硅酸盐学报

物理信息引导的3D打印混凝土智能设计方法

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耿松源 ¹^,² , 龙武剑 ¹^,²^,³^,⁴ , 李豪道 ¹^,² , 罗启灵 ¹^,²^,⁴ , 冯甘霖 ¹^,²

硅酸盐学报 | 研究论文 2026,54(8): 2579-2601

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硅酸盐学报 |研究论文 2026 , 54 (8) : 2579 -2601

物理信息引导的3D打印混凝土智能设计方法

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耿松源¹^,², 龙武剑¹^,²^,³^,⁴, 李豪道¹^,², 罗启灵¹^,²^,⁴, 冯甘霖¹^,²

作者信息

^1．深圳大学土木与交通工程学院，广东深圳 518060

^2．广东省滨海土木工程耐久性重点实验室，广东深圳 518060

^3．深圳市低碳建筑材料与技术重点实验室，广东深圳 518060

^4．深地工程智能建造与健康运维全国重点实验室，广东深圳 518060

通讯作者:

龙武剑（1977—），男，博士，教授。

作者简介:

耿松源（1997—），女，博士研究生。

Physics-Informed Intelligent Design Method for 3D Printing Concrete

Songyuan GENG¹^,², Wujian LONG¹^,²^,³^,⁴, Haodao LI¹^,², Qiling LUO¹^,²^,⁴, Ganlin FENG¹^,²

Affiliations

^1.College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, Guangdong, China

^2.Guangdong Provincial Key Laboratory of Durability for Marine Civil Engineering, Shenzhen 518060, Guangdong, China

^3.Shenzhen Key Laboratory for Low-carbon Construction Material and Technology, Shenzhen 518060, Guangdong, China

^4.State Key Laboratory of Intelligent Construction and Healthy Operation and Maintenance of Deep Underground Engineering, Shenzhen 518060, Guangdong, China

Corresponding Author:

{LONG} Wujian (1977-), male, Ph.D., Professor. E-mail: longwj@szu.edu.cn

GENG Songyuan (1997-), female, Doctoral candidate. E-mail: gengsongyuan2022@email.szu.edu.cn

出版时间: 2026-07-09 doi: 10.14062/j.issn.0454-5648.20260046

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摘要

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3D打印技术能够有效提升建造效率并拓展结构设计的自由度，因而在建筑领域具有广阔的应用前景。然而，该技术在建筑行业中的推广及规模化应用仍面临挑战，主要体现为混凝土材料多性能协同调控难，以及打印工艺参数决策智能化程度不足等。为此，本工作提出一种融合物理信息与数据驱动的混合建模框架，以实现面向目标材料性能的3D打印参数智能设计。该框架首先建立了基于物理信息方程（Physical Information Equation, PIE）的流变性能预测模型，并利用该模型与PIE构建挤出螺杆转速数据库，随后结合目标流量、喷嘴尺寸与打印层高等关键参数，实现对打印速度的精确求解。本工作提出的智能化参数设计方法，为混凝土多性能协同调控提供了量化工具，显著提升了工艺参数优化的效率与可靠性。这为改善3D打印混凝土的成型质量与工艺稳定性提供了坚实的理论依据与方法支持，对推动该技术在建筑工程中的应用与推广具有积极意义。

关键词

3D打印混凝土 / 机器学习 / 物理信息方程 / 融合策略 / 打印参数优化

Abstract

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Introduction

3D printing technology offers a significant potential in the construction sector via enhancing build efficiency and enabling greater design freedom. However, its widespread adoption faces challenges related to the multi-objective optimization of concrete performance and the lack of intelligent decision-making for printing parameters. To address these issues, this paper was to propose a hybrid modeling framework that could integrate physics-informed and data-driven approaches for the intelligent design of 3D printing parameters tailored to target material properties. The framework first developed a rheological performance prediction model based on the physical information equation (PIE). This model, combined with the PIE, was then used to construct an extrusion screw speed (ES) database. The framework could enable accurate determination of the printing speed (PS) via incorporating key parameters such as the target concrete flow rate (Q), printing nozzle diameter (PN), and printing layer height (PH). The proposed intelligent parameter design method could provide a quantitative tool for the collaborative control of multiple concrete properties, significantly enhancing the efficiency and reliability of process parameter optimization. This study could offer a theoretical foundation and methodological support for improving the build quality and process stability of 3D printing concrete, thus facilitating the technology's application and adoption in construction engineering.

Methods

A hybrid physics-data driven framework was developed for printing parameter optimization. This involved two distinct fusion strategies:

1. Physics-Informed Rheological Prediction ("PIE embedded in ML"): A Physics-Informed Convolutional Neural Network (PICNN) was constructed to predict rheological parameters (i.e., Yield Stress-YS, and Plastic Viscosity-PV). Physical equations describing the relationship between mix proportions and rheology were embedded as soft constraints into the CNN's loss function. The Dung Beetle Optimizer (DBO) was used for hyperparameter tuning, including the weights balancing data-driven loss and physics-informed loss.

