Investigation of mechanical properties of mortar with mixed recycled aggregates
Abstract In this study two phases were considered. In the first phase, demolition of building materials such as concrete, brick, ceramic and tile, and stone were crushed and 30–40% stone, 10–20% ceramic, 5–15% brick, and 50–60% concrete waste were used to produce recycled sand. The initial propertie...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Heidari, Ali [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer International Publishing AG, part of Springer Nature 2018 |
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| Übergeordnetes Werk: |
Enthalten in: Asian journal of civil engineering - Cham : Springer International Publishing, 2017, 19(2018), 5 vom: 12. Apr., Seite 583-593 |
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| Übergeordnetes Werk: |
volume:19 ; year:2018 ; number:5 ; day:12 ; month:04 ; pages:583-593 |
| Links: |
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| DOI / URN: |
10.1007/s42107-018-0044-1 |
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| Katalog-ID: |
SPR038421933 |
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| 520 | |a Abstract In this study two phases were considered. In the first phase, demolition of building materials such as concrete, brick, ceramic and tile, and stone were crushed and 30–40% stone, 10–20% ceramic, 5–15% brick, and 50–60% concrete waste were used to produce recycled sand. The initial properties of aggregates such as chemical properties, water absorption, moisture content, density, and abrasion were investigated. In the second phase, the mechanical properties of mortar with substitute of natural aggregates with recycled aggregates from 0 to 100% were examined. In addition, the sand to cement ratio was varied from 2, 2.5, and 3. The results showed that the compressive strength in sand to cement ratio of 3 is higher than other ratios. Water absorption test showed that the use of recycled sand increases the water absorption in all ratios of sand to cement. The flexural strength test showed that the use of 0–40% recycled sand in sand to cement ratio of 2.5 is higher than other specimens, but by increasing the waste to between 50 and 70%, specimens with sand to cement ratio of 3 have higher result. Increasing the recycled sand to 100%, produced higher results for sand to cement of 2. Additionally, backpropagation neural network was used to estimate the mechanical properties of mortar. With regression of up to 70% and mean standard error lower than 0.3, this network has good precision to predict the mortar properties. | ||
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| 700 | 1 | |a Hashempour, Masoumeh |4 aut | |
| 700 | 1 | |a Javdanian, Hamed |4 aut | |
| 700 | 1 | |a Karimian, Mehdi |4 aut | |
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10.1007/s42107-018-0044-1 doi (DE-627)SPR038421933 (SPR)s42107-018-0044-1-e DE-627 ger DE-627 rakwb eng Heidari, Ali verfasserin aut Investigation of mechanical properties of mortar with mixed recycled aggregates 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer International Publishing AG, part of Springer Nature 2018 Abstract In this study two phases were considered. In the first phase, demolition of building materials such as concrete, brick, ceramic and tile, and stone were crushed and 30–40% stone, 10–20% ceramic, 5–15% brick, and 50–60% concrete waste were used to produce recycled sand. The initial properties of aggregates such as chemical properties, water absorption, moisture content, density, and abrasion were investigated. In the second phase, the mechanical properties of mortar with substitute of natural aggregates with recycled aggregates from 0 to 100% were examined. In addition, the sand to cement ratio was varied from 2, 2.5, and 3. The results showed that the compressive strength in sand to cement ratio of 3 is higher than other ratios. Water absorption test showed that the use of recycled sand increases the water absorption in all ratios of sand to cement. The flexural strength test showed that the use of 0–40% recycled sand in sand to cement ratio of 2.5 is higher than other specimens, but by increasing the waste to between 50 and 70%, specimens with sand to cement ratio of 3 have higher result. Increasing the recycled sand to 100%, produced higher results for sand to cement of 2. Additionally, backpropagation neural network was used to estimate the mechanical properties of mortar. With regression of up to 70% and mean standard error lower than 0.3, this network has good precision to predict the mortar properties. Waste concrete (dpeaa)DE-He213 Backpropagation neural network (dpeaa)DE-He213 Recycled aggregate (dpeaa)DE-He213 Mechanical properties (dpeaa)DE-He213 Hashempour, Masoumeh aut Javdanian, Hamed aut Karimian, Mehdi aut Enthalten in Asian journal of civil engineering Cham : Springer International Publishing, 2017 19(2018), 5 vom: 12. Apr., Seite 583-593 (DE-627)101384565X (DE-600)2919928-1 2522-011X nnns volume:19 year:2018 number:5 day:12 month:04 pages:583-593 https://dx.doi.org/10.1007/s42107-018-0044-1 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_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_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_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_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 19 2018 5 12 04 583-593 |
