Computational Model for Chloride Diffusion Coefficient in Concrete Considering the Influence of Cement Type and Strength

doi:10.12141/j.issn.1000-565X.240573

Abstract

Abstract:

To address the limitations of traditional models in cross-laboratory validation, this study proposes a chloride diffusion coefficient model for concrete that incorporates a cement type factor, accounting for the influence of cement type and strength grade. The model is developed through regression analysis of multi-source large-sample Rapid Chloride Migration (RCM) test data. Firstly, a comprehensive database of 179 RCM test datasets from 70 laboratories was established to analyze the effects of water-binder ratio, cement type, and strength grade on the chloride diffusion coefficient via regression. Furthermore, the cement type factor was introduced into the computational model using a two-phase regression method, and its value was determined based on the multi-source large-sample data. Finally, comparative analyses with traditional models and validation using independent test data were conducted. The results show that the proposed multi-source large-sample model improves the fitting accuracy to experimental data by 19.6% compared to conventional mono-source small-sample models. The cement type factor effectively captures the combined influence of cement type and strength, reducing the weighted average error and coefficient of variation by 32.0% and 25.0%, respectively, thereby significantly enhancing the model’s predictive precision and adaptability.

Key words: concrete, cement type factor, chloride diffusion coefficient, rapid chloride migration（RCM）, multi-source large-sample

CLC Number:

TU525

ZHU En, YANG Lufeng. Computational Model for Chloride Diffusion Coefficient in Concrete Considering the Influence of Cement Type and Strength[J]. Journal of South China University of Technology(Natural Science Edition), 2025, 53(12): 153-160.

Figures/Tables 7

Table1

Table 2

Fig.1

Table 3

Cement type factors"

水泥品类	$α C 1$	$α C 2$		$α C 1$	$α C 2$
P·O 32.5	1.33	1.38	P·Ⅱ42.5R	0.85	0.88
P·O 42.5	0.99	0.88	P·O52.5	0.67	0.63
P·O 42.5R	1.24	1.28	P·Ⅰ52.5	1.06	1.18
P·Ⅰ42.5	0.78	0.58	P·Ⅱ52.5	1.35	1.70
P·Ⅱ42.5	0.44	0.19	P·Ⅱ52.5R	1.72	1.80

Table 3

Fig.2

Table 4

Table 5

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水泥品类	样本数量	R_W/B	D_RCM/（10^-12 m²∙s^-1）
P·O32.5	6	0.35~0.60	6.60~22.73
P·O42.5	37	0.31~0.55	4.00~18.50
P·O42.5R	31	0.27~0.55	2.03~19.82
P·O52.5	17	0.30~0.60	1.95~14.70
P·Ⅰ42.5	7	0.38~0.55	7.80~14.90
P·Ⅱ42.5	23	0.30~0.55	2.75~10.30
P·Ⅱ42.5R	18	0.30~0.51	3.31~11.50
P·Ⅰ52.5	17	0.30~0.53	1.51~14.48
P·Ⅱ52.5	11	0.30~0.54	2.00~15.60
P·Ⅱ52.5R	5	0.25~0.45	3.07~18.85

函数类型	r	R²	W_E	C_V
式（1）	0.78	0.61	0.25	0.36
式（2）	0.74	0.52	0.26	0.39
式（3）	0.78	0.61	0.25	0.36

序号	数据来源	水泥品类	R_W/B	D_RCM/（10^-12 m²∙s^-1）		数据来源	水泥品类	R_W/B	D_RCM/（10^-12 m²∙s^-1）
1	文献［26］	P·Ⅱ52.5R	0.30	6.2	20	文献［8］	P·O42.5	0.45	12
2	文献［26］	P·Ⅱ52.5R	0.35	12.5	21	文献［8］	P·O42.5	0.45	11.8
3	文献［26］	P·Ⅱ52.5R	0.40	15.9	22	文献［27］	P·O42.5	0.60	19.5
4	文献［26］	P·Ⅱ52.5R	0.45	17.8	23	文献［27］	P·O42.5	0.45	8.72
5	文献［26］	P·Ⅱ52.5R	0.50	20.73	24	文献［28］	P·O42.5	0.60	19.06
6	文献［8］	P·Ⅰ42.5	0.40	8	25	文献［28］	P·O42.5	0.46	13.73
7	文献［8］	P·Ⅰ42.5	0.45	10.5	26	文献［28］	P·O42.5	0.42	11.33
8	文献［8］	P·Ⅰ42.5	0.40	8.6	27	文献［29］	P·O42.5	0.55	15.8
9	文献［8］	P·Ⅰ42.5	0.45	9.9	28	文献［29］	P·O42.5	0.55	18
10	文献［8］	P·Ⅰ42.5	0.40	7.9	29	文献［30］	P·Ⅰ52.5	0.34	5.92
11	文献［8］	P·Ⅰ42.5	0.45	10.2	30	文献［30］	P·Ⅰ52.5	0.35	6.54
12	文献［8］	P·O42.5	0.35	7.2	31	文献［30］	P·Ⅰ52.5	0.38	8.57
13	文献［8］	P·O42.5	0.35	4.1	32	文献［30］	P·Ⅰ52.5	0.39	8.4
14	文献［8］	P·O42.5	0.35	9.5	33	文献［30］	P·Ⅰ52.5	0.46	10.69
15	文献［8］	P·O42.5	0.35	7.6	34	文献［30］	P·Ⅰ52.5	0.47	12.1
16	文献［8］	P·O42.5	0.35	6.4	35	文献［30］	P·Ⅰ52.5	0.49	11.57
17	文献［8］	P·O42.5	0.45	12.9	36	文献［30］	P·Ⅰ52.5	0.50	15.02
18	文献［8］	P·O42.5	0.45	15.1	37	文献［31］	P·Ⅰ42.5	0.35	6
19	文献［8］	P·O42.5	0.45	14.2

模型	数据来自文献［26］		数据来自文献［8，27-31］
模型	C_V	W_E	C_V	W_E
M₁	0.19	0.31	0.17	0.16
M₂	0.19	0.47	0.19	0.20
M₃	0.13	0.49	0.18	0.21
M₄	0.12	0.44	0.17	0.13
M₅	0.10	0.40	0.17	0.11
M₆	0.09	0.07	0.17	0.09