Mechanical Properties and Neural Network Prediction of Cement Fly Ash-Enhanced Roadbed Soil in Seasonal Frozen Zones Under Short-Term Curing
Abstract The freezing and thawing of roadbed soils in seasonal frozen zones can cause uneven settlement and other road problems, which puts road operation at risk. This paper focuses on the rapid construction of expressways and analyzes the effects of using fly ash and cement as modifiers on the phy...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Cui, Gaohang [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Anmerkung: |
© Korean Society of Civil Engineers 2024 |
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| Übergeordnetes Werk: |
Enthalten in: KSCE journal of civil engineering - Seoul : Korean Soc. of Civil Engineers, 1997, 28(2023), 1 vom: 11. Nov., Seite 106-123 |
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| Übergeordnetes Werk: |
volume:28 ; year:2023 ; number:1 ; day:11 ; month:11 ; pages:106-123 |
| Links: |
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| DOI / URN: |
10.1007/s12205-023-2077-6 |
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| Katalog-ID: |
SPR054058376 |
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| 100 | 1 | |a Cui, Gaohang |e verfasserin |0 (orcid)0000-0002-0003-6287 |4 aut | |
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| 520 | |a Abstract The freezing and thawing of roadbed soils in seasonal frozen zones can cause uneven settlement and other road problems, which puts road operation at risk. This paper focuses on the rapid construction of expressways and analyzes the effects of using fly ash and cement as modifiers on the physical properties and chemical composition of subgrade soil. The study found that cement admixtures can improve soil mechanical attributes and frost resistance, while also increasing the degree of fly ash hydration. And the freeze-thaw cycle process can enhance the mechanical characteristics of soils with higher cement admixture content. This is due to the development of hydrates in cement fly ash-enhanced soils during freeze?thaw cycles, resulting in a denser interior structure. The use of neural network prediction analysis showed that using ANN4-10-3 to forecast soil mechanical property parameters can produce superior results. Therefore, it is recommended to use cement fly ash-enhanced soil for roadbed construction in seasonal freezing areas, and neural network can be used to predict soil mechanical parameters. | ||
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| 650 | 4 | |a Fast construction |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Cement fly ash- enhanced soil |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Mechanical properties |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Neural network prediction |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Cheng, Zhuo |0 (orcid)0000-0003-1636-7555 |4 aut | |
| 700 | 1 | |a Sun, Fanyue |4 aut | |
| 700 | 1 | |a Ma, Shuxian |4 aut | |
| 700 | 1 | |a Liu, Zhiqiang |4 aut | |
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10.1007/s12205-023-2077-6 doi (DE-627)SPR054058376 (SPR)s12205-023-2077-6-e DE-627 ger DE-627 rakwb eng Cui, Gaohang verfasserin (orcid)0000-0002-0003-6287 aut Mechanical Properties and Neural Network Prediction of Cement Fly Ash-Enhanced Roadbed Soil in Seasonal Frozen Zones Under Short-Term Curing 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Korean Society of Civil Engineers 2024 Abstract The freezing and thawing of roadbed soils in seasonal frozen zones can cause uneven settlement and other road problems, which puts road operation at risk. This paper focuses on the rapid construction of expressways and analyzes the effects of using fly ash and cement as modifiers on the physical properties and chemical composition of subgrade soil. The study found that cement admixtures can improve soil mechanical attributes and frost resistance, while also increasing the degree of fly ash hydration. And the freeze-thaw cycle process can enhance the mechanical characteristics of soils with higher cement admixture content. This is due to the development of hydrates in cement fly ash-enhanced soils during freeze?thaw cycles, resulting in a denser interior structure. The use of neural network prediction analysis showed that using ANN4-10-3 to forecast soil mechanical property parameters can produce superior results. Therefore, it is recommended to use cement fly ash-enhanced soil for roadbed construction in seasonal freezing areas, and neural network can be used to predict soil mechanical parameters. Seasonal frozen area (dpeaa)DE-He213 Fast construction (dpeaa)DE-He213 Cement fly ash- enhanced soil (dpeaa)DE-He213 Mechanical properties (dpeaa)DE-He213 Neural network prediction (dpeaa)DE-He213 Cheng, Zhuo (orcid)0000-0003-1636-7555 aut Sun, Fanyue aut Ma, Shuxian aut Liu, Zhiqiang aut Enthalten in KSCE journal of civil engineering Seoul : Korean Soc. of Civil Engineers, 1997 28(2023), 1 vom: 11. Nov., Seite 106-123 (DE-627)57517238X (DE-600)2446036-9 1976-3808 nnns volume:28 year:2023 number:1 day:11 month:11 pages:106-123 https://dx.doi.org/10.1007/s12205-023-2077-6 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_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_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_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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 28 2023 1 11 11 106-123 |
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10.1007/s12205-023-2077-6 doi (DE-627)SPR054058376 (SPR)s12205-023-2077-6-e DE-627 ger DE-627 rakwb eng Cui, Gaohang verfasserin (orcid)0000-0002-0003-6287 aut Mechanical Properties and Neural Network Prediction of Cement Fly Ash-Enhanced Roadbed Soil in Seasonal Frozen Zones Under Short-Term Curing 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Korean Society of Civil Engineers 2024 Abstract The freezing and thawing of roadbed soils in seasonal frozen zones can cause uneven settlement and other road problems, which puts road operation at risk. This paper focuses on the rapid construction of expressways and analyzes the effects of using fly ash and cement as modifiers on the physical properties and chemical composition of subgrade soil. The study found that cement admixtures can improve soil mechanical attributes and frost resistance, while also increasing the degree of fly ash hydration. And the freeze-thaw cycle process can enhance the mechanical characteristics of soils with higher cement admixture content. This is due to the development of hydrates in cement fly ash-enhanced soils during freeze?thaw cycles, resulting in a denser interior structure. The use of neural network prediction analysis showed that using ANN4-10-3 to forecast soil mechanical property parameters can produce superior results. Therefore, it is recommended to use cement fly ash-enhanced soil for roadbed construction in seasonal freezing areas, and neural network can be used to predict soil mechanical parameters. Seasonal frozen area (dpeaa)DE-He213 Fast construction (dpeaa)DE-He213 Cement fly ash- enhanced soil (dpeaa)DE-He213 Mechanical properties (dpeaa)DE-He213 Neural network prediction (dpeaa)DE-He213 Cheng, Zhuo (orcid)0000-0003-1636-7555 aut Sun, Fanyue aut Ma, Shuxian aut Liu, Zhiqiang aut Enthalten in KSCE journal of civil engineering Seoul : Korean Soc. of Civil Engineers, 1997 28(2023), 1 vom: 11. Nov., Seite 106-123 (DE-627)57517238X (DE-600)2446036-9 1976-3808 nnns volume:28 year:2023 number:1 day:11 month:11 pages:106-123 https://dx.doi.org/10.1007/s12205-023-2077-6 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_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_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_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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 28 2023 1 11 11 106-123 |
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10.1007/s12205-023-2077-6 doi (DE-627)SPR054058376 (SPR)s12205-023-2077-6-e DE-627 ger DE-627 rakwb eng Cui, Gaohang verfasserin (orcid)0000-0002-0003-6287 aut Mechanical Properties and Neural Network Prediction of Cement Fly Ash-Enhanced Roadbed Soil in Seasonal Frozen Zones Under Short-Term Curing 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Korean Society of Civil Engineers 2024 Abstract The freezing and thawing of roadbed soils in seasonal frozen zones can cause uneven settlement and other road problems, which puts road operation at risk. This paper focuses on the rapid construction of expressways and analyzes the effects of using fly ash and cement as modifiers on the physical properties and chemical composition of subgrade soil. The study found that cement admixtures can improve soil mechanical attributes and frost resistance, while also increasing the degree of fly ash hydration. And the freeze-thaw cycle process can enhance the mechanical characteristics of soils with higher cement admixture content. This is due to the development of hydrates in cement fly ash-enhanced soils during freeze?thaw cycles, resulting in a denser interior structure. The use of neural network prediction analysis showed that using ANN4-10-3 to forecast soil mechanical property parameters can produce superior results. Therefore, it is recommended to use