Prediction and identification of capillary water absorption capacity of travertine dimension stone
Abstract The capillary transport mechanism has an important role on natural building stones that could be affected by moisture due to atmospheric conditions. Travertine with its natural porous structure is more sensitive to water which has a significant impact on durability. One of the important ind...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Çobanoğlu, İbrahim [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Arabian journal of geosciences - Berlin : Springer, 2008, 8(2015), 11 vom: 25. Apr., Seite 10135-10149 |
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| Übergeordnetes Werk: |
volume:8 ; year:2015 ; number:11 ; day:25 ; month:04 ; pages:10135-10149 |
| Links: |
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| DOI / URN: |
10.1007/s12517-015-1902-8 |
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SPR025943936 |
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| 520 | |a Abstract The capillary transport mechanism has an important role on natural building stones that could be affected by moisture due to atmospheric conditions. Travertine with its natural porous structure is more sensitive to water which has a significant impact on durability. One of the important indicators of stone–water interaction is color variations, while natural stones with high capillary water absorption potential are in contact with water due to atmospheric conditions. As far as water absorption is concerned, water absorption by weight (WAW), water absorption by volume (n, apparent porosity), and capillary water absorption (CWA) parameters are in close relation with each other. To develop the relationships between capillary water absorption and the other water absorption parameters, regression and artificial neural network (ANN) analyses were performed. Within the scope of this study, the ANN models with three different input structures were established using various input variables in order to determine the relationship between CWA and the other parameters. The results of the different ANN structures of these models have been calculated and evaluated. For this purpose, travertine samples quarried from Denizli region in Turkey were studied. In order to evaluate the model results of regression and ANN methods, statistical performance evaluation parameters, i.e., the coefficient of correlation (CORR), efficiency (E), and root mean square error (RMSE), were calculated. In both regression and ANN models, reasonable coefficients of correlation were calculated. The capillary water absorption test is time consuming. The necessary periodical measurements make the process even more impractical. It is known that capillary water absorption is directly correlated with similar water absorption parameters such as water absorption by weight and apparent porosity. In this study, the estimation of capillary water absorption by means of other water absorption parameters is proposed. Moreover, there is no proposed classification in literature for natural stones based on their capillary water absorption. In the final stage of this study, by using the relationships between porosity and capillary water absorption, a capillary water absorption classification was proposed for travertine. Proposed classification consists of five categories and capillary water absorption coefficient which is ranging between very high (>68 g/$ m^{2} $ $ s^{0.5} $) and very low (<1 g/$ m^{2} $ $ s^{0.5} $). It is thought that this study could provide significant advantages in engineering practice and also make contribution to the related literature. | ||
| 650 | 4 | |a Capillary water absorption |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Travertine |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Dimension stone |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Regression analysis |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Artificial neural networks |7 (dpeaa)DE-He213 | |
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10.1007/s12517-015-1902-8 doi (DE-627)SPR025943936 (SPR)s12517-015-1902-8-e DE-627 ger DE-627 rakwb eng 550 ASE Çobanoğlu, İbrahim verfasserin aut Prediction and identification of capillary water absorption capacity of travertine dimension stone 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The capillary transport mechanism has an important role on natural building stones that could be affected by moisture due to atmospheric conditions. Travertine with its natural porous structure is more sensitive to water which has a significant impact on durability. One of the important indicators of stone–water interaction is color variations, while natural stones with high capillary water absorption potential are in contact with water due to atmospheric conditions. As far as water absorption is concerned, water absorption by weight (WAW), water absorption by volume (n, apparent porosity), and capillary water absorption (CWA) parameters are in close relation with each other. To develop the relationships between capillary water absorption and the other water absorption parameters, regression and artificial neural network (ANN) analyses were performed. Within the scope of this study, the ANN models with three different input structures were established using various input variables in order to determine the relationship between