数据驱动范式下化学激发硅铝质胶凝材料的研究进展OA

In the context of global low-carbon and carbon-neutrality targets,chemico-activated aluminosilicate cementitious materials that are represented by geopolymers become one of the most promising green alternatives to ordinary Portland cement.Geopolymers are synthesized via the chemico-activation of reactive aluminosilicate solid wastes with an alkaline activator.Their manufacturing process is featured by low energy consumption and reduced carbon emissions through the valorization of industrial solid waste or low-energy natural mineral precursors.Furthermore,the reaction products are endowed with high early-age mechanical strength,excellent durability and fire resistance.However,the complexity of raw material composition,unclear polymerization mechanisms and intricate interdependencies among multiple variables result in low efficiency,poor reproducibility and accuracy of conventional trial-and-error,seriously restricting the multi-objective performance optimization and large-scale application of geopolymers. Based on the materials initiative and rapid development of artificial intelligence,the data-driven paradigm initiates a fundamental shift in materials science research.Establishing the relationships between high-dimensional data(e.g.,materials compositions,structure,physical and chemical properties)and the material performance through learning massive multi-source data can effectively address the limitations of the conventional methods,i.e.,high cost,long time and numerous resources.It therefore provides intelligent and accelerated routes to revolutionize the design and investigation of chemically activated aluminosilicate cementitious materials,leading a paradigm shift from experimental-driven to data-driven approach.This review represents research progress on data-driven approaches in geopolymer,with emphasis on three aspects,i.e.,1)machine learning methodologies for performance prediction;2)application of machine learning in targeted performance prediction;and 3)metaheuristic algorithms-based composition optimization. This review proposes a complete data-driven workflow for geopolymer research,including target definition,dataset collection,data processing,feature engineering,model construction and application.Based on the application scenarios,the existing models can be divided into four categories,i.e.,neural networks,ensemble learning,traditional regression and statistical learning,and symbolic regression combined with neuro-fuzzy systems.Each category offers distinct advantages and disadvantages.Neural networks capture nonlinear relationships,but require large dataset and lack interpretability.Ensemble learning handles high-dimensional mixed data with robustness,but risks overfitting with small samples.Traditional regression combined with neuro-fuzzy systems works for small to medium dataset,but faces an exponential rule growth under high-dimensional inputs.In terms of targeted performance prediction,machine learning is effectively applied to early-age properties,mechanical properties,and durability.For early-age behaviors,machine learning models can accurately predict geopolymerization heat flow,peak time,setting time and slump.For mechanical properties,compressive strength prediction is the most mature predicted performance,and the prediction is extended to flexural strength,splitting tensile strength,elastic modulus,etc..For durability,the initial research mainly focuses on drying shrinkage,autogenous shrinkage,carbonation,while some studies on chloride penetration,sulfate attack and long-term durability are relatively scarce. Metaheuristic algorithms play a significant role on geopolymer composition design.Firstly,metaheuristic algorithms serve to optimize hyperparameters and improve prediction accuracy.Subsequently,the well-trained predictive models can be leveraged to facilitate both forward guidance and inverse mix design.Based on the trained model,the importance of material composition,preparation parameters,and curing conditions to prediction accuracy can be quantitatively assessed.Furthermore,metaheuristic algorithms can also apply to reverse geopolymer mixture design targeting single or multiple objectives.For instance,a Pareto frontier is constructed via leveraging NSGA-Ⅱ to balance high strengths,low carbon and low cost,and the optimal solution can be further selected via TOPSIS.This approach pioneers an effective pathway for the inverse design of geopolymers and establishes a versatile decision-support tool that is poised to become a new standard for the accelerated development of high-performance and eco-efficient cementitious materials. Summary and prospects Despite progress on applying data-driven methods to chemically activated aluminosilicate cementitious materials,several critical challenges remain.Firstly,the inherent complexity and variability of raw materials,combined with the absence of standardized design specifications,lead to high-dimensional and redundant datasets that compromise the model performance.To address this issue,it is necessary to establish standardized open databases covering precursor composition,reactivity,activator parameters,and curing conditions,as well as the knowledge-embedded hybrid models that incorporate geopolymerization mechanisms and thermodynamic constraints.Secondly,data scarcity is a pervasive challenge in this field,with most datasets characterized by small sample sizes,severely limiting model generalizability.This issue can be alleviated through multi-source data fusion,data augmentation techniques,and transfer learning for small-sample scenarios.Lastly,the existing inverse design relies primarily on a two-step prediction-then-optimization framework,whereas deep generative models for direct material design remain largely underexplored.Integrating these advanced models with reaction kinetics and microstructure evolution can be essential to realize end-to-end inverse design. The evolution of artificial intelligence technology offers a significant potential for advancing chemico-activated aluminosilicate cementitious materials technology.This promises to serve as a robust reference for the deep integration of artificial intelligence into future material development.Future efforts should focus on developing standardized workflows,improving model interpretability and generalizability,as well as leveraging multi-scale simulations and deep generative models to accelerate material innovation.Data-driven methods will enable more accurate prediction and design of chemico-activated aluminosilicate materials due to these advancements,thereby contributing to the sustainable transformation of the construction industry.