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2017 2nd International Conference on Artificial Intelligence: Techniques and Applications (AITA 2017) ISBN: 978-1-60595-491-2

Failure Prediction of Underground Pipeline

Based on Artificial Neural Network

Yan-hua CHEN

1,*

, Jian-nan LI

2

, Kai LIAN

3

, Qing-jie ZHU

4

and Yi-liang LIU

5 1

Earthquake Engineering Research Center, North China University of Science and Technology, Tangshan 063210, Hebei Province, China

2

School of Mechanical Engineering, North China University of Science and Technology, Tangshan 063210, Hebei Province, China

3

CSCEC Road and Bridge Group Co., LTD, Shijiazhuang 050001, Hebei Province, China

4

School of Petroleum Engineering, Changzhou University, Changzhou 213016, Jiangsu Province, China

5

Department of Civil Engineering, North China Institute of Aerospace Technology, Langfang 065000, Hebei Province, China

*Corresponding author

Keywords: Failure prediction, Pipeline, Non-uniform settlement soil, MATLAB.

Abstract. Artificial neural network has become a useful tool for many engineering problems. For the prediction and analysis of underground pipeline failure, an ANN model is established as basis of the data of buried pipelines in non-uniform settlement soil. Therefore, the failure of underground pipeline in non-uniform settlement soil is treated as a nonlinear function with several variables. Six influence factors, such as buried depth, wall thickness, pipe diameter, precipitation level, soil modulus of elasticity, and soil density, are considered in this ANN model. The ANN model is a back propagation (BP) network, and model structure is designed based on MATLAB, in which Neuron number in hidden layer and calculating function are selected. Finally, the accuracy of this ANN model and predictive results are investigated, and some suggestions are offered for the protection of underground pipeline in non-uniform settlement soil.

Introduction

Underground pipeline is obvious frangible to site condition that make the relationship between pipeline and influencing factors very complex and nonlinear [1-4]. Failure probability of underground pipeline in the site condition of non-uniform settlement soil affected by many factors, such as buried depth, wall thickness, pipe diameter, precipitation level, soil modulus of elasticity, soil density, and so on [5-7]. Although there are many researches on the theory and experiment of buried pipeline damage, but most predictive models for pipeline failure is based on statistics [8]. Therefore, we construct a failure prediction model of underground pipeline based on ANN to represent the complex fuzzy nonlinear relation [9-11].

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Data of Underground Pipeline

[image:2.612.83.532.189.494.2]

From finite element calculation, it was found that the failure of underground pipeline in non-uniform settlement soil is affected by buried depth, wall thickness, pipe diameter, precipitation level, soil modulus of elasticity, and soil density. The data is shown in table 1, and six influence factors are concluded. Those six influence factors can be treated as a six-dimension vector that is the input variables in MATLAB. The failure probability is represented by axial stress that is treated as a one-dimension vector, and it is the output target variable in MATLAB.

Table 1. Training data from finite element calculation.

No. buried depth /m (x1)

wall thickness

/m (x2)

pipe diameter /m (x3)

precipitation level/m (x4)

modulus of elasticity/ MPa (x5)

soil density /kg/m3(x6)

axial stress /Mpa (σ)

1 2 0.02 0.8 0 3.56 1750 13.434

2 6 0.02 0.8 0 3.56 1750 30.971

3 10 0.02 0.8 0 3.56 1750 32.814

4 4 0.02 0.6 0 3.56 1750 16.370

5 4 0.02 1.0 0 3.56 1750 34.681

6 4 0.01 0.8 0 3.56 1750 54.212

7 4 0.04 0.8 0 3.56 1750 7.551

8 2 0.02 0.8 0.2 3.56 1750 6.641

9 2 0.02 0.8 0.4 3.56 1750 7.429

10 2 0.02 0.8 0.6 3.56 1750 8.184

11 2 0.02 0.8 1 3.56 1750 9.648

12 2 0.02 0.8 1.2 3.56 1750 10.362

13 2 0.02 0.8 1.4 3.56 1750 11.265

14 2 0.02 0.8 1.6 3.56 1750 12.066

15 2 0.02 0.8 1.7 3.56 1750 14.298

16 2 0.02 0.8 1.8 3.56 1750 15.902

17 2 0.02 0.8 1.9 3.56 1750 14.747

18 2 0.02 0.8 2.1 3.56 1750 14.590

19 2 0.02 0.8 2.2 3.56 1750 16.532

20 2 0.02 0.8 2.3 3.56 1750 15.081

21 2 0.02 0.8 2.4 3.56 1750 13.511

22 4 0.02 0.8 0 3.86 2600 38.052

According to the data in table 1, failure of underground pipeline can be computed based on this artificial neural network model. Obviously, the input variables is a 6×22 vector, and the output target variable is a one-dimensional vector containing 22 numbers.

