Trustworthy machine learning-enhanced 3D concrete printing: Predicting bond strength and designing reinforcement embedment length

Three-dimensional concrete printing (3DCP) faces challenges in determining and ensuring adequate bond strength between reinforcement and printed concrete. Traditional methods for predicting bond performance are merely deterministic without considering potential uncertainty, which would lead to risks...

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Veröffentlicht in:	Automation in construction Jg. 168; S. 105754
Hauptverfasser:	Ma, Xin-Rui, Wang, Xian-Lin, Chen, Shi-Zhi
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.12.2024
Schlagworte:	3D concrete printing Bond strength Embedment length Probabilistic prediction Trustworthy machine learning Bond strength 3D concrete printing Probabilistic prediction Embedment length Trustworthy machine learning
ISSN:	0926-5805
Online-Zugang:	Volltext
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