This paper presents a predictive model for the settlement of shallow-founded structures on liquefiable ground during earthquakes. The model is based on the results of an extensive fully coupled, three-dimensional numerical parametric study of soil–structure systems, validated with centrifuge experiments as well as a database of case history observations. The results of the numerical study provided insight into the relative importance and influence of each input parameter and the functional form of the predictive model for a structure's permanent settlement. The case history database helped validate and refine the predictive model, accounting for complexities of the ground motion and site conditions in the field. Non-linear regression and latent variable analysis were used to develop model coefficients. The uncertainty around model estimates was modelled by a lognormal distribution. An additional logistic model was provided to estimate the probability of insignificant settlement (defined as less than 1 cm). The proposed probabilistic procedure considers variations in site conditions as well as the presence and properties of a building in three dimensions. By including the case history database in its validation and adjustment, the model captures all mechanisms of settlement below the foundation, including volumetric and deviatoric strains as well as ejecta. The total uncertainty around its predictions is rigorously characterised, which is a necessary step before the benefits of performance-based seismic design can be realised in the evaluation and mitigation of the liquefaction hazard.

The Central Italy earthquake sequence nominally began on 24 August 2016 with a M 6.1 event on a normal fault that produced devastating effects in the town of Amatrice and several nearby villages and hamlets. A major international response was undertaken to record the effects of this disaster, including surface faulting, ground motions, landslides, and damage patterns to structures. This work targeted the development of high‐value case histories useful to future research. Subsequent events in October 2016 exacerbated the damage in previously affected areas and caused damage to new areas in the north, particularly the relatively large town of Norcia. Additional reconnaissance after a M 6.5 event on 30 October 2016 documented and mapped several large landslide features and increased damage states for structures in villages and hamlets throughout the region. This paper provides an overview of the reconnaissance activities undertaken to document and map these and other effects, and highlights valuable lessons learned regarding faulting and ground motions, engineering effects, and emergency response to this disaster.

Structure from motion (SfM) computer vision is a remote sensing method that is gaining popularity due to its simplicity and ability to accurately characterize site geometry in three dimensions (3D). While many researchers have demonstrated the potential for SfM to be used with unmanned aerial vehicles (UAVs) to model in 3D various geologic features, such as landslides, little is understood concerning how the selection of the UAV platform can affect the resolution and accuracy of the model. This study evaluates the resolution and accuracy of 3D point cloud models of a large landslide that occurred in 2013 near Page, Arizona, that were developed from various small UAV platform and camera configurations. Terrestrial laser scans were performed at the landslide and were used to establish a comparative baseline model. Results from the study indicate that point cloud resolution improved by more than 16% when using multi-rotor UAVs instead of fixed-wing UAVs. However, accuracy of the points in the point cloud model appear to be independent of the UAV platform, but depend principally on the selected camera and the image resolution. Additional practical guidance on flying various UAV platforms in challenging field conditions is provided for geologists and engineers.