ANN Model

As a BP network, the predictive model of underground pipeline failure with one hidden layer is expressed as,

(

)

{ }

ij

j =W2,0+W2,1∗W10 x

σ

(1)

In which,

σj=the jth value of axial stress,

W2,0=the weights matrix between output vector and input vector,

W2,1=the weights matrix between output vector and hidden layer vector,

W2,0=the weights matrix between hidden layer vector and input vector,

xij= the jth value of the ith input vector.

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model during training. Each input vector should be a normalized vector, and a 6×22 input vector is created for table 1.

The normalization is written as,

) ( ) (

) (

i i

i i

i

x Min x Max

x Min x x

− −

= (2)

After normalization, we can begin the artificial neural network training.

Training Function

The selection of training function is the key of ANN training, if the network error is set as 0.001, the results for this application example data are shown in table 2.

Table 2. Training results with different training function.

Training fuction Traindx Trainlm Traingd

Iterative number >1000 87 >1000

[image:3.612.164.451.312.485.2]

It can be found from table 2 that the best function is Trainlm, and the iterative calculation number is 87, this is shown as figure 1. For other training functions, such as Traindx and Traingd, the training results cannot reach the goal with error of 0.001 after 1000 steps’ iterative calculation.

Figure 1. Training of Trainlm.

In figure 1, convergence speed is very rapid within 10 steps, like a straight line with a steep slope. After 10 times iteration, convergence speed becomes slowly and reaches the error target with iterative number 87.

Neuron Number in Hidden Layer

The precision and convergence speed of ANN model are affected by neuron number during training, this is shown in table 3.

Table 3. Iteration number and error with different neuron cell number.

Neuron number 10 11 12 13 14 15

Iterative number 115 105 106 84 89 90

Network error 0.0312 0.0278 0.0264 0.0152 0.0178 0.0198

From table 3, the neuron number 13 has the fewest iterative number and the least network error. The iterative number is 84 and the network error is 0.0152. The most iterative number is 115 with neuron number 10, also the maximal network error 0.0312. This shows that more is not better, less is also not better for neuron number.

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[image:4.612.183.430.100.232.2]

and convergence of the model reach the target. Up to now, the modeling of failure prediction for underground pipeline has finished.

Figure 2. Network error with training function Trainlm.

Results Analysis

The results of weights matrix are expressed as,

] 5306 . 2 7961 . 1 2313 . 0 7581 . 0 5575 . 0 2146 . 0 [

20 = − −

W (3)

] 3374 . 0 5026 . 1 4227 . 0 8091 . 0 8549 . 1 4119 . 1 9233 . 0 3789 . 0 4562 . 0 7874 . 0 9349 . 0 0579 . 1 0622 . 0 [

21= − − − − − − −

W (4)                             − − − − − − − − − − − − − = 5348 . 0 6929 . 0 9801 . 0 2074 . 0 4739 . 0 6172 . 0 6172 . 0 2422 . 1 2851 . 0 4123 . 0 7252 . 0 1793 . 0 0769 . 0 6651 . 0 3003 . 0 6717 . 0 4289 . 0 3314 . 0 8689 . 0 0035 . 0 0002 . 0 1663 . 0 2831 . 0 3685 . 1 9284 . 1 5273 . 0 1492 . 0 2258 . 0 6547 . 0 0857 . 0 3506 . 1 3326 . 0 2423 . 0 3730 . 0 2842 . 0 4008 . 1 7919 . 0 5006 . 0 0339 . 1 1597 . 0 2682 . 1 3451 . 0 1907 . 0 9153 . 0 0022 . 1 5827 . 0 8447 . 0 7329 . 0 5576 . 0 0131 . 0 4807 . 0 5313 . 0 0473 . 0 6585 . 0 7859 . 0 1732 . 0 4927 . 0 3143 . 0 1122 . 0 3399 . 1 2248 . 0 9959 . 0 9993 . 0 1174 . 0 9079 . 0 4530 . 0 1124 . 0 3711 . 0 9377 . 0 7702 . 0 0586 . 1 0834 . 0 7568 . 0 3311 . 0 2906 . 0 5419 . 0 5379 . 0 6956 . 0 10

W (5)

[image:4.612.94.365.382.501.2]

The predictive results of this model are shown in table 4.

