Predicting the Residual Strength of Oil and Gas Pipelines Using the GA-BP Neural Network
Authors: Wang Zhanhui , Long Mengzhao , Duan Wenlong , Wang Aimin , Li Xiaojun 
Published: May 2024

Background:
Most NN (neural network) research only conducted qualitative analysis,
analyzing its accuracy, with certain limitations, without studying its NN model, error convergence
process, and pressure ratio. There is relatively limited research on the application of
NN optimized by GA (genetic algorithm) to oil and gas pipelines; Moreover, the residual
strength evaluation of GA-BP NN (genetic algorithm backpropagation neural network) has the
advantages of high global search ability, efficiency not limited by constant differences, and the
use of probability search instead of path search, which has a wide application prospect.

Objective:
Using MATLAB software, establish GA-BP NN models under five residual strength
evaluation criteria and introduce the relative error of the parameters and the pressure ratio to
comprehensively analyze the accuracy and applicability of GA-BP NN.

Methods:
Firstly, using MATLAB software, a GA-BP NN model was established based on five
residual strength evaluation criteria: ASME B31G Modified, BS7910, PCORRC, DNV RP
F101, and SHELL92, by changing five factors that affect the residual strength of oil and gas
pipelines: diameter, wall thickness, yield strength, corrosion length, and corrosion depth; Second,
the trained GA-BP NN model is used to predict the residual strength of the same set of evaluation
criteria test data and compared with the calculation results of five residual strength evaluation
criteria. The relative error of the parameters and pressure ratio are introduced to comprehensively
analyze the accuracy and applicability of the GA-BP NN.

Results:
The error convergence time of the BP NN is longer, and the optimized GA-BP NN has
a shorter convergence time. By comparing the convergence training times of different models, it
can be obtained that for the five sets of residual strength evaluation criteria of ASME B31G
Modified, BS7910, PCORRC, DNV RP F101, and SHELL92, the optimized GA-BP NN model
significantly reduces convergence training times, significantly improves convergence speed, and
further evolves the system performance. From the relative error and local magnification, it can
be seen that for the ASME B31G Modified evaluation criteria, the maximum relative error of
the BP NN model is 1.4008%, and the maximum relative error of the GA-BP NN model is
0.7304%. For the evaluation criterion BS7910, the maximum relative error of the BP NN model
is 0.7239%, and the maximum relative error of the GA-BP NN model is 0.5242%; for the evaluation
criteria of DNV RP F101, the maximum relative error of the BP NN model is 1.1260%,
and the maximum relative error of the GA-BP NN model is 0.4810%; for the PCORRC evaluation
criteria, the maximum relative, error and the maximum relative error of the GA-BP NN
model is 0.8004%; for the SHELL92 evaluation criterion, the maximum relative error of the BP
NN model is 1.2292%, and the maximum relative error of the GA-BP NN model is 0.8346%.
The results of the GA-BP NN prediction are closer to the results of the calculation of the five
residual strength evaluation criteria, and the prediction effect is better, which can more accurately
predict the residual strength of the oil and gas pipelines. Based on the pressure ratio, the average
pressure ratio A of the BP NN model under the five residual strength criteria is 1.0004, and
the average pressure ratio A of the GA-BP NN model is 0.9998. The results predicted by the
GA-BP NN model are more accurate.

Conclusion:
These findings have crucial implications for the forecast of the residual strength of
corrosive oil and gas pipelines.