2. Physics-Serialized Extrusion Speed Prediction ("PIE serialized with ML"): An ES prior database was built via coupling multiple physical equations (i.e., density, Bernoulli, torque) with the predicted rheological parameters from the PICNN model. A Random Forest (RF) model, optimized using DBO, was then trained on this database to predict ES intelligently based on material properties and equipment conditions.

Based on the principle of mass conservation, an explicit calculation equation for PS was derived as a function of Q, PN, and PH. This could complete the parameter optimization chain from material properties to process parameters.

Results and Discussion

The PICNN model for predicting YS and PV demonstrates a stable convergence during training. Compared to the pure data-driven CNN model, the PICNN achieves significantly lower losses (e.g., testing set average loss for YS: PICNN 198.92 Pa vs. CNN 307.80 Pa; for PV: PICNN 0.37 Pa·s vs. CNN 0.77 Pa·s), confirming that physical constraints enhance predictive accuracy and robustness. Furthermore, the predicted values from PICNN show a high consistency with the calculations from the pure PIE (e.g., low MAPE of 6.02% for YS and 2.60% for PV on testing sets), thus proving a great adherence to physical laws.

The RF model for ES prediction, trained on the physics-generated database, achieves a high accuracy with R² values of 0.99 (training set) and 0.97 (testing set), and RMSE values of 1.86 r/s (training) and 2.39 r/s (testing). The scatter plots show points closely distributed around the fit line, and error distributions are concentrated at low levels, indicating the model's effectiveness in learning the complex relationships for ES prediction.

The derived equation for PS effectively establishes the quantitative relationship with Q, PN, and PH. A case study presenting calculated PS values for various common combinations of these target parameters provides a practical reference for configuring printing processes, demonstrating the applicability of the complete optimization framework.

Conclusions

This study established a comprehensive physics-data hybrid modeling framework for optimizing 3D printing concrete parameters, achieving a precise matching between parameters and material rheological properties.

The physics-informed rheological prediction model (PICNN) significantly improved prediction accuracy and physical consistency for YS and PV compared to purely data-driven or purely physics-based approaches.

The physics-serialized extrusion speed prediction method, utilizing a physically-consistent database and an optimized RF model, enabled high-precision, robust intelligent recommendation of ES based on material attributes.

The complete parameter optimization solution, integrating rheological prediction, ES prediction, and PS calculation, provided a systematic and intelligent paradigm for 3D printing parameter design, reducing reliance on conventional trial-and-error methods.

This research could contribute to enhancing the forming quality and process stability of 3D printing concrete, facilitating the intelligent development of construction 3D printing technology. Future work could focus on more complex multi-objective optimization scenarios and real-time dynamic parameter control strategies.

Key words

3D printing concrete / machine learning / physical information equation / fusion strategy / printing parameter optimization

引用本文

耿松源, 龙武剑, 李豪道, 罗启灵, 冯甘霖. 物理信息引导的3D打印混凝土智能设计方法. 硅酸盐学报, 2026 , 54 (8) : 2579 -2601 . DOI: 10.14062/j.issn.0454-5648.20260046

Songyuan GENG, Wujian LONG, Haodao LI, Qiling LUO, Ganlin FENG. Physics-Informed Intelligent Design Method for 3D Printing Concrete[J]. Journal of the Chinese Ceramic Society, 2026 , 54 (8) : 2579 -2601 . DOI: 10.14062/j.issn.0454-5648.20260046

基金

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国家自然科学基金—区域创新发展联合基金集成项目(U25A6016)
深圳市科技计划基础研究面上项目(JCYJ2024 0813143004006)
广东省“特支计划”科技创新领军人才(2023TX07G066)

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2026年第54卷第8期

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文章信息

doi: 10.14062/j.issn.0454-5648.20260046

接收时间：2026-01-25
首发时间：2026-10-22
出版时间：2026-07-09

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出版历史

收稿日期：2026-01-25
修回日期：2026-03-11

基金

国家自然科学基金—区域创新发展联合基金集成项目(U25A6016)

深圳市科技计划基础研究面上项目(JCYJ2024 0813143004006)

广东省“特支计划”科技创新领军人才(2023TX07G066)

作者信息

^1．深圳大学土木与交通工程学院，广东深圳 518060

^2．广东省滨海土木工程耐久性重点实验室，广东深圳 518060

^3．深圳市低碳建筑材料与技术重点实验室，广东深圳 518060

^4．深地工程智能建造与健康运维全国重点实验室，广东深圳 518060

通讯作者:

龙武剑（1977—），男，博士，教授。

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https://castjournals.cast.org.cn/joweb/gsyxb/CN/10.14062/j.issn.0454-5648.20260046

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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