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DInSAR data assimilation for settlement prediction: Case study of a railway embankment in the Netherlands

机译:DInsaR数据同化用于沉降预测:荷兰铁路路堤的案例研究

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摘要

Secondary settlements in soft soils represent a significant fraction of the total settlement induced by external loads. Consequently, these settlements can play a key role in performance, serviceability, and safety of engineering works such as buildings, roads, embankments, and pipelines. This paper addresses the development of a predictive settlement model for a railway embankment built on soft clayey–peaty soils by following an original procedure consisting of three cascading steps: (i) preliminary detection of the most settlement-affected portions of the infrastructure; (ii) development of an equivalent subsoil model to study secondary settlements; (iii) back-calculation of the parameters of a predictive settlement model (design subsoil model) via a variational data assimilation scheme that exploits ground displacement measurements derived from differential interferometric synthetic aperture radar (DInSAR) data. The main achievement relies on the retrieval of a stochastic prediction of secondary settlements that can contribute to rationalize both conventional monitoring campaigns and management of key infrastructure.
机译:软土中的次生沉降占外部荷载引起的总沉降的很大一部分。因此,这些沉降在建筑物,道路,路堤和管道等工程工程的性能,可维修性和安全性中可以发挥关键作用。本文通过遵循由三个级联步骤组成的原始程序,探讨了在软质黏土-灰泥土上建造的铁路路堤的预测沉降模型的发展:(i)初步检测受基础设施影响最大的部分; (ii)建立等效的地下模型来研究次生定居点; (iii)通过变化数据同化方案对预测沉降模型(设计地下土模型)的参数进行反算,该方案利用了从差分干涉式合成孔径雷达(DInSAR)数据得出的地面位移测量结果。主要成就取决于对二级居民点的随机预测的检索,这有助于合理化常规监测活动和关键基础设施的管理。

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