钢纤维地聚物再生混凝土孔隙结构与力学性能试验研究

李振军; 刘喜; 赵辰宇; 王驰; 田鑫

钢纤维地聚物再生混凝土孔隙结构与力学性能试验研究

长安大学　建筑工程学院, 西安 710061

基金项目: 陕西省自然科学基金基础研究计划(2022KJXX-03)；陕西省杰出青年科学基金(2023-JC-JQ-47)；陕西高校青年创新团队建设项目(2023)；西安市科技计划项目(23GXFW0035)

详细信息

通讯作者:
刘喜，博士，副教授，博士生导师，研究方向为新材料与新结构体系关键技术　E-mail: xliu1205@126.com

中图分类号: TU528
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- 被引次数: 0
出版历程
- 收稿日期: 2023-11-08
- 修回日期: 2023-12-14
- 录用日期: 2023-12-31
- 网络出版日期: 2024-03-01

Pore structure and mechanical properties of steel fiber reinforced geopolymer recycled aggregate concrete

School of Civil Engineering, Chang′an University, Xi′an 710061, China

Funds: The Natural Science Foundation of Shaanxi Province, China (2022KJXX-03); Shaanxi Provincial Science Foundation for Outstanding Young Scholars (2023-JC-JQ-47); Shaanxi University Youth Innovation Team Construction Project (2023); Xi'an Science and Technology Plan Project (23GXFW0035)

摘要

摘要: 为了研究钢纤维地聚物再生混凝土(Steel fiber reinforced geopolymer recycled aggregate concrete，SFGRC)孔隙特性与宏观性能的发展规律，测试了混凝土的内部孔隙结构、力学性能与干燥收缩性能，分析了再生骨料掺量和前驱体钙硅比对混凝土孔隙结构、力学性能与收缩性能的影响规律，基于分形理论建立了SFGRC孔隙结构和宏观性能关联模型。研究结果表明：再生骨料显著增大了SFGRC的孔隙率和有害孔占比，劣化了其力学性能。高掺量矿渣细化了SFGRC的孔隙结构，加大了材料的孔径与空间分布的复杂程度。两者均加剧了SFGRC的早期干燥收缩。SFGRC的孔结构表现出明显的分形特征，其分形维数在2.623~2.731，且与孔隙结构特征参数、力学性能具有很强的相关性，能够有效评价材料孔隙结构特征。采用 Bayesian- markov chain monte carlo(Bayesian-MCMC)方法建立的基于分形维数的SFGRC弹性模量、极限应力、极限应变与干燥收缩应变等特征参数的预测模型，拟合优度为0.51~0.98，且具有较高的预测精度，为优化SFGRC孔隙结构和宏观性能提供了理论依据。
- 钢纤维地聚物再生混凝土 /
- 孔隙结构 /
- 力学性能 /
- 应力-应变曲线 /
- 干燥收缩 /
- 分形理论
Abstract: To study the pore characteristics and macroscopic performance of steel fiber reinforced geopolymer recycled aggregate concrete (SFGRC), the internal pore structure, mechanical properties and shrinkage performance of SFGRC were tested. The influences of recycled aggregate content and calcium silicon ratio on the pore structure, strength, stress-strain curve shape and characteristic parameters of concrete were analyzed. Based on fractal theory, a correlation model between pore structure and the macroscopic performance of SFGRC was established. The research results indicate that recycled aggregate significantly increases the porosity and harmful pore proportion of SFGRC and deteriorates its mechanical properties. The high ground granulated blast furnace slag (GGBS) content refines the pore structure of SFGRC, increasing the complexity of the material's pore size and spatial distribution. The GGBS and recycled aggregate significantly increase the shrinkage rate of SFGRC. The pore structure of SFGRC exhibits obvious fractal characteristics, with fractal dimensions ranging from 2.623 to 2.731. It strongly correlates with pore structure characteristic parameters and mechanical properties, which can effectively evaluate the pore structure characteristics of SFGRC. A prediction model based on fractal dimension for characteristic parameters such as SFGRC elastic modulus, ultimate stress, ultimate strain and drying shrinkage was established using the Bayesian- markov chain monte carlo (Bayesian-MCMC) method, with a goodness of fit of 0.51-0.98 and high prediction accuracy. This provides a theoretical basis for optimizing the pore structure and macroscopic performance of GRC concrete.
- steel fiber reinforced geopolymer recycled aggregates concrete /
- pore structures /
- mechanical properties /
- stress-strain curve /
- drying shrinkage /
- fractal theory

