硅酸盐学报

极端环境下碱激发模拟月壤：基于数据驱动的微观结构表征与性能预测

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姚羿舟 ¹^,² , 朱超 ¹^,²^,³ , 张韦 ¹^,² , 刘超 ¹^,²^,³

硅酸盐学报 | 研究论文 2026,54(8): 2673-2701

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硅酸盐学报 |研究论文 2026 , 54 (8) : 2673 -2701

极端环境下碱激发模拟月壤：基于数据驱动的微观结构表征与性能预测

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姚羿舟¹^,², 朱超¹^,²^,³, 张韦¹^,², 刘超¹^,²^,³

作者信息

^1．西安建筑科技大学土木工程学院，西安 710055

^2．西安建筑科技大学数字化绿色建造研究院，西安 710055

^3．陕西省智能建造未来产业创新研究院，西安 710055

通讯作者:

刘超（1982—），男，博士，教授。

作者简介:

姚羿舟（1999—），男，博士研究生。

Alkali-Activated Lunar Regolith Simulant Under Extreme Environments: Data-Driven Microstructural Characterization and Performance Prediction

Yizhou YAO¹^,², Chao ZHU¹^,²^,³, Wei ZHANG¹^,², Chao LIU¹^,²^,³

Affiliations

^1.School of Civil Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China

^2.Digital Green Construction Research Institute, Xi'an University of Architecture and Technology, Xi'an 710055, China

^3.Shaanxi Intelligent Construction Future Industry Innovation Research Institute, Xi'an 710055, China

Corresponding Author:

{LIU} Chao (1982-), male, Ph.D., Professor. E-mail: chaoliu@xauat.edu.cn

YAO Yizhou (1999-), male, Doctoral candidate. E-mail: yaoyizhou@xauat.edu.cn

出版时间: 2026-07-21 doi: 10.14062/j.issn.0454-5648.20250837

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摘要

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碱激发月壤作为极具应用前景的月基建筑材料，准确表征、理解和预测其在极端环境下的性能演化规律是确保月基建筑结构在施工与服役过程中安全可靠的基础。本工作制备了氢氧化钠基和硅酸钠基碱激发模拟月壤（AALRS）净浆并在真空和热真空环境下原位养护。采用降噪、聚类、堆叠算法与超参数优化算法，结合多尺度试验表征，提出基于数据驱动的“算法解构-性能预测-机制解析”的AALRS跨尺度智能分析范式，分别构建了可解释的特征X射线能谱（EDS）图像定量分析框架与强度预测模型，并基于此开展极端环境下AALRS微观结构表征与宏观性能预测。结果表明，与真空环境相比，热真空环境下AALRS有更高的产物相占比与更低的孔隙率，其物相分布更匀质，基体展现出更高的力学强度。而真空环境下AALRS出现孔隙率增加、碱溶液残留与物相分布异质性加剧的现象，基体力学强度明显下降。基于物相定量分析结果揭示了极端环境下AALRS的微观结构特征与反应机理。在性能预测模型中，基于随机搜索算法优化的Stacking-RS模型展现出最优的预测精度、泛化能力和计算效率。进一步基于全局特征重要性分析、特征依赖分析与特征局部贡献分析解构Stacking-RS模型的预测逻辑并分析强度影响机制。研究成果推动了极端环境下AALRS材料表征和性能预测由经验试错驱动向智能数据驱动的研究范式转变。

关键词

碱激发月壤 / 极端环境 / 机器学习 / 微观结构 / 性能预测

Abstract

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Introduction

Lunar construction represents a strategic frontier in deep space exploration. Alkali-activated lunar regolith simulant (AALRS) is a highly promising in-situ construction material. Accurately characterizing, understanding and predicting its performance under extreme environments are critical to the safety of lunar structures during construction and service. However, the existing research shows significant discrepancies and controversies. These arise from a series of challenges, i.e., 1) the inherent compositional complexity and variability of lunar regolith simulant, 2) the coupled effects of multiple factors, 3) a general reliance on qualitative and localized understanding of microstructure, 4) the lack of an underlying foundation for understanding the mechanisms that influence macroscopic performance, and 5) the reliance on simple statistical analysis of experimental data without establishing quantitative association models between parameters and performance. To address these challenges, this study was to propose an interpretable, cross-scale and data-driven research paradigm of "algorithmic deconstruction-performance prediction-mechanism analysis".

