The effectiveness of ensemble-neural network techniques to predict peak uplift resistance of buried pipes in reinforced sand

Zeng, Jie and Asteris, Panagiotis G. and Mamou, Anna P. and Mohammed, Ahmed Salih and Golias, Emmanuil A. and Armaghani, Danial Jahed and Faizi, Koohyar and Hasanipanah, Mahdi (2021) The effectiveness of ensemble-neural network techniques to predict peak uplift resistance of buried pipes in reinforced sand. Applied Sciences, 11 (3). p. 908. ISSN 2076-3417

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Official URL: https://doi.org/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. Multi-layer 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 pre-dicted 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. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Item Type: Article
Uncontrolled Keywords: maximum uplift resistance; pipeline; ensemble neural network; neural network; reinforced soil
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 21 Apr 2021 03:48
Last Modified: 21 Apr 2021 03:48
URI: http://eprints.um.edu.my/id/eprint/25879

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