CWA and the other parameters. The results of the different ANN structures of these models have been calculated and evaluated. For this purpose, travertine samples quarried from Denizli region in Turkey were studied. In order to evaluate the model results of regression and ANN methods, statistical performance evaluation parameters, i.e., the coefficient of correlation (CORR), efficiency (E), and root mean square error (RMSE), were calculated. In both regression and ANN models, reasonable coefficients of correlation were calculated. The capillary water absorption test is time consuming. The necessary periodical measurements make the process even more impractical. It is known that capillary water absorption is directly correlated with similar water absorption parameters such as water absorption by weight and apparent porosity. In this study, the estimation of capillary water absorption by means of other water absorption parameters is proposed. Moreover, there is no proposed classification in literature for natural stones based on their capillary water absorption. In the final stage of this study, by using the relationships between porosity and capillary water absorption, a capillary water absorption classification was proposed for travertine. Proposed classification consists of five categories and capillary water absorption coefficient which is ranging between very high (>68 g/$ m^{2} $ $ s^{0.5} $) and very low (<1 g/$ m^{2} $ $ s^{0.5} $). It is thought that this study could provide significant advantages in engineering practice and also make contribution to the related literature. Capillary water absorption (dpeaa)DE-He213 Travertine (dpeaa)DE-He213 Dimension stone (dpeaa)DE-He213 Regression analysis (dpeaa)DE-He213 Artificial neural networks (dpeaa)DE-He213 Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 8(2015), 11 vom: 25. Apr., Seite 10135-10149 (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:8 year:2015 number:11 day:25 month:04 pages:10135-10149 https://dx.doi.org/10.1007/s12517-015-1902-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 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_2008 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_4242 GBV_ILN_4246 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 8 2015 11 25 04 10135-10149 |
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10.1007/s12517-015-1902-8 doi (DE-627)SPR025943936 (SPR)s12517-015-1902-8-e DE-627 ger DE-627 rakwb eng 550 ASE Çobanoğlu, İbrahim verfasserin aut Prediction and identification of capillary water absorption capacity of travertine dimension stone 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The capillary transport mechanism has an important role on natural building stones that could be affected by moisture due to atmospheric conditions. Travertine with its natural porous structure is more sensitive to water which has a significant impact on durability. One of the important indicators of stone–water interaction is color variations, while natural stones with high capillary water absorption potential are in contact with water due to atmospheric conditions. As far as water absorption is concerned, water absorption by weight (WAW), water absorption by volume (n, apparent porosity), and capillary water absorption (CWA) parameters are in close relation with each other. To develop the relationships between capillary water absorption and the other water absorption parameters, regression and artificial neural network (ANN) analyses were performed. Within the scope of this study, the ANN models with three different input structures were established using various input variables in order to determine the relationship between CWA and the other parameters. The results of the different ANN structures of these models have been calculated and evaluated. For this purpose, travertine samples quarried from Denizli region in Turkey were studied. In order to evaluate the model results of regression and ANN methods, statistical performance evaluation parameters, i.e., the coefficient of correlation (CORR), efficiency (E), and root mean square error (RMSE), were calculated. In both regression and ANN models, reasonable coefficients of correlation were calculated. The capillary water absorption test is time consuming. The necessary periodical measurements make the process even more impractical. It is known that capillary water absorption is directly correlated with similar water absorption parameters such as water absorption by weight and apparent porosity. In this study, the estimation of capillary water absorption by means of other water absorption parameters is proposed. Moreover, there is no proposed classification in literature for natural stones based on their capillary water absorption. In the final stage of this study, by using the relationships between porosity and capillary water absorption, a capillary water absorption classification was proposed for travertine. Proposed classification consists of five categories and capillary water absorption coefficient which is ranging between very high (>68 g/$ m^{2} $ $ s^{0.5} $) and very low (<1 g/$ m^{2} $ $ s^{0.5} $). It is thought that this study could provide significant advantages in engineering practice and also make contribution to the related literature. Capillary water absorption (dpeaa)DE-He213 Travertine (dpeaa)DE-He213 Dimension stone (dpeaa)DE-He213 Regression analysis (dpeaa)DE-He213 Artificial neural networks (dpeaa)DE-He213 Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 8(2015), 11 vom: 25. Apr., Seite 10135-10149 (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:8 year:2015 number:11 day:25 month:04 pages:10135-10149 https://dx.doi.org/10.1007/s12517-015-1902-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 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_2008 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_4242 GBV_ILN_4246 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 8 2015 11 25 04 10135-10149 |
