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地聚物混凝土化学组成复杂，力学性能影响因素较多，为探究不同条件下其力学性能变化规律，利用数值模拟进行研究。在构建非均质细观模型上假定混凝土由骨料、砂浆、混凝土界面过渡区三相组成，材料属性服从Weibull分布，并采用等效单元拟合求解非均质参数；通过多尺度建模，提出采用折减系数表征初始微孔洞对材料力学性能的影响；采用模拟正交法反推求解砂浆单元的力学性能参数。为验证非均质细观模型，进行了地聚物混凝土力学性能试验。基于验证后的非均质细观模型，进一步探讨了试件尺寸、骨料体积分数、初始微孔洞率等因素对地聚物混凝土力学性能的影响。结果表明：三维细观模型能准确描述地聚物混凝土的力学性能，所提出的模型构建和参数确定方法具有可靠性，对于模拟研究地聚物混凝土力学性能具有指导意义。
Abstract：Introduction Geopolymer is a green cementitious material produced via the activation of silica-aluminate-rich precursor materials in an alkaline environment.It has outstanding mechanical qualities and durability.However,the different raw materials and complicated reaction process of geopolymers result in some parameters affecting mechanical qualities that are difficult to effectively manage and forecast.It is thus necessary to examine the variation rule of mechanical properties in geopolymer concrete (GPC) under various conditions via numerical simulation.Though the majority of existing numerical models are applicable to ordinary Portland cement (OPC),they cannot be directly applicable to GPC due to variations in the chemical characteristics as well as isomorphic model of mortar and interface transition zone (ITZ) in GPC and OPC.The existing studies lack clear criteria for material inhomogeneity in each phase of the model,and the majority of them neglect the impact of interior micropores on the model mechanical characteristics.In this work,an innovative method was used to account for non-homogeneity and determine the mechanical parameters and intrinsic model of geopolymer materials through experiment.In addition,the impacts of aggregate content,model size,and porosity on the mechanical properties of GPC were also investigated by the verified model.Methods The mechanical parameters and intrinsic model were determined via a geopolymer mortar test,and the model reliability was verified via a geopolymer concrete test.To prepare the specimens,slag and fly ash were used as precursors,and NaOH and water glass were used as alkaline activators.A coarse aggregate was a continuously graded gravel with a particle size range of 5–20 mm.A fine aggregate was a standard sand with a fineness modulus of 2.5.In this proportion,the alkali equivalent (in mass proportion of Na2O to precursor) was 4%,the water-cement ratio was 0.50,the modulus of alkali solution (SiO2/Na2O) was 1.2,and the mortar was kept in the same proportion of mortar phase in the concrete.At the simulation level,the Mesh–Placement–Identification–Assignment (MPIA) procedure was utilized to create a 3D GPC meso-scale model,and the model fundamental conditions were consistent with the experiments.It was assumed that the mechanical characteristics of each phase material inside the concrete could follow the Weibull distribution,and the mortar phase element non-homogeneous parameters could be solved using an equivalent unit approach.Also,initial flaws were introduced into the model,and multi-scale analysis was utilized to assess their impact on mechanical characteristics because the mixing process could form air bubbles in the concrete.After the model was constructed,the material constitutive model was determined via mortar experimental curve fitting,and the mortar test results and test algorithms were used to estimate the material mechanical characteristics.The model was imported into ABAQUS to set the boundary and loading conditions before being compared to the GPC test results obtained after the simulation.The results indicated that the damage patterns and mechanical characteristics of the test and simulation results could be nearly compatible,and the model was regarded as reliable.Results and discussion Based on the validated model,GPC models with different aggregate volume fractions of 30%,40%,and50%are simulated under uniaxial compression.The simulation findings reveal that the compressive strength of GPC increases as the aggregate volume percent increases,as does the energy absorbed for damage.This is because the aggregate plays a role in preventing crack development,so as the aggregate content increases in a specific range,the crack morphology inside the model becomes more complex,the energy required for damage increases,thus improving the macroscopic performance of the mechanical properties.Under uniaxial compression,GPC cubes with the side lengths of 70,100 mm,and 150 mm are simulated,respectively.According to the simulation results,the compressive strength of GPC descends when the specimen volume grows.Compared to OPC,GPC displays the same pronounced size effect phenomena and aligns well with the Ba?ant theoretical formulation.The GPC models with internal porosities of 1.9%,4.3%,and 5.9%are simulated under uniaxial compression,having the compressive strengths of 49.10,47.90 MPa,and 46.19 MPa,respectively.The mechanical characteristics of the geopolymer concrete decrease slightly as the model inside porosity increases.This is since the introduction of pores in this study takes into account both porosity and pore size distribution,and the number of small-sized pores increases as the porosity increases.The small pores have less influence on the mechanical properties,and the results show that the model mechanical properties are insensitive to the change of porosity.Conclusions A non-homogeneous meso-scale model of GPC was proposed,and the simulation results were similar to the experimental result.Also,an innovative approach to describe the non-homogeneity of GPC was proposed.The equivalent unit method was used to determine the non-homogeneity parameter,and the initial microporous defects were characterized via multiscale modeling and folding coefficients.The energy required to destroy GPC and the compressive strength increased with increasing aggregate content.As the model size increased,the mechanical characteristics of GPC decreased,aligning with Ba?ant's theory.The mechanical characteristics of geopolymer concrete diminished slightly as initial microporosity increased.
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基本信息:
DOI：10.14062/j.issn.0454-5648.20240323
中图分类号:TU528
引用信息:
[1]林俊华,左义兵,郑志善等.考虑材料非均质与初始微孔洞的地聚物混凝土力学性能数值模拟[J].硅酸盐学报,2025,53(01):71-83.DOI:10.14062/j.issn.0454-5648.20240323.
基金信息:
国家重点研发计划(2023YFC3902802); 湖北省自然科学基金(2022CFB048,2024AFB562)