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Article: Knowledge-enhanced data-driven models for quantifying the effectiveness of PP fibers in spalling prevention of ultra-high performance concrete

TitleKnowledge-enhanced data-driven models for quantifying the effectiveness of PP fibers in spalling prevention of ultra-high performance concrete
Authors
KeywordsExplosive spalling
UHPC
Machine learning
Data augment
High temperature
Issue Date2021
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/conbuildmat
Citation
Construction and Building Materials, 2021, v. 299, p. article no. 123946 How to Cite?
AbstractUltra-high performance concrete (UHPC) shows volumetric instability at high temperatures because of explosive spalling. Polypropylene (PP) fibers are effective in mitigating the explosive spalling of UHPC; however, the threshold PP fiber dosage to prevent explosive spalling of UHPC remains unascertained. In this work, a knowledge-enhanced data-driven machine learning method is proposed to quantify the effectiveness of PP fibers in preventing fire-induced spalling of UHPC. Based on the knowledge-enhanced technique, the training data size is increased from 244 to 1642. The prediction accuracy of the artificial neural network (ANN) and extreme gradient boosting (XGBoost) models on 54 groups of unseen data is increased by more than 10% after the training data augment. The well-validated ANN model is then used to determine the threshold PP fiber dosage of UHPC tunnel lining under hydrocarbon fire conditions. The analysis results show that as high as 3.5 kg/m3 PP fibers are required to eliminate explosive spalling of UHPC, but this critical dosage can be lowered to 2.5 kg/m3 by limiting the silica fume replacement level.
Persistent Identifierhttp://hdl.handle.net/10722/303928
ISSN
2023 Impact Factor: 7.4
2023 SCImago Journal Rankings: 1.999
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLiu, JC-
dc.contributor.authorHuang, L-
dc.contributor.authorTIAN, Z-
dc.contributor.authorYe, H-
dc.date.accessioned2021-09-23T08:52:45Z-
dc.date.available2021-09-23T08:52:45Z-
dc.date.issued2021-
dc.identifier.citationConstruction and Building Materials, 2021, v. 299, p. article no. 123946-
dc.identifier.issn0950-0618-
dc.identifier.urihttp://hdl.handle.net/10722/303928-
dc.description.abstractUltra-high performance concrete (UHPC) shows volumetric instability at high temperatures because of explosive spalling. Polypropylene (PP) fibers are effective in mitigating the explosive spalling of UHPC; however, the threshold PP fiber dosage to prevent explosive spalling of UHPC remains unascertained. In this work, a knowledge-enhanced data-driven machine learning method is proposed to quantify the effectiveness of PP fibers in preventing fire-induced spalling of UHPC. Based on the knowledge-enhanced technique, the training data size is increased from 244 to 1642. The prediction accuracy of the artificial neural network (ANN) and extreme gradient boosting (XGBoost) models on 54 groups of unseen data is increased by more than 10% after the training data augment. The well-validated ANN model is then used to determine the threshold PP fiber dosage of UHPC tunnel lining under hydrocarbon fire conditions. The analysis results show that as high as 3.5 kg/m3 PP fibers are required to eliminate explosive spalling of UHPC, but this critical dosage can be lowered to 2.5 kg/m3 by limiting the silica fume replacement level.-
dc.languageeng-
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/conbuildmat-
dc.relation.ispartofConstruction and Building Materials-
dc.subjectExplosive spalling-
dc.subjectUHPC-
dc.subjectMachine learning-
dc.subjectData augment-
dc.subjectHigh temperature-
dc.titleKnowledge-enhanced data-driven models for quantifying the effectiveness of PP fibers in spalling prevention of ultra-high performance concrete-
dc.typeArticle-
dc.identifier.emailLiu, JC: jcliu@hku.hk-
dc.identifier.emailYe, H: hlye@hku.hk-
dc.identifier.authorityYe, H=rp02379-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.conbuildmat.2021.123946-
dc.identifier.scopuseid_2-s2.0-85108119798-
dc.identifier.hkuros325003-
dc.identifier.volume299-
dc.identifier.spagearticle no. 123946-
dc.identifier.epagearticle no. 123946-
dc.identifier.isiWOS:000685936900001-
dc.publisher.placeNetherlands-

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