| allfields_unstemmed |
10.1007/s12517-015-1902-8 doi (DE-627)SPR025943936 (SPR)s12517-015-1902-8-e DE-627 ger DE-627 rakwb eng 550 ASE Çobanoğlu, İbrahim verfasserin aut Prediction and identification of capillary water absorption capacity of travertine dimension stone 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The capillary transport mechanism has an important role on natural building stones that could be affected by moisture due to atmospheric conditions. Travertine with its natural porous structure is more sensitive to water which has a significant impact on durability. One of the important indicators of stone–water interaction is color variations, while natural stones with high capillary water absorption potential are in contact with water due to atmospheric conditions. As far as water absorption is concerned, water absorption by weight (WAW), water absorption by volume (n, apparent porosity), and capillary water absorption (CWA) parameters are in close relation with each other. To develop the relationships between capillary water absorption and the other water absorption parameters, regression and artificial neural network (ANN) analyses were performed. Within the scope of this study, the ANN models with three different input structures were established using various input variables in order to determine the relationship between CWA and the other parameters. The results of the different ANN structures of these models have been calculated and evaluated. For this purpose, travertine samples quarried from Denizli region in Turkey were studied. In order to evaluate the model results of regression and ANN methods, statistical performance evaluation parameters, i.e., the coefficient of correlation (CORR), efficiency (E), and root mean square error (RMSE), were calculated. In both regression and ANN models, reasonable coefficients of correlation were calculated. The capillary water absorption test is time consuming. The necessary periodical measurements make the process even more impractical. It is known that capillary water absorption is directly correlated with similar water absorption parameters such as water absorption by weight and apparent porosity. In this study, the estimation of capillary water absorption by means of other water absorption parameters is proposed. Moreover, there is no proposed classification in literature for natural stones based on their capillary water absorption. In the final stage of this study, by using the relationships between porosity and capillary water absorption, a capillary water absorption classification was proposed for travertine. Proposed classification consists of five categories and capillary water absorption coefficient which is ranging between very high (>68 g/$ m^{2} $ $ s^{0.5} $) and very low (<1 g/$ m^{2} $ $ s^{0.5} $). It is thought that this study could provide significant advantages in engineering practice and also make contribution to the related literature. Capillary water absorption (dpeaa)DE-He213 Travertine (dpeaa)DE-He213 Dimension stone (dpeaa)DE-He213 Regression analysis (dpeaa)DE-He213 Artificial neural networks (dpeaa)DE-He213 Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 8(2015), 11 vom: 25. Apr., Seite 10135-10149 (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:8 year:2015 number:11 day:25 month:04 pages:10135-10149 https://dx.doi.org/10.1007/s12517-015-1902-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 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_2008 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_4242 GBV_ILN_4246 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 8 2015 11 25 04 10135-10149 |
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10.1007/s12517-015-1902-8 doi (DE-627)SPR025943936 (SPR)s12517-015-1902-8-e DE-627 ger DE-627 rakwb eng 550 ASE Çobanoğlu, İbrahim verfasserin aut Prediction and identification of capillary water absorption capacity of travertine dimension stone 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The capillary transport mechanism has an important role on natural building stones that could be affected by moisture due to atmospheric conditions. Travertine with its natural porous structure is more sensitive to water which has a significant impact on durability. One of the important indicators of stone–water interaction is color variations, while natural stones with high capillary water absorption potential are in contact with water due to atmospheric conditions. As far as water absorption is concerned, water absorption by weight (WAW), water absorption by volume (n, apparent porosity), and capillary water absorption (CWA) parameters are in close relation with each other. To develop the relationships between capillary water absorption and the other water absorption parameters, regression and artificial neural network (ANN) analyses were performed. Within the scope of this study, the ANN models with three different input structures were established using various input variables in order to determine the relationship between CWA and the other parameters. The results of the different ANN structures of these models have been calculated and evaluated. For this purpose, travertine samples quarried from Denizli region in Turkey were studied. In order to evaluate the model results of regression and ANN methods, statistical performance evaluation parameters, i.e., the coefficient of correlation (CORR), efficiency (E), and root mean square error (RMSE), were calculated. In both regression and ANN models, reasonable coefficients of correlation were calculated. The capillary water absorption test is time consuming. The necessary periodical measurements make the process even more impractical. It is known that capillary water absorption is directly correlated with similar water absorption parameters such as water absorption by weight and apparent porosity. In this study, the estimation of capillary water absorption by means of other water absorption parameters is proposed. Moreover, there is no proposed classification in literature for natural stones based on their capillary water absorption. In the final stage of this study, by using the relationships between porosity and capillary water absorption, a capillary water absorption classification was proposed for travertine. Proposed classification consists of five categories and capillary water absorption coefficient which is ranging between very high (>68 g/$ m^{2} $ $ s^{0.5} $) and very low (<1 g/$ m^{2} $ $ s^{0.5} $). It is thought that this study could provide significant advantages in engineering practice and also make contribution to the related literature. Capillary water absorption (dpeaa)DE-He213 Travertine (dpeaa)DE-He213 Dimension stone (dpeaa)DE-He213 Regression analysis (dpeaa)DE-He213 Artificial neural networks (dpeaa)DE-He213 Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 8(2015), 11 vom: 25. Apr., Seite 10135-10149 (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:8 year:2015 number:11 day:25 month:04 pages:10135-10149 https://dx.doi.org/10.1007/s12517-015-1902-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 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_2008 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_4242 GBV_ILN_4246 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 8 2015 11 25 04 10135-10149 |
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10.1007/s12517-015-1902-8 doi (DE-627)SPR025943936 (SPR)s12517-015-1902-8-e DE-627 ger DE-627 rakwb eng 550 ASE Çobanoğlu, İbrahim verfasserin aut Prediction and identification of capillary water absorption capacity of travertine dimension stone 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The capillary transport mechanism has an important role on natural building stones that could be affected by moisture due to atmospheric conditions. Travertine with its natural porous structure is more sensitive to water which has a significant impact on durability. One of the important indicators of stone–water interaction is color variations, while natural stones with high capillary water absorption potential are in contact with water due to atmospheric conditions. As far as water absorption is concerned, water absorption by weight (WAW), water absorption by volume (n, apparent porosity), and capillary water absorption (CWA) parameters are in close relation with each other. To develop the relationships between capillary water absorption and the other water absorption parameters, regression and artificial neural network (ANN) analyses were performed. Within the scope of this study, the ANN models with three different input structures were established using various input variables in order to determine the relationship between CWA and the other parameters. The results of the different ANN structures of these models have been calculated and evaluated. For this purpose, travertine samples quarried from Denizli region in Turkey were studied. In order to evaluate the model results of regression and ANN methods, statistical performance evaluation parameters, i.e., the coefficient of correlation (CORR), efficiency (E), and root mean square error (RMSE), were calculated. In both regression and ANN models, reasonable coefficients of correlation were calculated. The capillary water absorption test is time consuming. The necessary periodical measurements make the process even more impractical. It is known that capillary water absorption is directly correlated with similar water absorption parameters such as water absorption by weight and apparent porosity. In this study, the estimation of capillary water absorption by means of other water absorption parameters is proposed. Moreover, there is no proposed classification in literature for natural stones based on their capillary water absorption. In the final stage of this study, by using the relationships between porosity and capillary water absorption, a capillary water absorption classification was proposed for travertine. Proposed classification consists of five categories and capillary water absorption coefficient which is ranging between very high (>68 g/$ m^{2} $ $ s^{0.5} $) and very low (<1 g/$ m^{2} $ $ s^{0.5} $). It is thought that this study could provide significant advantages in engineering practice and also make contribution to the related literature. Capillary water absorption (dpeaa)DE-He213 Travertine (dpeaa)DE-He213 Dimension stone (dpeaa)DE-He213 Regression analysis (dpeaa)DE-He213 Artificial neural networks (dpeaa)DE-He213 Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 8(2015), 11 vom: 25. Apr., Seite 10135-10149 (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:8 year:2015 number:11 day:25 month:04 pages:10135-10149 https://dx.doi.org/10.1007/s12517-015-1902-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 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_2008 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_4242 GBV_ILN_4246 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 8 2015 11 25 04 10135-10149 |