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图 1 试验材料

Figure 1. Test material

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图 2 试验方法

Figure 2. Test methods

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图 3 SFGRC的SEM图像

Figure 3. SEM images of SFGRC

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图 4 SFGRC试件累积孔体积分布曲线

Figure 4. Cumulative pore volume distribution curve of SFGRC

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图 5 SFGRC孔隙结构分类

Figure 5. Pore structure classification of SFGRC

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图 6 SFGRC分形维数

Figure 6. Fractal dimension ofSFGRC

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图 7 SFGRC分形维数与孔结构参数的关系

Figure 7. The relationship between fractal dimension and pore structure parameters of SFGRC

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图 8 SFGRC立方体抗压强度

Figure 8. Cube compressive strength of SFGRC

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图 9 SFGRC劈裂抗拉和抗折强度

Figure 9. Splitting tensile and flexural strength of SFGRC

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图 10 SFGRC分形维数与力学性能的关系

Figure 10. Relationship between fractal dimension and mechanical properties of SFGRC

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图 11 SFGRC破坏形态

Figure 11. Failure pattern of SFGRC

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图 12 SFGRC应力-应变全曲线

Figure 12. Stress-strain curve of SFGRC

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图 13 SFGRC早期干燥收缩

Figure 13. Early drying shrinkage of SFGRC

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图 14 SFGRC分形维数与28 d干燥收缩应变

Figure 14. Fractal dimension and 28 d drying shrinkage of SFGRC

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表 1 再生骨料基本性能指标

Table 1. Basic performance index of recycled aggregate

Aggregate type	RCA	RFA
Gradation/mm	2.36-16	0.15-4.75
Bulk density/(kg·cm⁻³)	1250	1130
Apparent density/(kg·cm⁻³)	2250	2650
24 h water absorption/%	8.3	11.2
Crush index/%	18.2	14.1
Note: RCA represents the recycled coarse aggregate; RFA represents recycled fine aggregate.

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表 2 前驱体主要化学成分(wt%)

Table 2. Main chemical contents of precursor materials (wt%)

Material	Fly ash	GGBS	Silica fume
SiO₂	51.49	32.08	90.44
Al₂O₃	37.19	15.13	0.81
Fe₂O₃	3.52	0.47	3.56
CaO	2.79	38.61	0.63
MgO	0.41	8.45	1.04
SO₃	0.83	2.5	1.34
K₂O	1.11	0.43	0.95
Na₂O	1.15	0.49	0.29
Note: GGBS represents ground granulated blast furnace slag.

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表 3 钢纤维物理性能指标

Table 3. Physical properties of steel fiber

Fiber style	Hooked end steel fiber
Density/(kg·cm⁻³)	7800
Length/mm	35
Diameter/mm	0.5
Aspect ratio	65
Elasticity modulus/GPa	200
Tensile strength/MPa	1100

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表 4 钢纤维地聚物再生混凝土(SFGRC)配合比 (kg·m⁻³)

Table 4. Regeneration mix ratio of steel fiber reinforced geopolymer recycled aggregate concrete (SFGRC) (kg·m⁻³)

Mixture	GGBS	Fly ash	Silica fume	RFA	RCA	Steel fiber	NaOH solution	Na₂SiO₃ solution	Water	Superplasticizer
CG	420	210	70	530	675	39	54	209	88	7
Ca/ Si-0.1	140	490	70	530	675	39	54	209	88	7
Ca/ Si -0.26	280	350	70	530	675	39	54	209	88	7
Ca/ Si -0.64	560	70	70	530	675	39	54	209	88	7
R-30%	589	295	98	318	405	39	76	123	123	10
R-40%	500	260	90	430	540	39	66	253	105	8.5
R-60%	340	170	70	640	810	39	44	167	70	5.5
Notes: CG is the blank group. Ca/ Si-0.1, Ca/ Si -0.26 and Ca/ Si -0.64 are calcium silicon ratio (molar ratio)of precursor, indicating that the calcium silicon ratio is 0.1, 0.26 and 0.64, respectively. R-30%, R-40% and R-50% are volume fraction of recycled aggregate, indicating that the volume fraction of recycled aggregate accounts for 30%, 40% and 60%.

