Development of an excavator-avoidance system for buried pipes

2021 ◽  
pp. 1-16
Author(s):  
Ryosuke Yajima ◽  
Shinya Katsuma ◽  
Makoto Suzuki ◽  
Fumiya Matsushita ◽  
Shunsuke Hamasaki ◽  
...  
Keyword(s):  
Buried Pipes

Effect of Geocell Reinforcement above Buried Pipes on Surface Settlement

2018 ◽  
Vol 9 (2) ◽  
pp. 86 ◽  
Author(s):  
Mohammed Yousif Fattah ◽  
Waqed Hameed Hassan ◽  
Sajjad E. Rasheed
Keyword(s):  
Surface Settlement ◽  
Buried Pipes ◽  
Geocell Reinforcement

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

2021 ◽  
Vol 11 (3) ◽  
pp. 908
Author(s):  
Jie Zeng ◽  
Panagiotis G. Asteris ◽  
Anna P. Mamou ◽  
Ahmed Salih Mohammed ◽  
Emmanuil A. Golias ◽  
...  
Keyword(s):  
Neural Network ◽  
Numerical Models ◽  
Correlation Coefficients ◽  
Network Models ◽  
Small Scale ◽  
Offshore Platforms ◽  
Neural Network Models ◽  
Buried Pipes ◽  
Ensemble Techniques ◽  
Uplift Resistance

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.

Sign in / Sign up

Export Citation Format

Share Document