A Convolutional Neural Network-Based Corrosion Damage Determination Method for Localized Random Pitting Steel Columns
Abstract
:1. Introduction
2. Pitting Model
2.1. Multi-Parameter Localized Random Pitting Model
2.1.1. Depth and Size
2.1.2. Location
2.2. Finite Element Modeling
3. Mechanical Properties
3.1. Experiment and FEM Analysis of Non-Pitting Specimens
3.2. Influence of Local Random Pitting Rate
4. CNN for Pitting Detection
4.1. Critical Pitting Corrosion Rate
4.2. Dataset
4.3. CNN
5. Results and Discussion
6. Conclusions
- (1)
- The accuracy of the numerical model. The multi-parameter localized random pitting numerical model established herein can fully express the randomness of pitting pits in shape and location while ensuring the reasonable shape parameters and location coordinates of pitting pits, which can fully describe the realistic pitting situation of steel columns.
- (2)
- Statistical patterns of bearing capacity of localized random pitting corrosion in steel columns. With low dispersion, the ultimate strength distribution of localized random pitting steel columns has good statistical significance. For steel columns with one end fixed and one end hinged, the ultimate strength decreases linearly with the increase in the pitting corrosion rate when pitting occurs in regions 1–4; the ultimate strength shows a secondary parabolic downward trend as the pitting corrosion rate increases when pitting occurs in region 5; the bearing capacity of the steel column first remains constant and then shows a linear decrease with the increase in corrosion rate when pitting occurs in regions 6–8.
- (3)
- A pitting detection neural network. This study establishes a convolutional neural network to determine whether a steel component is damaged or not by inputting the first six vibration modes. The network has a high detection accuracy, which meets the practical engineering requirements and proves that it is of great theoretical significance and actual application value to determine the damage to a steel component by the convolutional neural network.
- (4)
- The detection system of random pitting corrosion. Based on the numerical model of random pitting, the critical corrosion rate is defined by studying the ultimate strength of pitting components, and then vibration modes are input to train the convolutional neural network for damage determination; thus, a localized random pitting damage determination network with reasonable accuracy is worked out.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Slenderness Ratio ζ | Length L/mm | Thickness T/mm | Diameter d/mm | Section Length s/mm |
---|---|---|---|---|
20 | 1309 | 10 | 168.3 | L/8 |
25 | 1636 | |||
30 | 1964 | |||
35 | 2291 |
Parameter | Unit | Illustration |
---|---|---|
mm | Length of specimen | |
mm | Diameter of specimen | |
mm | Thickness | |
% | Number of regions | |
% | Corrosion rate | |
t | year | Corrosion time |
- | Initial pit number |
t | |
---|---|
7 | 0.02,0.04,0.06,0.08 |
9 | |
11 | |
15 | 0.10,0.12,0.14,0.16,0.18 |
20 | |
25 | |
30 | |
35 | |
40 |
2.38 × 105 | 0.3 | 8.104 × 10−9 | 460 | 555 |
Experimental Result | Qin’s FE Result | FEM Result | Error 1 | Error 2 | |
---|---|---|---|---|---|
12.22 | 2424 | 2384.3 | 2446.1 | 0.90% | 2.60% |
25 | - | 2315.6 | 2237.2 | - | 3.40% |
Layer Name | Number of Filters | Size or Dropout Rate | Output Size |
---|---|---|---|
Input layer | - | - | 2 × 9 × 6 |
Convolution layer 1 | 10 | 3 × 2 | 10 × 7 × 5 |
Max-pooling layer 1 | - | 3 × 2 | 10 × 5 × 4 |
Convolution layer 2 | 20 | 2 × 2 | 20 × 4 × 3 |
Max-pooling layer 2 | - | 2 × 2 | 20 × 3 × 2 |
Fully connected layer 1 | - | - | 1 × 120 |
Dropout | - | 0.3 | - |
Fully connected layer 2 | - | - | 1 × 40 |
Fully connected layer 3 | - | - | 1 × 10 |
Output layer | - | - | 1 × 2 |
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Jiang, X.; Qi, H.; Qiang, X.; Zhao, B.; Dong, H. A Convolutional Neural Network-Based Corrosion Damage Determination Method for Localized Random Pitting Steel Columns. Appl. Sci. 2023, 13, 8883. https://doi.org/10.3390/app13158883
Jiang X, Qi H, Qiang X, Zhao B, Dong H. A Convolutional Neural Network-Based Corrosion Damage Determination Method for Localized Random Pitting Steel Columns. Applied Sciences. 2023; 13(15):8883. https://doi.org/10.3390/app13158883
Chicago/Turabian StyleJiang, Xu, Hao Qi, Xuhong Qiang, Bosen Zhao, and Hao Dong. 2023. "A Convolutional Neural Network-Based Corrosion Damage Determination Method for Localized Random Pitting Steel Columns" Applied Sciences 13, no. 15: 8883. https://doi.org/10.3390/app13158883
APA StyleJiang, X., Qi, H., Qiang, X., Zhao, B., & Dong, H. (2023). A Convolutional Neural Network-Based Corrosion Damage Determination Method for Localized Random Pitting Steel Columns. Applied Sciences, 13(15), 8883. https://doi.org/10.3390/app13158883