Document Type
Article
Publication Date
1-1-2021
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.
Keywords
maximum uplift resistance, pipeline, ensemble neural network, neural network, reinforced soil
Divisions
fac_eng
Funders
Science and Technology Planning Project of Chongqing Education Commission (KJQN201804305, KJQN201904307) (JG-KJ-2019-006)
Publication Title
Applied Sciences
Volume
11
Issue
3
Publisher
MDPI