Numerical Simulation and ANN Prediction of Crack Problems within Corrosion Defects

Abstract

Buried pipelines are widely used, so it is necessary to analyze and study their fracture characteristics. The locations of corrosion defects on the pipe are more susceptible to fracture under the influence of internal pressure generated during material transportation. In the open literature, a large number of studies have been conducted on the failure pressure or residual strength of corroded pipelines. On this basis, this study conducts a fracture analysis on buried pipelines with corrosion areas under seismic loads. The extended finite element method was used to model and analyze the buried pipeline under seismic load, and it was found that the stress value at the crack tip was maximum when the circumferential angle of the crack was near 5° in the corrosion area. The changes in the stress field at the crack tip in the corrosion zone of the pipeline under different loads were compared. Based on the BP algorithm, a neural network model that can predict the stress field at the pipe crack tip is established. The neural network is trained using numerical model data, and a prediction model with a prediction error of less than 10% is constructed. The crack tip characteristics were further studied using the BP neural network model, and it was determined that the tip stress fluctuation range is between 450 MPa and 500 MPa. The neural network model is optimized based on the GA algorithm, which solves the problem of convergence difficulties and improves the prediction accuracy. According to the prediction results, it is found that when the internal pressure increases, the corrosion depth will significantly affect the crack tip stress field. The maximum error of the optimized neural network is 5.32%. The calculation data of the optimized neural network model were compared with the calculation data of other models, and it was determined that GA-BPNN has better adaptability in this research problem.

Keywords: buried pipeline; corrosion defect; crack tip; extended finite element method; neural network prediction.

Figure 1

Schematic diagram of normal and tangential coordinates of smooth cracks.

Figure 2

Distance functions $\varphi$ and $\psi$ for non-planar crack symbols.

Figure 3

Typical traction-separation response [26].

Figure 4

Crack location in corrosion defect, (a) the whole pipeline, (b) angle of crack distribution.

Figure 5

Seismic wave acceleration-time curve.

Figure 6

Modeling and simulation process.

Figure 7

Corrosion defect meshing.

Figure 8

BP neural network structure diagram.

Figure 9

The relationship curve between MAPE and hidden layer. (a) Relationship curve between MAPE and hidden layer nodes. (b) Relationship curve between MAPE and number of hidden layers.

Figure 10

GA algorithm fitness function.

Figure 11

Relationship between crack circumferential distribution and crack tip stress.

Figure 12

Relationship between crack circumferential distribution and crack tip strain.

Figure 13

Relationship between crack tip stress and internal pressure under single load.

Figure 14

Relationship between crack tip stress and internal pressure under combined loads.

Figure 15

MSE curve during BPNN training process.

Figure 16

Crack tip stress changes with corrosion depth and pipeline internal pressure with BPNN prediction.

Figure 17

MSE curve during GA-BPNN training process.

Figure 18

Comparison of calculation errors of prediction models for crack tip stress field.

Figure 19

Crack tip stress changes with corrosion depth and pipeline internal pressure.

Figure 20

The impact of training data volume on prediction results.