An efficient model for predicting setting time of cement based on broad learning system

Cement is the main building material in the construction industry. Its setting time directly affects the setting time and strength of concrete, which further affects construction schedule and building quality. However, traditional measurement technology not only has high labor intensity and high tim...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Guo, Jifeng [verfasserIn] Wang, Lin [verfasserIn] Fan, Kaipeng [verfasserIn] Yang, Bo [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Broad learning system Initial setting time Final setting time Clinker composition Physical properties

Übergeordnetes Werk:	Enthalten in: Applied soft computing - Amsterdam [u.a.] : Elsevier Science, 2001, 96
Übergeordnetes Werk:	volume:96

DOI / URN:	10.1016/j.asoc.2020.106698

Katalog-ID:	ELV004901665

Internformat


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520			\|a Cement is the main building material in the construction industry. Its setting time directly affects the setting time and strength of concrete, which further affects construction schedule and building quality. However, traditional measurement technology not only has high labor intensity and high time consumption, but has a high technical requirement. Various human factors, such as insufficient operation, will result in great errors in the measurements. The accurate prediction of setting time enables manpower savings, avoids large errors caused by insufficient operation, and guides the production of high-performance cement. In this paper, an efficient model based on the broad learning system is proposed to predict the initial and final setting time. It is committed to directly predicting setting time from clinker composition and physical properties, which is of great significance to the optimization of clinker formula. The experimental results show that it can accurately predict the setting time and behave good generalization ability, which addresses the problem of labor intensity in measurement and saves many resources. In addition, the broad learning system can rapidly build a setting time prediction model with few errors, satisfying industrial demands for the rapid modeling of various specialty cements.
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publishDate	2020
allfields	10.1016/j.asoc.2020.106698 doi (DE-627)ELV004901665 (ELSEVIER)S1568-4946(20)30636-0 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl Guo, Jifeng verfasserin aut An efficient model for predicting setting time of cement based on broad learning system 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Cement is the main building material in the construction industry. Its setting time directly affects the setting time and strength of concrete, which further affects construction schedule and building quality. However, traditional measurement technology not only has high labor intensity and high time consumption, but has a high technical requirement. Various human factors, such as insufficient operation, will result in great errors in the measurements. The accurate prediction of setting time enables manpower savings, avoids large errors caused by insufficient operation, and guides the production of high-performance cement. In this paper, an efficient model based on the broad learning system is proposed to predict the initial and final setting time. It is committed to directly predicting setting time from clinker composition and physical properties, which is of great significance to the optimization of clinker formula. The experimental results show that it can accurately predict the setting time and behave good generalization ability, which addresses the problem of labor intensity in measurement and saves many resources. In addition, the broad learning system can rapidly build a setting time prediction model with few errors, satisfying industrial demands for the rapid modeling of various specialty cements. Broad learning system Initial setting time Final setting time Clinker composition Physical properties Wang, Lin verfasserin aut Fan, Kaipeng verfasserin aut Yang, Bo verfasserin aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 96 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:96 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 96
spelling	10.1016/j.asoc.2020.106698 doi (DE-627)ELV004901665 (ELSEVIER)S1568-4946(20)30636-0 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl Guo, Jifeng verfasserin aut An efficient model for predicting setting time of cement based on broad learning system 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Cement is the main building material in the construction industry. Its setting time directly affects the setting time and strength of concrete, which further affects construction schedule and building quality. However, traditional measurement technology not only has high labor intensity and high time consumption, but has a high technical requirement. Various human factors, such as insufficient operation, will result in great errors in the measurements. The accurate prediction of setting time enables manpower savings, avoids large errors caused by insufficient operation, and guides the production of high-performance cement. In this paper, an efficient model based on the broad learning system is proposed to predict the initial and final setting time. It is committed to directly predicting setting time from clinker composition and physical properties, which is of great significance to the optimization of clinker formula. The experimental results show that it can accurately predict the setting time and behave good generalization ability, which addresses the problem of labor intensity in measurement and saves many resources. In addition, the broad learning system can rapidly build a setting time prediction model with few errors, satisfying industrial demands for the rapid modeling of various specialty cements. Broad learning system Initial setting time Final setting time Clinker composition Physical properties Wang, Lin verfasserin aut Fan, Kaipeng verfasserin aut Yang, Bo verfasserin aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 96 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:96 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 96
