This chapter uses artificial neural network to predict the final dent depth (reround depth) after pressurisation and the stress concentration factor in the dent. It is also used predict the maximum strain in pipeline dents. The rationale behind this is to develop a means of predicting the reround depth, SCF and strain without having to run expensive experimental program and extensive finite element analysis. The parameters that affect the rerounding, SCF and maximum strain as discussed in chapter 3, 4 and 5 respectively are used to train the ANN to be able to predict the reround depth, SCF and strain. Two separate models are trained to predict dome and bar dents respectively for each result. The networks have one hidden layer as this is sufficient enough to perform the analysis. Different number of processing elements are used for each result to determine the network that best give a good fit with minimal errors. The Levenberg-Marquardt function is used as it is the most common training function and has be proven to give good fits. The two transfer functions (logarithmic sigmoid and hyperbolic tangent) is also investigated to determine the one that gives the best fit. Each network has a model architecture consisting of three (3) layers; the input, hidden and output layers. The input layer consist of four variables which include the diameter to thickness ratio of the pipe (D/t) representing the pipe geometric
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property, dent depth (%d/D) and Length to diameter ratio (L/D) representing the dent geometric properties. The 4th input variable is the yield strength of the pipe representing
the pipe material property. The hidden layer has a variable number of processing elements ranging between 5 and 15. The numbers are alternated with the two different transfer functions to see the model that gives the best performance. The output predicts the reround dent depth, SCF and strain after spring back. From the results the following can be concluded:
1. For the dome model rerounding result, the network architecture that gave the best performance is the network with 10 processing elements and a tansig activation function. The network gave the minimal error with a coefficient of variation(COV) 4.1 %. The ANN predicted reround depth showed a good correlation with the FEA reround depth with an R-square value of 0.99.
2. For the bar model rerounding result, the network that gives the best performance is the network with 15 hidden processing elements with tansig activation function. The network has a mean square error value of 5.01E-06 and a COV of 3.2%.
3. For the dome model SCF result, the logsig activation function gave better performance with the best network having 5 hidden processing elements and coefficient of variation (COV) of 4.7
4. For the Bar model SCF result, the network that has the best performance is the network with 5 processing elements and a logsig transfer function. This network has a mean squared error value of 8.86E-05. The linear regression gives good fit with an R-square value of 0.99 and a coefficient of variation of 3.5%
5. For the dome model strain result, the logsig function gave the best performance with 10 processing elements and a COV of 4.9%
6. For the bar model strain result, the network with the overall best performance has 15 processing element and logsig activation function. and a COV of 8.8%. Altogether, the ANN-based method have shown a good degree of reliability with low coefficient of variation for all models. These low coefficient of variation increases the confidence in the use of the ANN-based formula
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CHAPTER 7
PROPOSED PROCEDURE FOR DENT ASSESSMENT
Previous chapters have the described past and current methods for evaluating dent severity. It described the strain-based assessment and the fatigue assessment. The strain-based assessment was proposed by ASME B31.8 and equations 5-4 and 5-5 was given in order to find the total strain in the dent. Fatigue assessment, however, has been done by various researchers to calculate the fatigue life of pipelines with dent. This is done by extracting SCF from experimental data or finite element analysis and using it with an SN curve in order to determine the fatigue life. Experiments are expensive and finite element analysis are time consuming. This chapter proposes the use of the artificial neural network for both strain based assessment and fatigue assessment. This method eliminates the process of having to run an expensive experimental program or running an extensive finite element study in order to calculate the SCF used in fatigue assessment and strain used in strain based assessment. The application of ANN is new and unique in dent assessment. Apart from the fact that it will save time and money, it has a very good accuracy in predicting the SCF and maximum strain. What makes the ANN application unique is its ability to learn. Unlike other methods such as curve fitting, ANN studies patterns and trends through the data provided. The accuracy of the ANN depends on the number of training data. A wider range of data is recommended when using ANN. The more the training data, the more the accuracy of the predicted results. The ANN application in dent assessment is easy and straightforward. All that is required is the need to know the values of the input parameters. Once the input parameters are known and inputted into the ANN model, it predicts the results with good accuracy
A large data base of SCFs was generated from a prior finite element study which analysed the effect of various parameters on the fatigue life of pipeline with dents. A wider range of pipe grades, pipe geometry, dent geometry and pressure range was analysed in order to give the large database of SCFs. This was then used to train the
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ANN in order to predict the SCF. Similarly, ANN is used to predict the maximum strain in the pipe. It also considers the effective various parameters on the prediction of strain. The ANN is also used to predict the rerounding depth after pressurisation. The strain assessment includes the effect of pipe grade which was not included in the ASME B31.8 code. A flow chart showing this procedure is seen in figure 7-1 and each component of the flow chart is further explained below
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Figure 7-1 Flow chart for Dent assessment
In previous chapters, the various parameters that influence the fatigue and maximum strain was discussed , the parameters include, dent geometry, pipe geometry, pipe grade and mean pressure. The dent geometry factor is represented as the dent depth (Ho) and the ratio of the dent length to the diameter of the pipe (L/D). The pipe
geometry is represented as a ratio of the pipe diameter to the thickness of the pipe (D/t). Similarly, the pipe grade is represented by the specified minimum yield stress(SMYS). Finally the pressure range is represented by the mean pressure in the pipe.
In order to carry out a dent assessment using the proposed methods, these parameters needs to be known. It is important to note that the dent depth considered in the proposed methods is the dent depth after spring back (Ho). However, an ANN-based
formula in chapter 6 show the relationship between the spring back dent depth and the reround dent depth (Hr).