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Soil-pipe interaction modeling for pipe behavior prediction with super learning based methods
Author(s): Fang Shi; Xiang Peng; Huan Liu; Yafei Hu; Zheng Liu; Eric Li
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Paper Abstract

Underground pipelines are subject to severe distress from the surrounding expansive soil. To investigate the structural response of water mains to varying soil movements, field data, including pipe wall strains in situ soil water content, soil pressure and temperature, was collected. The research on monitoring data analysis has been reported, but the relationship between soil properties and pipe deformation has not been well-interpreted. To characterize the relationship between soil property and pipe deformation, this paper presents a super learning based approach combining feature selection algorithms to predict the water mains structural behavior in different soil environments. Furthermore, automatic variable selection method, e.i. recursive feature elimination algorithm, were used to identify the critical predictors contributing to the pipe deformations. To investigate the adaptability of super learning to different predictive models, this research employed super learning based methods to three different datasets. The predictive performance was evaluated by R-squared, root-mean-square error and mean absolute error. Based on the prediction performance evaluation, the superiority of super learning was validated and demonstrated by predicting three types of pipe deformations accurately. In addition, a comprehensive understand of the water mains working environments becomes possible.

Paper Details

Date Published: 27 March 2018
PDF: 12 pages
Proc. SPIE 10602, Smart Structures and NDE for Industry 4.0, 1060207 (27 March 2018); doi: 10.1117/12.2300812
Show Author Affiliations
Fang Shi, The Univ. of British Columbia Okanagan (Canada)
Xiang Peng, The Univ. of British Columbia Okanagan (Canada)
Huan Liu, China Univ. of Geosciences (China)
Yafei Hu, City of Regina (Canada)
Zheng Liu, The Univ. of British Columbia Okanagan (Canada)
Eric Li, The Univ. of British Columbia Okanagan (Canada)


Published in SPIE Proceedings Vol. 10602:
Smart Structures and NDE for Industry 4.0
Norbert G. Meyendorf; Dan J. Clingman, Editor(s)

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