Predicting pipeline burst pressures with machine learning models

Phan, Hieu Chi; Dhar, Ashutosh Sutra

doi:10.1016/j.ijpvp.2021.104384

Cited by 27 publications

(7 citation statements)

References 16 publications

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“…The basic unit of a biological network is called a neuron (or nerve cell). Most neurons have four basic components: a cell body (or soma), an axon, dendrites and synapses which receive input signals from other neurons or from the outside and combines them to perform a generally nonlinear operation on the received data [20]. Figure 1 shows a typical biological neuron.…”

Section: Basic Conceptsmentioning

confidence: 99%

“…An ANN that consists of interconnected processing units can be thought as a highly simplified model of a biological neural network. Application has demonstrated that ANNs can be very powerful tools for solving complicated engineering problems [20]. Instead of complex rules and mathematical formulations, ANNs are capable of learning key information patterns within a multiinformation domain.…”

Section: Architecture Of Annmentioning

confidence: 99%

“…Solve the cost function equation to minimize the model error. Different computational tools can be used to minimize the model cost function, including MATLAB machine learning toolbox [20], Microsoft Excel Solver, and other proprietary computer software. The error calculations are iterated through training samples until the convergence is reached.…”

Section: Machine Learning Proceduresmentioning

confidence: 99%

“…From detailed comparisons, Kumar [15] and Lee et al [16] showed that ANNs have superior performance and higher accuracy than the traditional regression and that both methods are comparable if a linear relationship exists between dependent and independent variables. Recently, the deep learning or ANN approaches were successfully applied to predict the burst strength for pressure vessels or pipelines with or without defects [17][18][19][20] and for metal weld joints [21][22].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Machine Learning Models of Burst Strength for Defect-Free Pipelines

Zhu

Johnson

Sindelar

et al. 2022

Volume 4B: Materials and Fabrication

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Burst strength of line pipes is essential to pipeline design and integrity management. The simple Barlow equation with the ultimate tensile strength (UTS) was often used to estimate burst strength of line pipes. To consider the plastic flow effect of ductile steels, Zhu and Leis (2006, IJPVP) developed an average shear stress yield criterion and obtained the Zhu-Leis solution of burst strength for defect-free pipelines in term of UTS and strain hardening exponent, n, of materials. The Zhu-Leis solution was validated by more than 100 burst tests for various pipeline steels. The Zhu-Leis solution, when normalized by the Barlow strength, is a function of strain hardening rate, n, only, while the experimentally measured burst strength, when normalized by the Barlow strength, is a strong function of n and a weak function of UTS and pipe diameter to thickness ratio D/t. Due to difficulty of three-parameter regressions, this paper adopts the machine learning technology to develop alternative models of burst strength based on a large database of full-scale burst tests. In comparing to the regression, the machine learning method works well for both single and multiple parameters by introducing an artificial neural network (ANN), activation functions and learning algorithm for the network to learn and make predictions. Three ANN models were developed for predicting the burst strength of defect-free pipelines. Model 1 has one input variable and one hidden layer with three neurons; Model 2 has three input variables and one hidden layer with five neurons; and Model 3 has three input variables and two hidden layers with three neurons for the first hidden layer and two neurons for the second hidden layer. Those ANN models were then validated by the full-scale test data and evaluated through comparison with the Zhu-Leis solution and the linear regression result. On this basis, the best ANN model is recommended.

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Section: Basic Conceptsmentioning

confidence: 99%

Section: Architecture Of Annmentioning

confidence: 99%

Section: Machine Learning Proceduresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Machine Learning Models of Burst Strength for Defect-Free Pipelines

Zhu

Johnson

Sindelar

et al. 2022

Volume 4B: Materials and Fabrication

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“…However, finding a proper architecture for the NNet requires careful hyper-parametric tuning 29 or advanced techniques. 30 The user must also possess advanced knowledge and skills.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Neural Network architecture and derivation of closed-form equation to predict ultimate load of functionally graded material plate

Duong

Phan

2023

Advances in Mechanical Engineering

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Functionally Graded Material (FGM) plate is a complicated structure with complex allocation of spatially changing proportions of ceramic and metal within the matter. Various analytical and numerical methods have been applied with a view to evaluating the critical load of FGM plate. However, these conventional methods struggle when the computational complexity is significant, which represents an obstacle to incorporation with other advanced techniques where computational power is required (e.g. optimization or random simulations). The Neural Network (NNet) model has been successfully applied to resolve this issue. However, the conventional NNet requires proper configuration to take advantage of the model, and thus, careful parameter tuning is required. Furthermore, the NNet is typically a “black box,” where the prediction mechanism is hidden. This paper establishes an optimized architecture for NNet, with parametric study of the model’s hyperparameters. Variance propagation is also applied to observe the variation of the model’s performance on random sub-databases splintered from the database. To this end, the explicit expression of the trained NNet model is provided after mathematically deploying the hidden algebra behind an NNet prediction. The developed model has very promising evaluation metrics: R2, MAE, and RMSE on the test set are 0.999925, 0.067516, and 0.146438, respectively.

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Critical Buckling Load Evaluation of Functionally Graded Material Plate Using Gaussian Process Regression

Duong

Phan

2022

Advances in Engineering Research and Application

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Predicting pipeline burst pressures with machine learning models

Cited by 27 publications

References 16 publications

Machine Learning Models of Burst Strength for Defect-Free Pipelines

Machine Learning Models of Burst Strength for Defect-Free Pipelines

Optimization of Neural Network architecture and derivation of closed-form equation to predict ultimate load of functionally graded material plate

Critical Buckling Load Evaluation of Functionally Graded Material Plate Using Gaussian Process Regression

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