Machine Learning Driven Fluidity and Rheological Properties Prediction of Fresh Cement-Based Materials
Controlling workability during the design stage of cement-based material mix ratios is a highly time-consuming and labor-intensive task. Applying artificial intelligence (AI) methods to predict and optimize the workability of cement-based materials can significantly enhance the efficiency of mix des...
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description | Controlling workability during the design stage of cement-based material mix ratios is a highly time-consuming and labor-intensive task. Applying artificial intelligence (AI) methods to predict and optimize the workability of cement-based materials can significantly enhance the efficiency of mix design. In this study, experimental testing was conducted to create a dataset of 233 samples, including fluidity, dynamic yield stress, and plastic viscosity of cement-based materials. The proportions of cement, fly ash (FA), silica fume (SF), water, superplasticizer (SP), hydroxypropyl methylcellulose (HPMC), and sand were selected as inputs. Machine learning (ML) methods were employed to establish predictive models for these three early workability indicators. To improve prediction capability, optimized hybrid models, such as Particle Swarm Optimization (PSO)-based CatBoost and XGBoost, were adopted. Furthermore, the influence of individual input variables on each workability indicator of the cement-based material was examined using Shapley Additive Explanations (SHAP) and Partial Dependence Plot (PDP) analyses. This study provides a novel reference for achieving rapid and accurate control of cement-based material workability. |
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Applying artificial intelligence (AI) methods to predict and optimize the workability of cement-based materials can significantly enhance the efficiency of mix design. In this study, experimental testing was conducted to create a dataset of 233 samples, including fluidity, dynamic yield stress, and plastic viscosity of cement-based materials. The proportions of cement, fly ash (FA), silica fume (SF), water, superplasticizer (SP), hydroxypropyl methylcellulose (HPMC), and sand were selected as inputs. Machine learning (ML) methods were employed to establish predictive models for these three early workability indicators. To improve prediction capability, optimized hybrid models, such as Particle Swarm Optimization (PSO)-based CatBoost and XGBoost, were adopted. Furthermore, the influence of individual input variables on each workability indicator of the cement-based material was examined using Shapley Additive Explanations (SHAP) and Partial Dependence Plot (PDP) analyses. This study provides a novel reference for achieving rapid and accurate control of cement-based material workability.</description><identifier>ISSN: 1996-1944</identifier><identifier>EISSN: 1996-1944</identifier><identifier>DOI: 10.3390/ma17225400</identifier><identifier>PMID: 39597224</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>Artificial intelligence ; Cement ; Construction ; Design optimization ; Fly ash ; Machine learning ; Mathematical optimization ; Mechanical properties ; Methylcellulose ; Optimization ; Particle swarm optimization ; Prediction models ; Predictions ; Rheological properties ; Rheology ; Shear stress ; Silica fume ; Superplasticizers ; Viscosity ; Workability ; Yield stress</subject><ispartof>Materials, 2024-11, Vol.17 (22), p.5400</ispartof><rights>COPYRIGHT 2024 MDPI AG</rights><rights>2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2024 by the authors. 2024</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c363t-9526a47adf6f3dd266689689be7431489866ad49120eca8ee565f0e98c9242c43</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11595406/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11595406/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,27901,27902,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39597224$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Liu, Yi</creatorcontrib><creatorcontrib>Mohammed, Zeyad M A</creatorcontrib><creatorcontrib>Ma, Jialu</creatorcontrib><creatorcontrib>Xia, Rui</creatorcontrib><creatorcontrib>Fan, Dongdong</creatorcontrib><creatorcontrib>Tang, Jie</creatorcontrib><creatorcontrib>Yuan, Qiang</creatorcontrib><title>Machine Learning Driven Fluidity and Rheological Properties Prediction of Fresh Cement-Based Materials</title><title>Materials</title><addtitle>Materials (Basel)</addtitle><description>Controlling workability during the design stage of cement-based material mix ratios is a highly time-consuming and labor-intensive task. Applying artificial intelligence (AI) methods to predict and optimize the workability of cement-based materials can significantly enhance the efficiency of mix design. In this study, experimental testing was conducted to create a dataset of 233 samples, including fluidity, dynamic yield stress, and plastic viscosity of cement-based materials. The proportions of cement, fly ash (FA), silica fume (SF), water, superplasticizer (SP), hydroxypropyl methylcellulose (HPMC), and sand were selected as inputs. Machine learning (ML) methods were employed to establish predictive models for these three early workability indicators. To improve prediction capability, optimized hybrid models, such as Particle Swarm Optimization (PSO)-based CatBoost and XGBoost, were adopted. Furthermore, the influence of individual input variables on each workability indicator of the cement-based material was examined using Shapley Additive Explanations (SHAP) and Partial Dependence Plot (PDP) analyses. This study provides a novel reference for achieving rapid and accurate control of cement-based material workability.