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10.1007/s42107-018-0044-1 doi (DE-627)SPR038421933 (SPR)s42107-018-0044-1-e DE-627 ger DE-627 rakwb eng Heidari, Ali verfasserin aut Investigation of mechanical properties of mortar with mixed recycled aggregates 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer International Publishing AG, part of Springer Nature 2018 Abstract In this study two phases were considered. In the first phase, demolition of building materials such as concrete, brick, ceramic and tile, and stone were crushed and 30–40% stone, 10–20% ceramic, 5–15% brick, and 50–60% concrete waste were used to produce recycled sand. The initial properties of aggregates such as chemical properties, water absorption, moisture content, density, and abrasion were investigated. In the second phase, the mechanical properties of mortar with substitute of natural aggregates with recycled aggregates from 0 to 100% were examined. In addition, the sand to cement ratio was varied from 2, 2.5, and 3. The results showed that the compressive strength in sand to cement ratio of 3 is higher than other ratios. Water absorption test showed that the use of recycled sand increases the water absorption in all ratios of sand to cement. The flexural strength test showed that the use of 0–40% recycled sand in sand to cement ratio of 2.5 is higher than other specimens, but by increasing the waste to between 50 and 70%, specimens with sand to cement ratio of 3 have higher result. Increasing the recycled sand to 100%, produced higher results for sand to cement of 2. Additionally, backpropagation neural network was used to estimate the mechanical properties of mortar. With regression of up to 70% and mean standard error lower than 0.3, this network has good precision to predict the mortar properties. Waste concrete (dpeaa)DE-He213 Backpropagation neural network (dpeaa)DE-He213 Recycled aggregate (dpeaa)DE-He213 Mechanical properties (dpeaa)DE-He213 Hashempour, Masoumeh aut Javdanian, Hamed aut Karimian, Mehdi aut Enthalten in Asian journal of civil engineering Cham : Springer International Publishing, 2017 19(2018), 5 vom: 12. Apr., Seite 583-593 (DE-627)101384565X (DE-600)2919928-1 2522-011X nnns volume:19 year:2018 number:5 day:12 month:04 pages:583-593 https://dx.doi.org/10.1007/s42107-018-0044-1 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_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_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_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_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 19 2018 5 12 04 583-593 |
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10.1007/s42107-018-0044-1 doi (DE-627)SPR038421933 (SPR)s42107-018-0044-1-e DE-627 ger DE-627 rakwb eng Heidari, Ali verfasserin aut Investigation of mechanical properties of mortar with mixed recycled aggregates 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer International Publishing AG, part of Springer Nature 2018 Abstract In this study two phases were considered. In the first phase, demolition of building materials such as concrete, brick, ceramic and tile, and stone were crushed and 30–40% stone, 10–20% ceramic, 5–15% brick, and 50–60% concrete waste were used to produce recycled sand. The initial properties of aggregates such as chemical properties, water absorption, moisture content, density, and abrasion were investigated. In the second phase, the mechanical properties of mortar with substitute of natural aggregates with recycled aggregates from 0 to 100% were examined. In addition, the sand to cement ratio was varied from 2, 2.5, and 3. The results showed that the compressive strength in sand to cement ratio of 3 is higher than other ratios. Water absorption test showed that the use of recycled sand increases the water absorption in all ratios of sand to cement. The flexural strength test showed that the use of 0–40% recycled sand in sand to cement ratio of 2.5 is higher than other specimens, but by increasing the waste to between 50 and 70%, specimens with sand to cement ratio of 3 have higher result. Increasing the recycled sand to 100%, produced higher results for sand to cement of 2. Additionally, backpropagation neural network was used to estimate the mechanical properties of mortar. With regression of up to 70% and mean standard error lower than 0.3, this network has good precision to predict the mortar properties. Waste concrete (dpeaa)DE-He213 Backpropagation neural network (dpeaa)DE-He213 Recycled aggregate (dpeaa)DE-He213 Mechanical properties (dpeaa)DE-He213 Hashempour, Masoumeh aut Javdanian, Hamed aut Karimian, Mehdi aut Enthalten in Asian journal of civil engineering Cham : Springer International Publishing, 2017 19(2018), 5 vom: 12. Apr., Seite 583-593 (DE-627)101384565X (DE-600)2919928-1 2522-011X nnns volume:19 year:2018 number:5 day:12 month:04 pages:583-593 https://dx.doi.org/10.1007/s42107-018-0044-1 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_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_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_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_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 19 2018 5 12 04 583-593 |