Methods

AALRS pastes were prepared using lunar regolith simulant XJ-1, alkaline activators (i.e., sodium hydroxide or sodium silicate), and water at a specified alkali content of 0.075 and a water-to-binder ratio of 0.3. To simulate extreme environments, the samples were exposed to a vacuum (i.e, 20 ℃ and 100 Pa) and a thermal vacuum (i.e., 90 ℃ and 100 Pa) for 1, 3, 7, 14 d and 28 d, respectively. Macroscopically, the compressive strength was measured in a WAW-1000 testing machine at a loading rate of 480 N/s. The energy dispersive X-ray spectroscopy (EDS) mappings of key elements (i.e., Si, Al, Na, Ca, Mg, and Fe) were acquired in an accelerating voltage of 15 kV, a working distance of 10 mm, a dwell time of 100 μs/pixel, a process time of 6, and a channel count of 2048.

A quantitative analysis framework for EDS mappings driven by unsupervised clustering algorithms was developed. Implemented in Python, this framework automates the establishment of an interpretable analysis paradigm using libraries such as OpenCV, scikit-learn, and scikit-image. To address underfitting, overfitting, and poor performance of basic ML algorithms in complex hypothesis spaces, a two-level Stacking ensemble model was proposed. The first level integrates six heterogeneous base learners like AdaBoost, XGBoost, CatBoost, SVR, MLPNN, and RF. The predictions from these base learners served as input features for the second level by a 5-fold cross validation strategy. This second level employs a linear regression model as a meta-learner. To further enhance the predictive accuracy of the model, its configuration was optimized via applying six hyperparameter tuning algorithms (i.e., RS, BO, PSO, GWO, SA, and TPE) to efficiently search the hyperparameter space.

Results and discussion

The results of compressive strength reveal the disparities governed by both the activation system and environment. The AALRS-SS consistently outperforms the AALRS-SH. The AALRS-SS under HV environment achieves a peak 28 d strength of 32.9 MPa, which is 3.65 times higher than its AALRS-SH counterpart under the same conditions. In addition, the HV environment proves crucial for the strength development, compared to the V environment. To understand the mechanisms behind these macroscopic differences, a quantitative EDS mapping analysis is employed. The results show that AALRS-SS generally contains a higher area fraction of reaction products and correspondingly lower proportions of unreacted XJ-1 and porosity. This indicates a higher overall degree of reaction and a denser microstructure in the AALRS-SS, providing a direct explanation for its superior strength. The HV environment facilitates a more homogeneous phase assemblage, compared to the greater heterogeneity observed in the V environment. The elemental analysis based on the superpixel clustering further reveals that calcium remains largely inert. The gels in the AALRS-SH exhibit a higher aluminum content, while those in the AALRS-SS are characterized at a higher silicon content. Based on the quantitative EDS analysis, the microstructural characteristics and reaction mechanisms of AALRS under different environments and activation systems are elucidated.

To capture the complex, non-linear relationships among material composition, environmental conditions, and mechanical performance, a two-level Stacking ensemble model is proposed. The model tuned with RS is identified as an optimal choice for its excellent balance of high performance and rapid computation after comparing multiple optimization algorithms on both accuracy and computational efficiency. This selected Stacking-RS model is interpreted based on the SHAP analysis to reveal the underlying strength development mechanisms. The analysis confirms the strong, positive, and monotonic effects of AC, CA and IT. In contrast, it identifies a critical negative threshold for MPD around 50 μm and reveals a non-monotonic influence of W/B. The effect of SM is complex, with significant interactions with other features. A graphical user interface (GUI) is developed based on the Stacking-RS model, thus facilitating the efficient design of AALRS. Furthermore, this study establishes a cross-scale, data-driven analytical framework that integrates algorithmic deconstruction, performance prediction, and mechanistic interpretation, wherein the microstructural analysis and macroscopic prediction modules mutually complement and validate each other to enable an intelligent understanding and accurate prediction of AALRS performance under extreme environments.

