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Fragility analysis of continuous pipelines subjected to transverse permanent ground deformation

机译:连续横向地基变形的连续管道的脆性分析

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The structural integrity of buried continuous pipelines can be jeopardized by transverse permanent ground deformation (PGD) induced by landslides. A probabilistic analysis can facilitate understanding the likelihood of damage to pipelines in landslide regions, further minimizing the risk. However, empirical fragility curves for the landslide-pipeline interaction problem are not available due to the lack of field data. The problem can be addressed by numerical approaches. In this study, a simplified two-dimensional numerical model is developed. It characterizes the pipes as beam-type structures and the surrounding soil as Winkler springs. It is compared here against three-dimensional continuum-based analyses, which could save extensively on computational efforts. All input parameters were sampled randomly and paired with the displacement demands to form a limited set of statistically significant, yet nominally identical, pipeline samples, and the demand models for the maximum tensile strain were evaluated. A supervised machine learning technique, called Lasso regression, was then used to establish a predictive relation between the input and the output using the limited dataset, based on which a large dataset (one million) was calculated for the fragility analysis. The use of a Winkler-based analysis and the supervised machine learning technique makes it possible to generate fragility curves for pipelines subjected to transverse PGD for the first time. (C) 2018 Production and hosting by Elsevier B.V. on behalf of The Japanese Geotechnical Society.
机译:滑坡引起的横向永久性地面变形(PGD)会危害地下连续管线的结构完整性。概率分析可以帮助您了解滑坡地区管道受损的可能性,从而进一步降低风险。但是,由于缺乏现场数据,无法获得滑坡与管道相互作用问题的经验脆性曲线。该问题可以通过数值方法解决。在这项研究中,开发了简化的二维数值模型。它的特征是管道为梁型结构,周围的土壤为Winkler弹簧。在此将其与基于三维连续体的分析进行比较,这可以节省大量的计算工作。随机采样所有输入参数,并与位移需求配对以形成有限的一组统计上重要但名义上相同的管道样本,并评估了最大拉伸应变的需求模型。然后,使用一种称为Lasso回归的有监督的机器学习技术,使用有限的数据集在输入和输出之间建立预测关系,在此基础上计算出大数据集(一百万)用于脆弱性分析。基于Winkler的分析和有监督的机器学习技术的使用,使得首次生成横向PGD的管道的易碎性曲线成为可能。 (C)2018年由Elsevier B.V.代表日本岩土工程学会制作和托管。

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