Stochastic Model for Surface Characterization of Structural Steel Corroded in Simulated Offshore Atmosphere
WANG Youde1,2(), ZHOU Xiaodong1,2, MA Rui2,3, XU Shanhua1,2
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 3.First Affiliated Hospital, Xi'an Jiaotong University, Xi'an 710061, China
Cite this article:
WANG Youde, ZHOU Xiaodong, MA Rui, XU Shanhua. Stochastic Model for Surface Characterization of Structural Steel Corroded in Simulated Offshore Atmosphere. Acta Metall Sin, 2021, 57(6): 811-821.
Steel structures exposed to offshore atmospheric environment for a long time inevitably suffer from corrosion damage. Safety assessment of corroded steel structures largely depends on the quantification of corroded surface features as the irregular corrosion characteristics are the main factors causing decline in steel mechanical properties. To investigate the structural steel corrosion characteristics in offshore atmospheric environment, accelerated corrosion tests were conducted on 16 pieces of Q235B steel plates by periodic spraying to simulate the offshore atmospheric environment. Moreover, the surface morphologies and characteristic parameters were measured and analyzed using a ST400 3D Noncontact Profilometer and a self-written algorithm. The distribution characteristics such as corrosion depth, pit depth, and aspect ratio were elucidated, and the changing laws of statistical parameters such as mean value, standard deviation, and pitting shapes were revealed. The results indicated that in the simulated offshore atmospheric environment, the structural steel corrosion process generally goes through three stages: scab, swell, and spall. The scab and swell stages are dominated by pitting corrosion, whereas, the spall stage shows the general corrosion characteristics. Moreover, the corrosion depth of structural steel in the simulated offshore atmospheric environment conforms to the normal distribution, whereas, the pit depth and aspect ratio conform to the log-normal distribution. As the degree of corrosion increases, the mean value and standard deviation of the corrosion depth, peak value of the power spectral density of the corrosion depth, and logarithmic mean value of the pit depth also gradually increase, whereas, the logarithmic mean value of the pit aspect ratio decreases. Meanwhile, at different ages, the cone pits have the highest proportion, and the pit shape gradually changes from a cylinder or a hemisphere to a cone. Finally, based on the results of the statistical analysis of the corrosion depth and pit parameters, the stochastic field model of corrosion depth and random distribution model of corrosion pits were constructed, which achieved the accurate characterization and reproduction of the surface morphology of the corroded steel in a simulated offshore atmospheric environment. The research results would lay the foundation for the establishment of an accurate stochastic model and structural reliability analysis in the natural offshore atmospheric environment.
Fund: National Natural Science Foundation of China(51908455);China Postdoctoral Science Foundation(2019M653572);Scientific Research Project of Shaanxi Provincial Department of Education(19JS042)
About author: WANG Youde, associate professor, Tel: (029)82207610, E-mail: yord.w@xauat.edu.cn
Fig.1 Corrosion features of Q235B steel specimens with different ages before (b1-i1) and after (b2-i2) descaling
Fig.2 Comparisons between the corrosion losses of steel under accelerated and natural[17] offshore atmospheric environment (Δte / 2 refers to the single-sided equivalent thickness loss, which is equal to ηT0 / 2; η is the mass loss ratio, and T0 is the initial thickness)
Fig.3 Surface morphologies of corroded specimens (z is the corrosion depth relative to the vertex of surface)
Fig.4 Statistical results of corrosion depth of corroded specimens
Sample
No.
Δte
μm
Δtave
μm
tsd
μm
a
b
Pd
cm-2
μh
σh
μAr
σAr
S1-1
114
58
24
1.618
1.062
15.3
4.55
0.24
2.21
0.44
S1-2
119
62
26
1.867
1.031
19.8
4.23
0.15
2.19
0.41
S2-1
374
157
78
2.369
1.583
21.9
5.33
0.19
1.60
0.50
S2-2
379
192
87
2.458
1.635
19.0
5.49
0.33
1.53
0.55
S3-1
552
212
101
2.124
1.374
18.5
5.64
0.25
1.33
0.61
S3-2
540
160
87
2.215
1.382
15.6
5.40
0.38
1.49
0.63
S4-1
582
227
72
2.271
1.501
18.0
5.62
0.22
1.31
0.62
S4-2
602
234
129
1.535
0.844
13.4
5.56
0.47
1.50
0.47
S5-1
692
261
121
3.306
1.955
23.0
5.60
0.48
1.56
0.55
S5-2
698
210
76
1.338
0.853
24.1
5.72
0.17
1.37
0.39
S6-1
796
267
107
2.644
1.598
25.1
5.97
0.26
1.19
0.55
S6-2
808
207
95
2.429
1.442
21.8
5.66
0.27
1.19
0.50
S7-1
1091
293
109
2.531
1.546
21.1
5.95
0.23
1.07
0.59
S7-2
1144
395
126
2.613
1.609
20.9
6.28
0.13
0.81
0.61
S8-1
1387
360
107
2.438
1.469
23.5
6.22
0.17
0.79
0.54
S8-2
1434
397
128
2.024
1.198
19.0
6.38
0.14
0.74
0.48
Table 1 Corrosion depth parameters and pitting characteristic parameters
Fig.5 Changing laws of Δtave relative to Δte (a) and tsd relative to Δtave (b)
Fig.6 Fitting results of power spectral density (PSD) of corroded specimens (ω1 and ω2—wave numbers corresponding to x and y axes, respectively)
Fig.7 Changing laws of a relative to Δtave (a) and b relative to a (b)
Fig.8 Extracted results of pit depth (h) and aspect ratio (Ar)
Fig.9 Changing laws of μh (a) and μAr (b) relative to Δtave
Fig.10 Pitting shape analyses (hmax—maximum depth of pits; h / hmax—relative depth of pit; h'a, h'b, h'c, and h'd—relative depths of pits; I, II, and III—regions of cone, hemisphere, and cylinder pits divided by VB value, respectively; ①-⑥—distribution zones of the three shapes of pits in the range of different relative depths (h'a-h'b, h'b-h'c, and h'c-h'd))
Fig.11 Reconstructed surfaces of structural steel corroded in offshore atmosphere based on the stochastic field model of corrosion depth (SFCD) and the random distribution model of corrosion pits (RDCP)
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