allfields_unstemmed	10.1016/j.asoc.2020.106698 doi (DE-627)ELV004901665 (ELSEVIER)S1568-4946(20)30636-0 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl Guo, Jifeng verfasserin aut An efficient model for predicting setting time of cement based on broad learning system 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Cement is the main building material in the construction industry. Its setting time directly affects the setting time and strength of concrete, which further affects construction schedule and building quality. However, traditional measurement technology not only has high labor intensity and high time consumption, but has a high technical requirement. Various human factors, such as insufficient operation, will result in great errors in the measurements. The accurate prediction of setting time enables manpower savings, avoids large errors caused by insufficient operation, and guides the production of high-performance cement. In this paper, an efficient model based on the broad learning system is proposed to predict the initial and final setting time. It is committed to directly predicting setting time from clinker composition and physical properties, which is of great significance to the optimization of clinker formula. The experimental results show that it can accurately predict the setting time and behave good generalization ability, which addresses the problem of labor intensity in measurement and saves many resources. In addition, the broad learning system can rapidly build a setting time prediction model with few errors, satisfying industrial demands for the rapid modeling of various specialty cements. Broad learning system Initial setting time Final setting time Clinker composition Physical properties Wang, Lin verfasserin aut Fan, Kaipeng verfasserin aut Yang, Bo verfasserin aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 96 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:96 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 96
allfieldsGer	10.1016/j.asoc.2020.106698 doi (DE-627)ELV004901665 (ELSEVIER)S1568-4946(20)30636-0 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl Guo, Jifeng verfasserin aut An efficient model for predicting setting time of cement based on broad learning system 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Cement is the main building material in the construction industry. Its setting time directly affects the setting time and strength of concrete, which further affects construction schedule and building quality. However, traditional measurement technology not only has high labor intensity and high time consumption, but has a high technical requirement. Various human factors, such as insufficient operation, will result in great errors in the measurements. The accurate prediction of setting time enables manpower savings, avoids large errors caused by insufficient operation, and guides the production of high-performance cement. In this paper, an efficient model based on the broad learning system is proposed to predict the initial and final setting time. It is committed to directly predicting setting time from clinker composition and physical properties, which is of great significance to the optimization of clinker formula. The experimental results show that it can accurately predict the setting time and behave good generalization ability, which addresses the problem of labor intensity in measurement and saves many resources. In addition, the broad learning system can rapidly build a setting time prediction model with few errors, satisfying industrial demands for the rapid modeling of various specialty cements. Broad learning system Initial setting time Final setting time Clinker composition Physical properties Wang, Lin verfasserin aut Fan, Kaipeng verfasserin aut Yang, Bo verfasserin aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 96 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:96 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 96
allfieldsSound	10.1016/j.asoc.2020.106698 doi (DE-627)ELV004901665 (ELSEVIER)S1568-4946(20)30636-0 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl Guo, Jifeng verfasserin aut An efficient model for predicting setting time of cement based on broad learning system 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Cement is the main building material in the construction industry. Its setting time directly affects the setting time and strength of concrete, which further affects construction schedule and building quality. However, traditional measurement technology not only has high labor intensity and high time consumption, but has a high technical requirement. Various human factors, such as insufficient operation, will result in great errors in the measurements. The accurate prediction of setting time enables manpower savings, avoids large errors caused by insufficient operation, and guides the production of high-performance cement. In this paper, an efficient model based on the broad learning system is proposed to predict the initial and final setting time. It is committed to directly predicting setting time from clinker composition and physical properties, which is of great significance to the optimization of clinker formula. The experimental results show that it can accurately predict the setting time and behave good generalization ability, which addresses the problem of labor intensity in measurement and saves many resources. In addition, the broad learning system can rapidly build a setting time prediction model with few errors, satisfying industrial demands for the rapid modeling of various specialty cements. Broad learning system Initial setting time Final setting time Clinker composition Physical properties Wang, Lin verfasserin aut Fan, Kaipeng verfasserin aut Yang, Bo verfasserin aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 96 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:96 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 96
language	English
source	Enthalten in Applied soft computing 96 volume:96
sourceStr	Enthalten in Applied soft computing 96 volume:96
format_phy_str_mv	Article
bklname	Informatik: Allgemeines
institution	findex.gbv.de
topic_facet	Broad learning system Initial setting time Final setting time Clinker composition Physical properties
dewey-raw	004
isfreeaccess_bool	false
container_title	Applied soft computing