cement fly ash-enhanced soil for roadbed construction in seasonal freezing areas, and neural network can be used to predict soil mechanical parameters. Seasonal frozen area (dpeaa)DE-He213 Fast construction (dpeaa)DE-He213 Cement fly ash- enhanced soil (dpeaa)DE-He213 Mechanical properties (dpeaa)DE-He213 Neural network prediction (dpeaa)DE-He213 Cheng, Zhuo (orcid)0000-0003-1636-7555 aut Sun, Fanyue aut Ma, Shuxian aut Liu, Zhiqiang aut Enthalten in KSCE journal of civil engineering Seoul : Korean Soc. of Civil Engineers, 1997 28(2023), 1 vom: 11. Nov., Seite 106-123 (DE-627)57517238X (DE-600)2446036-9 1976-3808 nnns volume:28 year:2023 number:1 day:11 month:11 pages:106-123 https://dx.doi.org/10.1007/s12205-023-2077-6 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_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_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_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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 28 2023 1 11 11 106-123 |
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10.1007/s12205-023-2077-6 doi (DE-627)SPR054058376 (SPR)s12205-023-2077-6-e DE-627 ger DE-627 rakwb eng Cui, Gaohang verfasserin (orcid)0000-0002-0003-6287 aut Mechanical Properties and Neural Network Prediction of Cement Fly Ash-Enhanced Roadbed Soil in Seasonal Frozen Zones Under Short-Term Curing 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Korean Society of Civil Engineers 2024 Abstract The freezing and thawing of roadbed soils in seasonal frozen zones can cause uneven settlement and other road problems, which puts road operation at risk. This paper focuses on the rapid construction of expressways and analyzes the effects of using fly ash and cement as modifiers on the physical properties and chemical composition of subgrade soil. The study found that cement admixtures can improve soil mechanical attributes and frost resistance, while also increasing the degree of fly ash hydration. And the freeze-thaw cycle process can enhance the mechanical characteristics of soils with higher cement admixture content. This is due to the development of hydrates in cement fly ash-enhanced soils during freeze?thaw cycles, resulting in a denser interior structure. The use of neural network prediction analysis showed that using ANN4-10-3 to forecast soil mechanical property parameters can produce superior results. Therefore, it is recommended to use cement fly ash-enhanced soil for roadbed construction in seasonal freezing areas, and neural network can be used to predict soil mechanical parameters. Seasonal frozen area (dpeaa)DE-He213 Fast construction (dpeaa)DE-He213 Cement fly ash- enhanced soil (dpeaa)DE-He213 Mechanical properties (dpeaa)DE-He213 Neural network prediction (dpeaa)DE-He213 Cheng, Zhuo (orcid)0000-0003-1636-7555 aut Sun, Fanyue aut Ma, Shuxian aut Liu, Zhiqiang aut Enthalten in KSCE journal of civil engineering Seoul : Korean Soc. of Civil Engineers, 1997 28(2023), 1 vom: 11. Nov., Seite 106-123 (DE-627)57517238X (DE-600)2446036-9 1976-3808 nnns volume:28 year:2023 number:1 day:11 month:11 pages:106-123 https://dx.doi.org/10.1007/s12205-023-2077-6 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_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_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_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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 28 2023 1 11 11 106-123 |
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10.1007/s12205-023-2077-6 doi (DE-627)SPR054058376 (SPR)s12205-023-2077-6-e DE-627 ger DE-627 rakwb eng Cui, Gaohang verfasserin (orcid)0000-0002-0003-6287 aut Mechanical Properties and Neural Network Prediction of Cement Fly Ash-Enhanced Roadbed Soil in Seasonal Frozen Zones Under Short-Term Curing 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Korean Society of Civil Engineers 2024 Abstract The freezing and thawing of roadbed soils in seasonal frozen zones can cause uneven settlement and other road problems, which puts road operation at risk. This paper focuses on the rapid construction of expressways and analyzes the effects of using fly ash and cement as modifiers on the physical properties and chemical composition of subgrade soil. The study found that cement admixtures can improve soil mechanical attributes and frost resistance, while also increasing the degree of fly ash hydration. And the freeze-thaw cycle process can enhance the mechanical characteristics of soils with higher cement admixture content. This is due to the development of hydrates in cement fly ash-enhanced soils during freeze?thaw cycles, resulting in a denser