and testing results in the ANFIS models showed a strong potential for predicting the water absorption of the geopolymer specimens. © Springer-Verlag London Limited 2012 |
| abstractGer |
Abstract In the present work, water absorption of lightweight geopolymers produced by fine fly ash and rice husk–bark ash together with palm oil clinker (POC) aggregates has been investigated experimentally and modeled by adaptive network-based fuzzy inference systems (ANFIS). Different specimens made from a mixture of fine fly ash and rice husk–bark ash with and without POC were subjected to water absorption tests at 2, 7, and 28 days of curing. The specimens were oven cured for 36 h at 80 °C and then cured at room temperature until 2, 7, and 28 days. The results showed that high amount of POC particles improve the percentage of water absorption at the early age of curing. In addition, the ratio of “the percentage of water absorption” to “weight” of the POC-contained specimens at all ages of curing was much higher than that of POC-free specimens, which make them suitable for lightweight applications. To build the model, training, validating, and testing using experimental results from 144 specimens were conducted. The used data in the ANFIS models are arranged in a format of six input parameters that cover the quantity of fine POC particles, the quantity of coarse POC particles, the quantity of FA + RHBA mixture, the ratio of alkali activator to ashes mixture, the age of curing, and the test trial number. According to these input parameters, the water absorption of each specimen was predicted. The training, validating, and testing results in the ANFIS models showed a strong potential for predicting the water absorption of the geopolymer specimens. © Springer-Verlag London Limited 2012 |
| abstract_unstemmed |
Abstract In the present work, water absorption of lightweight geopolymers produced by fine fly ash and rice husk–bark ash together with palm oil clinker (POC) aggregates has been investigated experimentally and modeled by adaptive network-based fuzzy inference systems (ANFIS). Different specimens made from a mixture of fine fly ash and rice husk–bark ash with and without POC were subjected to water absorption tests at 2, 7, and 28 days of curing. The specimens were oven cured for 36 h at 80 °C and then cured at room temperature until 2, 7, and 28 days. The results showed that high amount of POC particles improve the percentage of water absorption at the early age of curing. In addition, the ratio of “the percentage of water absorption” to “weight” of the POC-contained specimens at all ages of curing was much higher than that of POC-free specimens, which make them suitable for lightweight applications. To build the model, training, validating, and testing using experimental results from 144 specimens were conducted. The used data in the ANFIS models are arranged in a format of six input parameters that cover the quantity of fine POC particles, the quantity of coarse POC particles, the quantity of FA + RHBA mixture, the ratio of alkali activator to ashes mixture, the age of curing, and the test trial number. According to these input parameters, the water absorption of each specimen was predicted. The training, validating, and testing results in the ANFIS models showed a strong potential for predicting the water absorption of the geopolymer specimens. © Springer-Verlag London Limited 2012 |
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RETRACTED ARTICLE: Utilizing ANFIS for prediction water absorption of lightweight geopolymers produced from waste materials |
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https://doi.org/10.1007/s00521-012-0934-1 |
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