The Effectiveness of Ensemble-Neural Network Techniques to Predict Peak Uplift Resistance of Buried Pipes in Reinforced Sand

Jie Zeng,Panagiotis G Asteris,Ahmed Salih Mohammed,Emmanuil A Golias,Danial Jahed Armaghani,Mahdi Hasanipanah,Anna P Mamou,Koohyar Faizi

doi:10.3390/app11030908

Abstract

Buried pipes are extensively used for oil transportation from offshore platforms. Under unfavorable loading combinations, the pipe’s uplift resistance may be exceeded, which may result in excessive deformations and significant disruptions. This paper presents findings from a series of small-scale tests performed on pipes buried in geogrid-reinforced sands, with the measured peak uplift resistance being used to calibrate advanced numerical models employing neural networks. Multilayer perceptron (MLP) and Radial Basis Function (RBF) primary structure types have been used to train two neural network models, which were then further developed using bagging and boosting ensemble techniques. Correlation coefficients in excess of 0.954 between the measured and predicted peak uplift resistance have been achieved. The results show that the design of pipelines can be significantly improved using the proposed novel, reliable and robust soft computing models.

Highlights

Buried pipes are extensively used to transport oil and gas from offshore platforms.Under unfavorable loading combinations, the pipe’s uplift resistance may be exceeded and may lead to excessive deformations [1,2,3,4,5] and significant disruptions
This paper aims to perform a series of small-scale tests on pipes buried in geogridreinforced sands and the measured peak uplift resistance was used to calibrate advanced numerical models employing neural networks
The results show that the peak uplift resistance increased with increasing pipe diameter

Summary

Introduction

Buried pipes are extensively used to transport oil and gas from offshore platforms.Under unfavorable loading combinations, the pipe’s uplift resistance may be exceeded and may lead to excessive deformations [1,2,3,4,5] and significant disruptions. The peak uplift resistance of buried pipes is typically determined using a combination of laboratory, field tests and numerical modelling [1,2,3,4,5,6,7,8,9,10]. Laboratory-scale and field tests for the prediction of the peak uplift resistance of buried pipes are time-consuming and costly to perform [7] and in this context advanced numerical modelling may offer a viable alternative. Both MLP and RBF comprise non-linear universal approximators with feed-forward structures [61,62]

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Conclusion