3D打印参数对地聚物混凝土力学性能的影响及深度学习预测模型

doi:10.11896/cldb.25030205

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Abstract To elucidate the effects of three printing parameters—nozzle travel speed V (2.4—7.2 m/min), printing layer height H (4—12 mm), and printing time interval T (0—20 min)— on the workability and mechanical properties of 3D printed geopolymer concrete (3DGPC), this work systematically investigates the underlying mechanisms through rheological parameter analysis and pore structure characterization. Meanwhile, a mechanical property prediction model based on the CEEMDAN-VMD-Transformer-BiLSTM deep learning architecture was constructed. Experimental results demonstrate that 3DGPC achieves optimal comprehensive performance at parameter combination V=4.8 m/min, H=8 mm, and T=0 min, exhibiting 28-day compressive strength, flexural strength, and splitting tensile strength values reaching 82.4%, 86.5%, and 69.7% of cast specimens respectively. The anisotropic index reaches its minimum while buildability attains 81.3% of the theoretical maximum. Rheological analysis reveals 29.2% and 35.7% increments in yield stress and plastic viscosity respectively when T increases from 0 min to 20 min. Porosity characterization shows that printed specimens (V4.8-H8-T0) exhibit 0.61% higher total air content and 10.9 μm reduction in spacing factor compared to cast specimens. The developed deep learning model achieves high-precision prediction of multiple mechanical properties, with error metrics approaching the theoretical optima. This investigation provides a solid theoretical foundation for parameter optimization and performance prediction in 3DGPC fabrication.

Key words： 3D printing parameter workability mechanical property pore structure deep learning

Published: 25 April 2026 Online: 2026-05-06

CLC number:

TU528.41

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	Articles by authors
	LIU Xiang
	ZHU Haifeng
	ZHANG Dongsheng
	MAO Mingjie
	YANG Qiuning

Cite this article:

LIU Xiang,ZHU Haifeng,ZHANG Dongsheng, et al. Effects of 3D Printing Parameters on Mechanical Properties of Geopolymer Concrete and Deep Learning-based Prediction Model[J]. Materials Reports, 2026, 40(8): 25030205-9.

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https://www.mater-rep.com/EN/10.11896/cldb.25030205 OR https://www.mater-rep.com/EN/Y2026/V40/I8/25030205

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