Deformation analysis of the Lake Urmia causeway (LUC) embankments in northwest Iran: insights from multi-sensor interferometry synthetic aperture radar (InSAR) data and finite element modeling (FEM)

This paper presents deformation analysis of Lake Urmia causeway (LUC) embankments in northwest Iran using observations from interferometry synthetic aperture radar (InSAR) and finite element model (FEM) simulation. 58 SAR images including 10 ALOS, 30 Envisat and 18 TerraSAR-X are used to assess settlement of the embankments during 2003–2013. The interferometric dataset includes 140 differential interferograms which are processed using InSAR time series technique of small baseline subset approach. The results show a clear indication of large deformation on the embankments with peak amplitude of \(>\) 50 mm/year in 2003–2010, increasing to \(>\!\!80\)  mm/year in 2012–2013 in the line of sight (LOS) direction from ground to the satellite. 2D decomposition of InSAR observations from Envisat and ALOS satellites that overlap in the years 2007–2010 shows that the rate of the vertical settlement and horizontal motion is not uniform along the embankments; Both eastern and western embankments show significant vertical motion, while horizontal motion plays a more significant role in eastern embankment than western embankment. The InSAR results are then used to simulate deformation using FEM at two cross-sections at the distance of 4 and 9 km from the most western edge of the LUC for which detailed stratigraphy data are available. Results suggest that consolidation due to dissipation of excess pore pressure in embankments can satisfactory predict settlement of the LUC embankments. Our numerical modeling indicates that nearly half of the consolidation since the construction time of the causeway 30 years ago has been done.     

Automated numerical analysis tool for assessing potential bank failures during flooding

River embankments failure due to severe flooding is an extremely complex phenomena triggering permanent or temporary modification to the river morphology, river flow and sediment movement. Reliable and automatic prediction of these movements is crucial to properly identify the protective measures for residents living within the inundation flood zones. In this regard, BISHOP, a decision tool to automatically predict, at multiple river cross-sections, the slope failure circle with the minimum safety factor has been developed. In this paper, the computer tool BISHOP, named after the simplified Bishop method, is presented. Its applications have proven to be highly efficient in real case studies, where the stability of multiple slope profiles, at different river cross-sections, must be analyzed to establish spatial and temporal evolution of the river banks failures. The integration of the proposed methodology within a comprehensive flow hydrodynamic, sediment transport and landslide calculation has particularly enhanced the evaluation of the flood-risk zone during major flooding. Typical results demonstrating the effectiveness of the developed methodology are demonstrated during the analysis of the evolution of a river reach downstream of a dam a dam break scenario.     

The inclusive and simplified forms of Bayesian interpolation for general and monotonic models using Gaussian and Generalized Beta distributions with application to Monte Carlo simulations

A recently developed Bayesian interpolation method (BI) and its application to safety assessment of a flood defense structure are described in this paper. We use a one-dimensional Bayesian Monte Carlo method (BMC) that has been proposed in (Rajabalinejad 2009) to develop a weighted logical dependence between neighboring points. The concept of global uncertainty is adequately explained and different uncertainty association models (UAMs) are presented for linking the local and global uncertainty. Based on the global uncertainty, a simplified approach is introduced. By applying the global uncertainty, we apply the Guassian error estimation to general models and the Generalized Beta (GB) distribution to monotonic models. Our main objective in this research is to simplify the newly developed BMC method and demonstrate that it can dramatically improve the simulation efficiency by using prior information from outcomes of the preceding simulations. We provide theory and numerical algorithms for the BI method geared to multi-dimensional problems, integrate it with a probabilistic finite element model, and apply the coupled models to the reliability assessment of a flood defense for the 17th Street Flood Wall system in New Orleans.