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Çobanoğlu, İbrahim |
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Çobanoğlu, İbrahim ddc 550 misc Capillary water absorption misc Travertine misc Dimension stone misc Regression analysis misc Artificial neural networks Prediction and identification of capillary water absorption capacity of travertine dimension stone |
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550 ASE Prediction and identification of capillary water absorption capacity of travertine dimension stone Capillary water absorption (dpeaa)DE-He213 Travertine (dpeaa)DE-He213 Dimension stone (dpeaa)DE-He213 Regression analysis (dpeaa)DE-He213 Artificial neural networks (dpeaa)DE-He213 |
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ddc 550 misc Capillary water absorption misc Travertine misc Dimension stone misc Regression analysis misc Artificial neural networks |
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Prediction and identification of capillary water absorption capacity of travertine dimension stone |
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Prediction and identification of capillary water absorption capacity of travertine dimension stone |
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Çobanoğlu, İbrahim |
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Arabian journal of geosciences |
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prediction and identification of capillary water absorption capacity of travertine dimension stone |
| title_auth |
Prediction and identification of capillary water absorption capacity of travertine dimension stone |
| abstract |
Abstract The capillary transport mechanism has an important role on natural building stones that could be affected by moisture due to atmospheric conditions. Travertine with its natural porous structure is more sensitive to water which has a significant impact on durability. One of the important indicators of stone–water interaction is color variations, while natural stones with high capillary water absorption potential are in contact with water due to atmospheric conditions. As far as water absorption is concerned, water absorption by weight (WAW), water absorption by volume (n, apparent porosity), and capillary water absorption (CWA) parameters are in close relation with each other. To develop the relationships between capillary water absorption and the other water absorption parameters, regression and artificial neural network (ANN) analyses were performed. Within the scope of this study, the ANN models with three different input structures were established using various input variables in order to determine the relationship between CWA and the other parameters. The results of the different ANN structures of these models have been calculated and evaluated. For this purpose, travertine samples quarried from Denizli region in Turkey were studied. In order to evaluate the model results of regression and ANN methods, statistical performance evaluation parameters, i.e., the coefficient of correlation (CORR), efficiency (E), and root mean square error (RMSE), were calculated. In both regression and ANN models, reasonable coefficients of correlation were calculated. The capillary water absorption test is time consuming. The necessary periodical measurements make the process even more impractical. It is known that capillary water absorption is directly correlated with similar water absorption parameters such as water absorption by weight and apparent porosity. In this study, the estimation of capillary water absorption by means of other water absorption parameters is proposed. Moreover, there is no proposed classification in literature for natural stones based on their capillary water absorption. In the final stage of this study, by using the relationships between porosity and capillary water absorption, a capillary water absorption classification was proposed for travertine. Proposed classification consists of five categories and capillary water absorption coefficient which is ranging between very high (>68 g/$ m^{2} $ $ s^{0.5} $) and very low (<1 g/$ m^{2} $ $ s^{0.5} $). It is thought that this study could provide significant advantages in engineering practice and also make contribution to the related literature. |
| abstractGer |