interior structure. The use of neural network prediction analysis showed that using ANN4-10-3 to forecast soil mechanical property parameters can produce superior results. Therefore, it is recommended to use cement fly ash-enhanced soil for roadbed construction in seasonal freezing areas, and neural network can be used to predict soil mechanical parameters. Seasonal frozen area (dpeaa)DE-He213 Fast construction (dpeaa)DE-He213 Cement fly ash- enhanced soil (dpeaa)DE-He213 Mechanical properties (dpeaa)DE-He213 Neural network prediction (dpeaa)DE-He213 Cheng, Zhuo (orcid)0000-0003-1636-7555 aut Sun, Fanyue aut Ma, Shuxian aut Liu, Zhiqiang aut Enthalten in KSCE journal of civil engineering Seoul : Korean Soc. of Civil Engineers, 1997 28(2023), 1 vom: 11. Nov., Seite 106-123 (DE-627)57517238X (DE-600)2446036-9 1976-3808 nnns volume:28 year:2023 number:1 day:11 month:11 pages:106-123 https://dx.doi.org/10.1007/s12205-023-2077-6 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_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_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_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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 28 2023 1 11 11 106-123 |
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Cui, Gaohang @@aut@@ Cheng, Zhuo @@aut@@ Sun, Fanyue @@aut@@ Ma, Shuxian @@aut@@ Liu, Zhiqiang @@aut@@ |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR054058376</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20231213064640.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">231213s2023 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s12205-023-2077-6</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR054058376</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s12205-023-2077-6-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Cui, Gaohang</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-0003-6287</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Mechanical Properties and Neural Network Prediction of Cement Fly Ash-Enhanced Roadbed Soil in Seasonal Frozen Zones Under Short-Term Curing</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Korean Society of Civil Engineers 2024</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The freezing and thawing of roadbed soils in seasonal frozen zones can cause uneven settlement and other road problems, which puts road operation at risk. This paper focuses on the rapid construction of expressways and analyzes the effects of using fly ash and cement as modifiers on the physical properties and chemical composition of subgrade soil. The study found that cement admixtures can improve soil mechanical attributes and frost resistance, while also increasing the degree of fly ash hydration. And the freeze-thaw cycle process can enhance the mechanical characteristics of soils with higher cement admixture content. This is due to the development of hydrates in cement fly ash-enhanced soils during freeze?thaw cycles, resulting in a denser interior structure. The use of neural network prediction analysis showed that using ANN4-10-3 to forecast soil mechanical property parameters can produce superior results. 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Cui, Gaohang |
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Cui, Gaohang misc Seasonal frozen area misc Fast construction misc Cement fly ash- enhanced soil misc Mechanical properties misc Neural network prediction Mechanical Properties and Neural Network Prediction of Cement Fly Ash-Enhanced Roadbed Soil in Seasonal Frozen Zones Under Short-Term Curing |
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Mechanical Properties and Neural Network Prediction of Cement Fly Ash-Enhanced Roadbed Soil in Seasonal Frozen Zones Under Short-Term Curing Seasonal frozen area (dpeaa)DE-He213 Fast construction (dpeaa)DE-He213 Cement fly ash- enhanced soil (dpeaa)DE-He213 Mechanical properties (dpeaa)DE-He213 Neural network prediction (dpeaa)DE-He213 |
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misc Seasonal frozen area misc Fast construction misc Cement fly ash- enhanced soil misc Mechanical properties misc Neural network prediction |
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Mechanical Properties and Neural Network Prediction of Cement Fly Ash-Enhanced Roadbed Soil in Seasonal Frozen Zones Under Short-Term Curing |
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Mechanical Properties and Neural Network Prediction of Cement Fly Ash-Enhanced Roadbed Soil in Seasonal Frozen Zones Under Short-Term Curing |
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Cui, Gaohang |