Conclusions

AALRS-SS under the HV environment exhibited the maximum strength. Its 28 d strength reached 32.9 MPa, which was 3.65 times greater than that of AALRS-SH (9.0 MPa). This was attributed to the SS system forming a higher proportion of gel products and a denser microstructure.

A quantitative EDS image analysis framework based on GMM and SLIC clustering algorithms was established. Compared with the V environment, the HV environment significantly increased the proportion of reaction products and compositional homogeneity, while reducing porosity. The reaction degree of AALRS-SS was significantly higher than that of AALRS-SH.

The reaction mechanisms of AALRS-SH and AALRS-SS under extreme environments were revealed. Under the V environment, AALRS-SH showed a significantly increased porosity with Na enrichment inside the pores. Under the HV environment, gels precipitated on the XJ-1 particle surfaces, forming a product layer that hindered subsequent dissolution, leading to a porous structure, and low strength. In contrast, AALRS-SS, due to the presence of silicate polymers, provided numerous nucleation sites through depolymerization under the HV environment, forming a relatively dense microstructure. Under the V environment, it provided a bonding force through self-assembly gelation of silicate polymers.

A Stacking ensemble model optimized by RS was identified as the best-performing model, showing excellent accuracy, generalization, and efficiency (i.e., R=0.9482, RMSE=5.58 MPa, MAE=4.07 MPa, and t=0.77 min).

The SHAP analysis (global, local, and dependence) showed that AC, CA, and IT were the most significant positive contributing features. The negative impact of MPD became more significant beyond 50 μm. The effect of W/B was non-monotonic. A GUI was developed based on the Stacking-RS model to enhance its practicality.

A data-driven, cross-scale intelligent analysis paradigm of "algorithmic deconstruction-performance prediction-mechanism analysis" was proposed. Its core lay in the macro-prediction module, which was used for strength prediction and identifying key factors, and the microstructure module, providing direct experimental evidence to elucidate the underlying strength development mechanism.

Key words

alkali-activated lunar regolith / extreme environment / machine learning / microstructure / performance prediction

引用本文

姚羿舟, 朱超, 张韦, 刘超. 极端环境下碱激发模拟月壤：基于数据驱动的微观结构表征与性能预测. 硅酸盐学报, 2026 , 54 (8) : 2673 -2701 . DOI: 10.14062/j.issn.0454-5648.20250837

Yizhou YAO, Chao ZHU, Wei ZHANG, Chao LIU. Alkali-Activated Lunar Regolith Simulant Under Extreme Environments: Data-Driven Microstructural Characterization and Performance Prediction[J]. Journal of the Chinese Ceramic Society, 2026 , 54 (8) : 2673 -2701 . DOI: 10.14062/j.issn.0454-5648.20250837

基金

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国家自然科学基金资助项目(52178251)
陕西省秦创原“科学家+工程师”队伍建设项目(2023KXJ-242)
陕西省教育厅产业化项目(23JC047)
陕西高校青年创新团队(2023-2026)

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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.0454-5648.20250837

接收时间：2025-11-13
首发时间：2026-10-22
出版时间：2026-07-21

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出版历史

收稿日期：2025-11-13
修回日期：2025-12-01

基金

国家自然科学基金资助项目(52178251)

陕西省秦创原“科学家+工程师”队伍建设项目(2023KXJ-242)

陕西省教育厅产业化项目(23JC047)

陕西高校青年创新团队(2023-2026)

作者信息

^1．西安建筑科技大学土木工程学院，西安 710055

^2．西安建筑科技大学数字化绿色建造研究院，西安 710055

^3．陕西省智能建造未来产业创新研究院，西安 710055

通讯作者:

刘超（1982—），男，博士，教授。

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https://castjournals.cast.org.cn/joweb/gsyxb/CN/10.14062/j.issn.0454-5648.20250837

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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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