Abstract The capillary transport mechanism has an important role on natural building stones that could be affected by moisture due to atmospheric conditions. Travertine with its natural porous structure is more sensitive to water which has a significant impact on durability. One of the important indicators of stone–water interaction is color variations, while natural stones with high capillary water absorption potential are in contact with water due to atmospheric conditions. As far as water absorption is concerned, water absorption by weight (WAW), water absorption by volume (n, apparent porosity), and capillary water absorption (CWA) parameters are in close relation with each other. To develop the relationships between capillary water absorption and the other water absorption parameters, regression and artificial neural network (ANN) analyses were performed. Within the scope of this study, the ANN models with three different input structures were established using various input variables in order to determine the relationship between CWA and the other parameters. The results of the different ANN structures of these models have been calculated and evaluated. For this purpose, travertine samples quarried from Denizli region in Turkey were studied. In order to evaluate the model results of regression and ANN methods, statistical performance evaluation parameters, i.e., the coefficient of correlation (CORR), efficiency (E), and root mean square error (RMSE), were calculated. In both regression and ANN models, reasonable coefficients of correlation were calculated. The capillary water absorption test is time consuming. The necessary periodical measurements make the process even more impractical. It is known that capillary water absorption is directly correlated with similar water absorption parameters such as water absorption by weight and apparent porosity. In this study, the estimation of capillary water absorption by means of other water absorption parameters is proposed. Moreover, there is no proposed classification in literature for natural stones based on their capillary water absorption. In the final stage of this study, by using the relationships between porosity and capillary water absorption, a capillary water absorption classification was proposed for travertine. Proposed classification consists of five categories and capillary water absorption coefficient which is ranging between very high (>68 g/$ m^{2} $ $ s^{0.5} $) and very low (<1 g/$ m^{2} $ $ s^{0.5} $). It is thought that this study could provide significant advantages in engineering practice and also make contribution to the related literature. |
| abstract_unstemmed |
Abstract The capillary transport mechanism has an important role on natural building stones that could be affected by moisture due to atmospheric conditions. Travertine with its natural porous structure is more sensitive to water which has a significant impact on durability. One of the important indicators of stone–water interaction is color variations, while natural stones with high capillary water absorption potential are in contact with water due to atmospheric conditions. As far as water absorption is concerned, water absorption by weight (WAW), water absorption by volume (n, apparent porosity), and capillary water absorption (CWA) parameters are in close relation with each other. To develop the relationships between capillary water absorption and the other water absorption parameters, regression and artificial neural network (ANN) analyses were performed. Within the scope of this study, the ANN models with three different input structures were established using various input variables in order to determine the relationship between CWA and the other parameters. The results of the different ANN structures of these models have been calculated and evaluated. For this purpose, travertine samples quarried from Denizli region in Turkey were studied. In order to evaluate the model results of regression and ANN methods, statistical performance evaluation parameters, i.e., the coefficient of correlation (CORR), efficiency (E), and root mean square error (RMSE), were calculated. In both regression and ANN models, reasonable coefficients of correlation were calculated. The capillary water absorption test is time consuming. The necessary periodical measurements make the process even more impractical. It is known that capillary water absorption is directly correlated with similar water absorption parameters such as water absorption by weight and apparent porosity. In this study, the estimation of capillary water absorption by means of other water absorption parameters is proposed. Moreover, there is no proposed classification in literature for natural stones based on their capillary water absorption. In the final stage of this study, by using the relationships between porosity and capillary water absorption, a capillary water absorption classification was proposed for travertine. Proposed classification consists of five categories and capillary water absorption coefficient which is ranging between very high (>68 g/$ m^{2} $ $ s^{0.5} $) and very low (<1 g/$ m^{2} $ $ s^{0.5} $). It is thought that this study could provide significant advantages in engineering practice and also make contribution to the related literature. |
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Prediction and identification of capillary water absorption capacity of travertine dimension stone |
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https://dx.doi.org/10.1007/s12517-015-1902-8 |
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10.1007/s12517-015-1902-8 |
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|
| score |
7.400079 |