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10.1007/s42107-018-0044-1 doi (DE-627)SPR038421933 (SPR)s42107-018-0044-1-e DE-627 ger DE-627 rakwb eng Heidari, Ali verfasserin aut Investigation of mechanical properties of mortar with mixed recycled aggregates 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer International Publishing AG, part of Springer Nature 2018 Abstract In this study two phases were considered. In the first phase, demolition of building materials such as concrete, brick, ceramic and tile, and stone were crushed and 30–40% stone, 10–20% ceramic, 5–15% brick, and 50–60% concrete waste were used to produce recycled sand. The initial properties of aggregates such as chemical properties, water absorption, moisture content, density, and abrasion were investigated. In the second phase, the mechanical properties of mortar with substitute of natural aggregates with recycled aggregates from 0 to 100% were examined. In addition, the sand to cement ratio was varied from 2, 2.5, and 3. The results showed that the compressive strength in sand to cement ratio of 3 is higher than other ratios. Water absorption test showed that the use of recycled sand increases the water absorption in all ratios of sand to cement. The flexural strength test showed that the use of 0–40% recycled sand in sand to cement ratio of 2.5 is higher than other specimens, but by increasing the waste to between 50 and 70%, specimens with sand to cement ratio of 3 have higher result. Increasing the recycled sand to 100%, produced higher results for sand to cement of 2. Additionally, backpropagation neural network was used to estimate the mechanical properties of mortar. With regression of up to 70% and mean standard error lower than 0.3, this network has good precision to predict the mortar properties. Waste concrete (dpeaa)DE-He213 Backpropagation neural network (dpeaa)DE-He213 Recycled aggregate (dpeaa)DE-He213 Mechanical properties (dpeaa)DE-He213 Hashempour, Masoumeh aut Javdanian, Hamed aut Karimian, Mehdi aut Enthalten in Asian journal of civil engineering Cham : Springer International Publishing, 2017 19(2018), 5 vom: 12. Apr., Seite 583-593 (DE-627)101384565X (DE-600)2919928-1 2522-011X nnns volume:19 year:2018 number:5 day:12 month:04 pages:583-593 https://dx.doi.org/10.1007/s42107-018-0044-1 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_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_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_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_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 19 2018 5 12 04 583-593 |
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10.1007/s42107-018-0044-1 doi (DE-627)SPR038421933 (SPR)s42107-018-0044-1-e DE-627 ger DE-627 rakwb eng Heidari, Ali verfasserin aut Investigation of mechanical properties of mortar with mixed recycled aggregates 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer International Publishing AG, part of Springer Nature 2018 Abstract In this study two phases were considered. In the first phase, demolition of building materials such as concrete, brick, ceramic and tile, and stone were crushed and 30–40% stone, 10–20% ceramic, 5–15% brick, and 50–60% concrete waste were used to produce recycled sand. The initial properties of aggregates such as chemical properties, water absorption, moisture content, density, and abrasion were investigated. In the second phase, the mechanical properties of mortar with substitute of natural aggregates with recycled aggregates from 0 to 100% were examined. In addition, the sand to cement ratio was varied from 2, 2.5, and 3. The results showed that the compressive strength in sand to cement ratio of 3 is higher than other ratios. Water absorption test showed that the use of recycled sand increases the water absorption in all ratios of sand to cement. The flexural strength test showed that the use of 0–40% recycled sand in sand to cement ratio of 2.5 is higher than other specimens, but by increasing the waste to between 50 and 70%, specimens with sand to cement ratio of 3 have higher result. Increasing the recycled sand to 100%, produced higher results for sand to cement of 2. Additionally, backpropagation neural network was used to estimate the mechanical properties of mortar. With regression of up to 70% and mean standard error lower than 0.3, this network has good precision to predict the mortar properties. Waste concrete (dpeaa)DE-He213 Backpropagation neural network (dpeaa)DE-He213 Recycled aggregate (dpeaa)DE-He213 Mechanical properties (dpeaa)DE-He213 Hashempour, Masoumeh aut Javdanian, Hamed aut Karimian, Mehdi aut Enthalten in Asian journal of civil engineering Cham : Springer International Publishing, 2017 19(2018), 5 vom: 12. Apr., Seite 583-593 (DE-627)101384565X (DE-600)2919928-1 2522-011X nnns volume:19 year:2018 number:5 day:12 month:04 pages:583-593 https://dx.doi.org/10.1007/s42107-018-0044-1 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_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_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_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_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 19 2018 5 12 04 583-593 |
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Heidari, Ali @@aut@@ Hashempour, Masoumeh @@aut@@ Javdanian, Hamed @@aut@@ Karimian, Mehdi @@aut@@ |
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Heidari, Ali |
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Investigation of mechanical properties of mortar with mixed recycled aggregates Waste concrete (dpeaa)DE-He213 Backpropagation neural network (dpeaa)DE-He213 Recycled aggregate (dpeaa)DE-He213 Mechanical properties (dpeaa)DE-He213 |