</description><subject>Artificial intelligence</subject><subject>Cement</subject><subject>Construction</subject><subject>Design optimization</subject><subject>Fly ash</subject><subject>Machine learning</subject><subject>Mathematical optimization</subject><subject>Mechanical properties</subject><subject>Methylcellulose</subject><subject>Optimization</subject><subject>Particle swarm optimization</subject><subject>Prediction models</subject><subject>Predictions</subject><subject>Rheological properties</subject><subject>Rheology</subject><subject>Shear stress</subject><subject>Silica fume</subject><subject>Superplasticizers</subject><subject>Viscosity</subject><subject>Workability</subject><subject>Yield stress</subject><issn>1996-1944</issn><issn>1996-1944</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNptkltrHSEQx6WkNCHNSz9AEPISApuul_XoU0hOetLCCS2lfRaj4zmGXT3R3UC-fc2luZSqMIP-5q8zDkKfSHvMmGo_D4bMKO14275DO0Qp0RDF-dYrfxvtlXLd1sEYkVR9QNtMdapG8R3kL41dhwh4CSbHEFf4PIdbiHjRT8GF8Q6b6PDPNaQ-rYI1Pf6R0wbyGKBUF1ywY0gRJ48XGcoaz2GAODZnpoDDl2aEHExfPqL3vhrYe7K76Pfiy6_512b5_eLb_HTZWCbY2KiOCsNnxnnhmXNUCCFVXVcw44xwqaQQxnFFaAvWSIBOdL4FJa2inFrOdtHJo-5muhrA2fqUbHq9yWEw-U4nE_TbkxjWepVuNSGdqkUUVeHwSSGnmwnKqIdQLPS9iZCmohlhjHdS0q6iB_-g12nKseb3QDEuarVfqJXpQYfoU73Y3ovqU0lk_UQxk5U6_g9Vp4Mh2BTBh7r_JuDoMcDmVEoG_5wkafV9Z-iXzqjw_uuyPKN_-4D9AQj3sbI</recordid><startdate>20241105</startdate><enddate>20241105</enddate><creator>Liu, Yi</creator><creator>Mohammed, Zeyad M A</creator><creator>Ma, Jialu</creator><creator>Xia, Rui</creator><creator>Fan, Dongdong</creator><creator>Tang, Jie</creator><creator>Yuan, Qiang</creator><general>MDPI AG</general><general>MDPI</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SR</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>JG9</scope><scope>KB.</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20241105</creationdate><title>Machine Learning Driven Fluidity and Rheological Properties Prediction of Fresh Cement-Based Materials</title><author>Liu, Yi ; Mohammed, Zeyad M A ; Ma, Jialu ; Xia, Rui ; Fan, Dongdong ; Tang, Jie ; Yuan, Qiang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c363t-9526a47adf6f3dd266689689be7431489866ad49120eca8ee565f0e98c9242c43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Artificial intelligence</topic><topic>Cement</topic><topic>Construction</topic><topic>Design optimization</topic><topic>Fly ash</topic><topic>Machine learning</topic><topic>Mathematical optimization</topic><topic>Mechanical properties</topic><topic>Methylcellulose</topic><topic>Optimization</topic><topic>Particle swarm optimization</topic><topic>Prediction models</topic><topic>Predictions</topic><topic>Rheological properties</topic><topic>Rheology</topic><topic>Shear stress</topic><topic>Silica fume</topic><topic>Superplasticizers</topic><topic>Viscosity</topic><topic>Workability</topic><topic>Yield stress</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Yi</creatorcontrib><creatorcontrib>Mohammed, Zeyad M A</creatorcontrib><creatorcontrib>Ma, Jialu</creatorcontrib><creatorcontrib>Xia, Rui</creatorcontrib><creatorcontrib>Fan, Dongdong</creatorcontrib><creatorcontrib>Tang, Jie</creatorcontrib><creatorcontrib>Yuan, Qiang</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Engineered Materials Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>Materials Research Database</collection><collection>Materials Science Database</collection><collection>Materials Science Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Materials</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, Yi</au><au>Mohammed, Zeyad M A</au><au>Ma, Jialu</au><au>Xia, Rui</au><au>Fan, Dongdong</au><au>Tang, Jie</au><au>Yuan, Qiang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Machine Learning Driven Fluidity and Rheological Properties Prediction of Fresh Cement-Based Materials</atitle><jtitle>Materials</jtitle><addtitle>Materials (Basel)</addtitle><date>2024-11-05</date><risdate>2024</risdate><volume>17</volume><issue>22</issue><spage>5400</spage><pages>5400-</pages><issn>1996-1944</issn><eissn>1996-1944</eissn><abstract>Controlling workability during the design stage of cement-based material mix ratios is a highly time-consuming and labor-intensive task. Applying artificial intelligence (AI) methods to predict and optimize the workability of cement-based materials can significantly enhance the efficiency of mix design. In this study, experimental testing was conducted to create a dataset of 233 samples, including fluidity, dynamic yield stress, and plastic viscosity of cement-based materials. The proportions of cement, fly ash (FA), silica fume (SF), water, superplasticizer (SP), hydroxypropyl methylcellulose (HPMC), and sand were selected as inputs. Machine learning (ML) methods were employed to establish predictive models for these three early workability indicators. To improve prediction capability, optimized hybrid models, such as Particle Swarm Optimization (PSO)-based CatBoost and XGBoost, were adopted. Furthermore, the influence of individual input variables on each workability indicator of the cement-based material was examined using Shapley Additive Explanations (SHAP) and Partial Dependence Plot (PDP) analyses. This study provides a novel reference for achieving rapid and accurate control of cement-based material workability.</abstract><cop>Switzerland</cop><pub>MDPI AG</pub><pmid>39597224</pmid><doi>10.3390/ma17225400</doi><oa>free_for_read</oa></addata></record> |
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subjects | Artificial intelligence Cement Construction Design optimization Fly ash Machine learning Mathematical optimization Mechanical properties Methylcellulose Optimization Particle swarm optimization Prediction models Predictions Rheological properties Rheology Shear stress Silica fume Superplasticizers Viscosity Workability Yield stress |
title | Machine Learning Driven Fluidity and Rheological Properties Prediction of Fresh Cement-Based Materials |
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