Surface Characteristics and Stochastic Model of Corroded Structural Steel Under General Atmospheric Environment

doi:10.11900/0412.1961.2019.00156

Acta Metall Sin

2020, Vol. 56

Issue (2): 148-160 DOI: 10.11900/0412.1961.2019.00156

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Surface Characteristics and Stochastic Model of Corroded Structural Steel Under General Atmospheric Environment

WANG Youde^1,²(

),XU Shanhua^1,²,LI Han^1,²,ZHANG Haijiang^1,²

1. State Key Laboratory of Green Building in Western China, Xi’an University of Architecture and Technology, Xi’an 710055, China
2. Key Lab of Engineering Structural Safety and Durability, Xi’an University of Architecture and Technology, Xi’an 710055, China

Cite this article:

WANG Youde,XU Shanhua,LI Han,ZHANG Haijiang. Surface Characteristics and Stochastic Model of Corroded Structural Steel Under General Atmospheric Environment. Acta Metall Sin, 2020, 56(2): 148-160.

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Abstract

Steel structures exposed to corrosive atmospheres for a long time are highly susceptible to corrosion damage. The safety assessments of existing corroded steel structures rely heavily on the quantification of corrosion itself. In order to study the corrosion characteristics of structural steel in general atmospheric environment, 6 batches of artificial accelerated corrosion experiments and 8 a of natural exposure experiments were carried out. The surface characteristic parameters and evolution rules of corroded structural steel were studied by the surface morphology tests and self-programmed morphology analysis program. The distribution characteristics of corrosion depth, pit depth and aspect ratio were clarified, and the changing laws of statistical parameters (such as mean value and standard deviation) and pitting shapes were revealed. The results indicated that the corrosion depth of structural steel in general atmospheric environment obeyed the normal distribution, and the pit depth and aspect ratio obeyed the lognormal distribution. With the increase of corrosion degree, the mean value and standard deviation of corrosion depth, the peak value of power spectrum density of corrosion depth, and the logarithmic mean value of pit depth gradually increased, and the logarithmic mean value of pit aspect ratio decreased. Meanwhile, the shape of pits was gradually changed from a cylinder or hemisphere to a cone. Finally, based on the statistical analysis results of corrosion depth parameters and pit parameters, and taking the variation laws and internal relationships of characterization parameters into consideration, the stochastic field model of corrosion depth and the random distribution model of corrosion pits were established, which achieved the accurate simulation and reconstruction of surface characteristics of corroded steel under general atmospheric environment.

Key words: general atmospheric environment structural steel corrosion surface characteristic stochastic model

Received: 20 May 2019

ZTFLH:

TU511.3

Fund: National Natural Science Foundation of China(51908455);China Postdoctoral Science Foundation(2019M653572);Scientific Research Project of Shaanxi Provincial Department of Education(19JS042)

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https://www.ams.org.cn/EN/10.11900/0412.1961.2019.00156 OR https://www.ams.org.cn/EN/Y2020/V56/I2/148

Fig.1 Schematics of corrosion depth parameters extraction (M and N—number of scanning points in x and y axes, Δt_ave—mean value of corrosion depth)(a) corrosion surface (b) cross-section

Fig.2 Principle (a) and result (b) of pitting parameters extraction

Table 1 Corrosion degree parameters of steel plates

Fig.3 Reconstructed surface topographies of accelerated corroded steel plates(a) A1 (b) A2 (c) A3 (d) A4 (e) A5 (f) A6

Fig.4 Surface topographies of naturally exposed steel plates (Side A—upper surface, Side B— lower surface)(a) HTF (b) HBF (c) HW (d) STF (e) SBF (f) SW (g) VTF (h) VBF (i) VW

Fig.5 Statistical results of corrosion depth of accelerated and natural corroded specimens(a) A1 (b) A3 (c) A6 (d) HBF-side A (e) SW-side A (f) VTF-side A

Table 2 Corrosion depth parameters of steel plates

Fig.6 Changing laws of Δt_ave (a) and t_sd (b)

Fig.7 Fitting results of power spectral density (PSD) of accelerated corroded specimens A1 (a), A3 (b) and A6 (c) (ω₁and ω₂—wave numbers corresponding to x and y axes)

Fig.8 Changing laws of κ₁ (a) and κ₂ (b)

Fig.9 Extracted results of pit depth (h) and aspect ratio (A_r)

Sample No.	P_d / cm^-2		μ_h		σ_h		μ $A r$		σ $A r$
Sample No.	Side A	Side B	Side A	Side B	Side A	Side B	Side A	Side B	Side A	Side B
A1	22.2	19.1	4.91	5.09	0.19	0.16	1.58	1.55	0.55	0.53
A2	20.5	20.1	5.21	5.35	0.20	0.18	1.56	1.62	0.69	0.63
A3	19.4	18.5	5.68	5.51	0.07	0.10	0.90	1.43	0.61	0.59
A4	16.2	17.2	5.75	5.80	0.11	0.21	0.94	0.85	0.60	0.63
A5	12.5	13.2	5.63	5.82	0.13	0.14	1.21	0.92	0.64	0.66
A6	10.1	11.5	5.88	5.95	0.24	0.16	1.33	0.95	0.63	0.60
HTF	20.2	6.4	5.46	6.47	0.53	0.38	0.97	0.34	0.69	0.69
HBF	6.9	7.5	6.38	6.23	0.39	0.31	0.72	0.72	0.54	0.68
HW	9.4	11.5	6.34	5.98	0.34	0.21	0.16	0.25	0.5	0.53
STF	15.7	7.6	5.40	6.35	0.40	0.44	1.24	0.61	0.64	0.66
SBF	13.4	10.7	5.88	5.72	0.67	0.48	0.43	0.57	0.62	0.60
SW	8.6	6.8	5.89	6.46	0.38	0.36	1.07	0.68	0.51	0.51
VTF	15.3	14.6	5.49	5.80	0.47	0.63	0.58	0.24	0.49	0.62
VBF	11.0	13.3	6.09	6.27	0.50	0.34	0.28	0.24	0.56	0.56
VW	12.2	14.5	5.91	5.70	0.35	0.39	0.69	0.73	0.49	0.59

Table 3 Characteristic parameters of corrosion pits of steel plates

Fig.10 Changing laws of μ_h (a) and μ

A r

(b)

Fig.11 Statistical results of pitting shape parameter (VB)(a) accelerated corrosion(b) natural corrosion

Fig.12 Relationship between pit depth and pitting shape parameter (h_max—maximum depth of pits;

h a'

h b'

h c'

—relative depths of pits)

Fig.13 Change law of pit density (P_d)

Fig.14 Reconstructed surface of HTF-side B based on SFCD model (a), corrosion depth simlated by Silva model^[27] (b), corrosion pits simulated by RDCP model (c) and Silva model^[27] (d) (Δt_ave=600 μm, t_sd=200 μm, κ₁=2.64, κ₂=1.050, ω_1u=ω_2u=2.4, μ_h=6.31 μm, σ_h=0.48, μ

A r

=0.44, σ

A r

=0.61, N_P=100. ω_1u and ω_2u—upper cut-off wave numbers corresponding to x and y axes; N_p—number of pits)

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