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Cui, Gaohang Cheng, Zhuo Sun, Fanyue Ma, Shuxian Liu, Zhiqiang |
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mechanical properties and neural network prediction of cement fly ash-enhanced roadbed soil in seasonal frozen zones under short-term curing |
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Mechanical Properties and Neural Network Prediction of Cement Fly Ash-Enhanced Roadbed Soil in Seasonal Frozen Zones Under Short-Term Curing |
| abstract |
Abstract The freezing and thawing of roadbed soils in seasonal frozen zones can cause uneven settlement and other road problems, which puts road operation at risk. This paper focuses on the rapid construction of expressways and analyzes the effects of using fly ash and cement as modifiers on the physical properties and chemical composition of subgrade soil. The study found that cement admixtures can improve soil mechanical attributes and frost resistance, while also increasing the degree of fly ash hydration. And the freeze-thaw cycle process can enhance the mechanical characteristics of soils with higher cement admixture content. This is due to the development of hydrates in cement fly ash-enhanced soils during freeze?thaw cycles, resulting in a denser interior structure. The use of neural network prediction analysis showed that using ANN4-10-3 to forecast soil mechanical property parameters can produce superior results. Therefore, it is recommended to use cement fly ash-enhanced soil for roadbed construction in seasonal freezing areas, and neural network can be used to predict soil mechanical parameters. © Korean Society of Civil Engineers 2024 |
| abstractGer |
Abstract The freezing and thawing of roadbed soils in seasonal frozen zones can cause uneven settlement and other road problems, which puts road operation at risk. This paper focuses on the rapid construction of expressways and analyzes the effects of using fly ash and cement as modifiers on the physical properties and chemical composition of subgrade soil. The study found that cement admixtures can improve soil mechanical attributes and frost resistance, while also increasing the degree of fly ash hydration. And the freeze-thaw cycle process can enhance the mechanical characteristics of soils with higher cement admixture content. This is due to the development of hydrates in cement fly ash-enhanced soils during freeze?thaw cycles, resulting in a denser interior structure. The use of neural network prediction analysis showed that using ANN4-10-3 to forecast soil mechanical property parameters can produce superior results. Therefore, it is recommended to use cement fly ash-enhanced soil for roadbed construction in seasonal freezing areas, and neural network can be used to predict soil mechanical parameters. © Korean Society of Civil Engineers 2024 |
| abstract_unstemmed |
Abstract The freezing and thawing of roadbed soils in seasonal frozen zones can cause uneven settlement and other road problems, which puts road operation at risk. This paper focuses on the rapid construction of expressways and analyzes the effects of using fly ash and cement as modifiers on the physical properties and chemical composition of subgrade soil. The study found that cement admixtures can improve soil mechanical attributes and frost resistance, while also increasing the degree of fly ash hydration. And the freeze-thaw cycle process can enhance the mechanical characteristics of soils with higher cement admixture content. This is due to the development of hydrates in cement fly ash-enhanced soils during freeze?thaw cycles, resulting in a denser interior structure. The use of neural network prediction analysis showed that using ANN4-10-3 to forecast soil mechanical property parameters can produce superior results. Therefore, it is recommended to use cement fly ash-enhanced soil for roadbed construction in seasonal freezing areas, and neural network can be used to predict soil mechanical parameters. © Korean Society of Civil Engineers 2024 |
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Mechanical Properties and Neural Network Prediction of Cement Fly Ash-Enhanced Roadbed Soil in Seasonal Frozen Zones Under Short-Term Curing |
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https://dx.doi.org/10.1007/s12205-023-2077-6 |
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Cheng, Zhuo Sun, Fanyue Ma, Shuxian Liu, Zhiqiang |
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10.1007/s12205-023-2077-6 |
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2024-07-03T23:43:15.027Z |
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| score |
7.3980665 |