Ren Meng, Zhang Yanmei, Fan Mu, Xiao Zhongmin
State Key Laboratory of Mechanics and Control for Aerospace Structures, College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210010, China.
College of Aerospace Engineering, Chong Qing University, Chongqing 400044, China.
Materials (Basel). 2024 Jul 1;17(13):3237. doi: 10.3390/ma17133237.
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.
埋地管道应用广泛，因此有必要对其断裂特性进行分析研究。管道上腐蚀缺陷的位置在物料输送过程中产生的内压影响下更容易发生断裂。在公开文献中，已经对腐蚀管道的失效压力或剩余强度进行了大量研究。在此基础上，本研究对地震荷载作用下具有腐蚀区域的埋地管道进行断裂分析。采用扩展有限元法对地震荷载作用下的埋地管道进行建模分析，发现腐蚀区域裂纹圆周角接近5°时，裂纹尖端的应力值最大。比较了不同荷载作用下管道腐蚀区裂纹尖端应力场的变化。基于BP算法，建立了能够预测管道裂纹尖端应力场的神经网络模型。利用数值模型数据对神经网络进行训练，构建了预测误差小于10%的预测模型。利用BP神经网络模型进一步研究了裂纹尖端特性，确定尖端应力波动范围在450MPa至500MPa之间。基于遗传算法对神经网络模型进行优化，解决了收敛困难的问题，提高了预测精度。根据预测结果发现，当内压增加时，腐蚀深度将显著影响裂纹尖端应力场。优化后的神经网络最大误差为5.32%。将优化后的神经网络模型的计算数据与其他模型的计算数据进行比较，确定遗传算法-反向传播神经网络（GA-BPNN）在本研究问题中具有更好的适应性。