authorswithroles_txt_mv	Guo, Jifeng @@aut@@ Wang, Lin @@aut@@ Fan, Kaipeng @@aut@@ Yang, Bo @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
hierarchy_top_id	334375754
dewey-sort	14
id	ELV004901665
language_de	englisch
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author	Guo, Jifeng
spellingShingle	Guo, Jifeng ddc 004 bkl 54.00 misc Broad learning system misc Initial setting time misc Final setting time misc Clinker composition misc Physical properties An efficient model for predicting setting time of cement based on broad learning system
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topic_title	004 DE-600 54.00 bkl An efficient model for predicting setting time of cement based on broad learning system Broad learning system Initial setting time Final setting time Clinker composition Physical properties
topic	ddc 004 bkl 54.00 misc Broad learning system misc Initial setting time misc Final setting time misc Clinker composition misc Physical properties
topic_unstemmed	ddc 004 bkl 54.00 misc Broad learning system misc Initial setting time misc Final setting time misc Clinker composition misc Physical properties
topic_browse	ddc 004 bkl 54.00 misc Broad learning system misc Initial setting time misc Final setting time misc Clinker composition misc Physical properties
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title	An efficient model for predicting setting time of cement based on broad learning system
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title_full	An efficient model for predicting setting time of cement based on broad learning system
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title_sort	an efficient model for predicting setting time of cement based on broad learning system
title_auth	An efficient model for predicting setting time of cement based on broad learning system
abstract	Cement is the main building material in the construction industry. Its setting time directly affects the setting time and strength of concrete, which further affects construction schedule and building quality. However, traditional measurement technology not only has high labor intensity and high time consumption, but has a high technical requirement. Various human factors, such as insufficient operation, will result in great errors in the measurements. The accurate prediction of setting time enables manpower savings, avoids large errors caused by insufficient operation, and guides the production of high-performance cement. In this paper, an efficient model based on the broad learning system is proposed to predict the initial and final setting time. It is committed to directly predicting setting time from clinker composition and physical properties, which is of great significance to the optimization of clinker formula. The experimental results show that it can accurately predict the setting time and behave good generalization ability, which addresses the problem of labor intensity in measurement and saves many resources. In addition, the broad learning system can rapidly build a setting time prediction model with few errors, satisfying industrial demands for the rapid modeling of various specialty cements.
abstractGer	Cement is the main building material in the construction industry. Its setting time directly affects the setting time and strength of concrete, which further affects construction schedule and building quality. However, traditional measurement technology not only has high labor intensity and high time consumption, but has a high technical requirement. Various human factors, such as insufficient operation, will result in great errors in the measurements. The accurate prediction of setting time enables manpower savings, avoids large errors caused by insufficient operation, and guides the production of high-performance cement. In this paper, an efficient model based on the broad learning system is proposed to predict the initial and final setting time. It is committed to directly predicting setting time from clinker composition and physical properties, which is of great significance to the optimization of clinker formula. The experimental results show that it can accurately predict the setting time and behave good generalization ability, which addresses the problem of labor intensity in measurement and saves many resources. In addition, the broad learning system can rapidly build a setting time prediction model with few errors, satisfying industrial demands for the rapid modeling of various specialty cements.
abstract_unstemmed	Cement is the main building material in the construction industry. Its setting time directly affects the setting time and strength of concrete, which further affects construction schedule and building quality. However, traditional measurement technology not only has high labor intensity and high time consumption, but has a high technical requirement. Various human factors, such as insufficient operation, will result in great errors in the measurements. The accurate prediction of setting time enables manpower savings, avoids large errors caused by insufficient operation, and guides the production of high-performance cement. In this paper, an efficient model based on the broad learning system is proposed to predict the initial and final setting time. It is committed to directly predicting setting time from clinker composition and physical properties, which is of great significance to the optimization of clinker formula. The experimental results show that it can accurately predict the setting time and behave good generalization ability, which addresses the problem of labor intensity in measurement and saves many resources. In addition, the broad learning system can rapidly build a setting time prediction model with few errors, satisfying industrial demands for the rapid modeling of various specialty cements.
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title_short	An efficient model for predicting setting time of cement based on broad learning system
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author2	Wang, Lin Fan, Kaipeng Yang, Bo
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up_date	2024-07-07T00:31:20.683Z
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score	7.4008236

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An efficient model for predicting setting time of cement based on broad learning system

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