Table 4. Error of predictive results.

No. axial stress

/Mpa (σ)

prective axial

stress/Mpa (σ) Error/(%)

1 25.723 26.315 2.3

2 32.290 32.338 1.4

3 7.550 7.912 4.8

4 61.671 59.634 3.2

5 13.074 13.453 2.9

6 8.945 9.348 4.5

7 12.754 12.333 3.3

8 12.467 12.779 2.5

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Summary

A predictive model for underground pipeline is constructed with double parallel feed-forward neutral network. Neuron number in hidden layer and training function are investigated based on MATLAB. Through compare the predictive results with eight groups’ sample data, the results meet well with actual axial stress values. Therefore, ANN is a useful tool for predicting the failure of underground pipeline in non-uniform settlement soil.

Acknowledgement

This research was financially supported by the China National Science Foundation (51378172), and Science Foundation of Hebei Province (E2014209089).

References

[1] E.D. Guo, S.Z. Yu, W. Wu, Seismic damage analysis of buried pipeline engineering, Earthquake Engineering and Engineering Vibration, 26(2006):180-183.

[2] W.M. Ryan, M.O. Joan, S. Daniel, Assessing reliability in energy supply systems, Energy policy, 35(2007)2151-2162.

[3] H.Y. Guo, W.Y. Dong, M. Lou, Vortex-induced vibration testing and fatigue life analysis of practical risers conveying fluid, Periodical of Ocean University of China, 38(2007)503-507.

[4] Z.P. Wen, M.T. Gao, F.X. Zhao, Seismic vulnerability estimation of the building considering seismic environment and local site condition, ACTA Seismologica Sinica, 28(2006)277-283.

[5] Q.J. Zhu, Y.L. Liu, L.Z. Jiang, X.Y. Zhang, Analysis of buried pipeline damage affected by pipe- soil friction and pipe radius, Earthquake Engineering and Engineering Vibration, 26(2006)197-199.

[6] Q.J. Zhu, Y.H. Chen, L.Z. Jiang, Influences of site and faults on damage of buried pipelines, Rock and Soil Mechanics, 29(2008)2392-2396.

[7] Q.J. Zhu, Y.P. Su, Influence of basement rock condition on earthquake affecting coefficient, Chinese Journal of Geotechnical Engineering, 26(2004)198-201.

[8] Z.C. Pei, Y.X. Liu, X.Q. Wang, P. Liu, Method of earthquake damage prediction of pipeline, Northwestern Seismological Journal, 27(2005)186-189.

[9] Y.H. Guo, B.G. Wang, Two types of new neural networks and their applications to safety assessment, China Safety Science Journal, 18(2008)29-33.

[10] X. Yu, C. Ye, L. Xiang, Application of artificial neural network in the diagnostic system of osteoporosis, Neurocomputing, 214(2016)376-381.

[11] M. Peer, M. Mahdyarfar, T. Mohammadi, Evaluation of mathematical model using experimental data and artificial neural network for prediction of gas separation, Journal of Natural Gas Chemistry, 17(2008)135-141.

[12] Z.B. Sheng, BP Neural Network Principles and Matlab Simulation, Journal of Weinan Teachers University, 23(2008)65-67.

[13]M. Cao, M.H. Yang, G.M. Zeng, Simulation based on artificial neural network for SBBR shortcut nitrification treatment, China Environmental Science, 28(2008)694-698.

[14] M.Buscema, V.Consonni, D.Ballabio, et al, K-CM: A new artificial neural network, Chemome- trics and Intelligent Laboratory Systems, 138 (2014) 110-119.

[15] T.C. Mccandless, S.E. Haupt, A regime-dependent artificial neural network technique for short- range solar irradiance forecasting, Renewable Energy, 89(2016)351-359.

Figure

Table 1. Training data from finite element calculation.
Figure 1. Training of Trainlm.
Figure 2. Network error with training function Trainlm.

References

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