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Investigation of mechanical properties of mortar with mixed recycled aggregates |
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investigation of mechanical properties of mortar with mixed recycled aggregates |
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Investigation of mechanical properties of mortar with mixed recycled aggregates |
| abstract |
Abstract In this study two phases were considered. In the first phase, demolition of building materials such as concrete, brick, ceramic and tile, and stone were crushed and 30–40% stone, 10–20% ceramic, 5–15% brick, and 50–60% concrete waste were used to produce recycled sand. The initial properties of aggregates such as chemical properties, water absorption, moisture content, density, and abrasion were investigated. In the second phase, the mechanical properties of mortar with substitute of natural aggregates with recycled aggregates from 0 to 100% were examined. In addition, the sand to cement ratio was varied from 2, 2.5, and 3. The results showed that the compressive strength in sand to cement ratio of 3 is higher than other ratios. Water absorption test showed that the use of recycled sand increases the water absorption in all ratios of sand to cement. The flexural strength test showed that the use of 0–40% recycled sand in sand to cement ratio of 2.5 is higher than other specimens, but by increasing the waste to between 50 and 70%, specimens with sand to cement ratio of 3 have higher result. Increasing the recycled sand to 100%, produced higher results for sand to cement of 2. Additionally, backpropagation neural network was used to estimate the mechanical properties of mortar. With regression of up to 70% and mean standard error lower than 0.3, this network has good precision to predict the mortar properties. © Springer International Publishing AG, part of Springer Nature 2018 |
| abstractGer |
Abstract In this study two phases were considered. In the first phase, demolition of building materials such as concrete, brick, ceramic and tile, and stone were crushed and 30–40% stone, 10–20% ceramic, 5–15% brick, and 50–60% concrete waste were used to produce recycled sand. The initial properties of aggregates such as chemical properties, water absorption, moisture content, density, and abrasion were investigated. In the second phase, the mechanical properties of mortar with substitute of natural aggregates with recycled aggregates from 0 to 100% were examined. In addition, the sand to cement ratio was varied from 2, 2.5, and 3. The results showed that the compressive strength in sand to cement ratio of 3 is higher than other ratios. Water absorption test showed that the use of recycled sand increases the water absorption in all ratios of sand to cement. The flexural strength test showed that the use of 0–40% recycled sand in sand to cement ratio of 2.5 is higher than other specimens, but by increasing the waste to between 50 and 70%, specimens with sand to cement ratio of 3 have higher result. Increasing the recycled sand to 100%, produced higher results for sand to cement of 2. Additionally, backpropagation neural network was used to estimate the mechanical properties of mortar. With regression of up to 70% and mean standard error lower than 0.3, this network has good precision to predict the mortar properties. © Springer International Publishing AG, part of Springer Nature 2018 |
| abstract_unstemmed |
Abstract In this study two phases were considered. In the first phase, demolition of building materials such as concrete, brick, ceramic and tile, and stone were crushed and 30–40% stone, 10–20% ceramic, 5–15% brick, and 50–60% concrete waste were used to produce recycled sand. The initial properties of aggregates such as chemical properties, water absorption, moisture content, density, and abrasion were investigated. In the second phase, the mechanical properties of mortar with substitute of natural aggregates with recycled aggregates from 0 to 100% were examined. In addition, the sand to cement ratio was varied from 2, 2.5, and 3. The results showed that the compressive strength in sand to cement ratio of 3 is higher than other ratios. Water absorption test showed that the use of recycled sand increases the water absorption in all ratios of sand to cement. The flexural strength test showed that the use of 0–40% recycled sand in sand to cement ratio of 2.5 is higher than other specimens, but by increasing the waste to between 50 and 70%, specimens with sand to cement ratio of 3 have higher result. Increasing the recycled sand to 100%, produced higher results for sand to cement of 2. Additionally, backpropagation neural network was used to estimate the mechanical properties of mortar. With regression of up to 70% and mean standard error lower than 0.3, this network has good precision to predict the mortar properties. © Springer International Publishing AG, part of Springer Nature 2018 |
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Investigation of mechanical properties of mortar with mixed recycled aggregates |
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Hashempour, Masoumeh Javdanian, Hamed Karimian, Mehdi |
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10.1007/s42107-018-0044-1 |
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2024-07-03T18:00:38.974Z |
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