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OA

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.