Internal erosion, or piping, has been attributed as a major cause of dam and embankment failures. Most prediction models for predicting piping use the hydraulic gradient between the upstream and downstream water levels as an indicator. No explicit consideration is made regarding preferential pathways, although piping usually initiates from a discrete downstream location. The local seepage velocity is investigated here through stochastic seepage analysis incorporating consideration of soil heterogeneity. The results show that when the coefficient of variation of hydraulic conductivity is small, the location of the maximum local velocity is typically near the downstream toe of the embankment, as for a deterministic analysis. In contrast, increasing the coefficient of variation scatters the possible locations of the maximum local velocity. The heterogeneity of hydraulic conductivity also leads to an increase in the average exit hydraulic gradient, as well as having a significant influence on the global kinetic energy and kinetic energy distribution.

A data assimilation framework, utilising measurements of pore water pressure to sequentially improve the estimation of soil hydraulic parameters and, in turn, the prediction of slope stability, is proposed. Its effectiveness is demonstrated for an idealised numerical example involving the spatial variability of saturated hydraulic conductivity, k_sat. It is shown that the estimation of k_sat generally improves with more measurement points. The degree of spatial correlation of k_sat influences the improvement in the predicted performance, as does the selection of initial input statistics. However, the results are robust with respect to moderate uncertainty in the spatial and point statistics.

The determination of slope stability for existing slopes is challenging, partly due to the spatial variability of soils. Reliability-based design can incorporate uncertainties and yield probabilities of slope failure. Field measurements can be utilised to constrain probabilistic analyses, thereby reducing uncertainties and generally reducing the calculated probabilities of failure. A method to utilise pore pressure measurements, to first reduce the spatial uncertainty of hydraulic conductivity, by using inverse analysis linked to the Ensemble Kalman Filter, is presented. Subsequently, the hydraulic conductivity has been utilised to constrain uncertainty in strength parameters, usually leading to an increase in the calculated slope reliability.

Current 3D data capturing as implemented on for example airborne or mobile laser scanning systems is able to efficiently sample the surface of a city by billions of unselective points during one working day. What is still difficult is to extract and visualize meaningful information hidden in these point clouds with the same efficiency. This is where the FP7 IQmulus project enters the scene. IQmulus is an interactive facility for processing and visualizing big spatial data. In this study the potential of IQmulus is demonstrated on a laser mobile mapping point cloud of 1 billion points sampling " 10 km of street environment in Toulouse, France. After the data is uploaded to the IQmulus Hadoop Distributed File System, a workflow is defined by the user consisting of retiling the data followed by a PCA driven local dimensionality analysis, which runs efficiently on the IQmulus cloud facility using a Spark implementation. Points scattering in 3 directions are clustered in the tree class, and are separated next into individual trees. Five hours of processing at the 12 node computing cluster results in the automatic identification of 4000+ urban trees. Visualization of the results in the IQmulus fat client helps users to appreciate the results, and developers to identify remaining flaws in the processing workflow.