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表 5 分形模型

Table 5. Fractal model

Fractal model	Formula
Menger sponge model	$ \lg (1 - {V_n}) = (3 - D)\lg \left( {{r_n}/R} \right) $
The pore axis fractal model	$ \mathrm{lg}\dfrac{{\text{d}}^{2}{V}_{n}}{ \text{d}{r}_{n}{}^{2}}\propto (1-D)\mathrm{lg}{r}_{n} $
Fractal model based on thermodynamic relationships	$ \lg \left( {{{{W_n}} \mathord{\left/ {\vphantom {{{W_n}} {r_n^2}}} \right. } {r_n^2}}} \right) = D\lg {Q_n} + \lg C' $ $ {W_n}{\text{ = }}\displaystyle\sum\limits_{i = 1}^n {{{\bar P}_i}} \Delta V $　$ {Q_n} = {{V_n^{1/3}} \mathord{\left/ {\vphantom {{V_n^{1/3}} {{r_n}}}} \right. } {{r_n}}} $
Notes: R is the maximum pore size; i represents different pressure stages; P_i is the average pressure of stage i; ΔV is the volume of mercury injected in phase i; n is the number of times mercury is injected; D is the fractal dimension; r_n is the pore diameter corresponding to the n-th injection of mercury; V_n is the accumulated volume of mercury injected up to the n-th injection.

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表 6 SFGRC混凝土孔隙结构特征参数

Table 6. Pore structure characteristic parameters of SFGRC

Specimen	Porosity/%	Most probable aperture/nm	Tortuosity	Total pore volume/ (mL·g⁻¹)
Specimen	Porosity/%	Most probable aperture/nm	Tortuosity	Total pore volume/ (mL·g⁻¹)	CG	22.06	126.33	2.009	0.0662
Ca/Si-0.64	20.78	110.68	2.057	0.0624
Ca/Si-0.26	23.56	135.90	1.959	0.0707
Ca/Si-0.10	25.32	131.22	1.929	0.0759
R-30%	19.06	100.20	1.998	0.0599
R-40%	21.06	120.96	1.976	0.0632
R-60%	26.72	161.18	1.932	0.0802

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表 7 SFGRC力学性能 (MPa)

Table 7. Mechanical properties of SFGRC (MPa)

Specimen	f_{cu,3 d}	f_{cu,7 d}	f_{cu,28 d}	f_ts	f_f
CG	28.18	32.08	43.13	3.27	3.77
Ca/Si-0.64	38.27	39.87	47.15	3.65	3.91
Ca/Si-0.26	25.59	32.08	36.20	2.78	3.59
Ca/Si-0.10	13.94	17.05	23.05	2.48	3.03
R-30%	37.28	40.44	46.12	3.35	4.05
R-40%	37.45	40.66	44.77	3.38	3.96
R-60%	20.69	26.39	27.39	2.35	2.62
Notes: f_{cu,3 d}, f_{cu,7 d}, and f_{cu,28 d} are cube compressive strength of 3 d, 7 d, and 28 d, respectively. f_ts and f_f are the tensile and flexural strength of 28 d, respectively.

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表 8 基于分形维数的特征参数预测模型

Table 8. Prediction model of characteristic parameters based on fractal dimension

Characteristic parameters	Prediction model	Goodness of fit R²	Prediction Error
Characteristic parameters	Prediction model	Goodness of fit R²	Mean	Standard deviation	Covariance
Elastic modulus	$ {E_C} = 5300\sqrt[3]{{{f_c}}}\left( {0.0057 D - 0.0143} \right) $	0.80	1.06	0.1008	0.0948
Peak stress	$ {f_{\text{c}}} = {f_{{\text{cu}}}}\left( {{\text{1}}{\text{.3353 - }}0.1475 D} \right) $	0.98	0.98	0.0050	0.0051
Peak strain	$ {\varepsilon _{\text{c}}} = \left( {{\text{0}}{\text{.3097}}D - 0.1681} \right){f_c}^{0.3838} $	0.51	1.01	0.1066	0.1057

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