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.
DInSAR data assimilation for settlement prediction: Case study of a railway embankment in the Netherlands
PEDUTO, DARIO
;SPERANZA, GIANLUCA;CASCINI, Leonardo
2017-01-01
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.