SUMMARYArias intensity, I a , has been identified as an efficient intensity measure for the estimation of earthquakeinduced losses. In this paper, a new model for the prediction of Arias intensity, which incorporates nonlinear site response through the use of the average shear-wave velocity and a heteroskedastic variance structure, is proposed. In order to estimate the effects of ground motions on spatially-distributed systems, it is important to take into account the spatial correlation of the intensity measure. However, existing lossestimation models, which use I a as an input, do not take this aspect of the ground motion into account. Therefore, the potential to model the spatial correlation of Arias intensity is also investigated. The empirical predictive model is developed using recordings from the Pacific Earthquake Engineering Research Center Next Generation of Attenuation database whereas the model for spatial correlation makes use of the wellrecorded events from this database, that is the Northridge and Chi-Chi earthquakes.

Arias intensity (I A ) and cumulative absolute velocity (CAV) are groundmotion measures that have been found to be well suited to application in a number of problems in earthquake engineering. Both measures reflect multiple characteristics of the ground motion (e.g., amplitude and duration), despite being scalar measures. In this study, new ground-motion prediction models for the average horizontal component of I A and CAV are developed, using an extended database of strong-motion records from Japan, including the 2011 Tohoku event. The models are valid for magnitude greater than 5.0, rupture distance less than 300 km, and focal depth less than 150 km. The models are novel because they take account of ground-motion data from the 2011 Tohoku earthquake while incorporating other important features such as event type and regional anelastic attenuation. The residuals from the ground-motion modeling are analyzed in detail to gain further insights into the uncertainties related to the developed median prediction equations for I A and CAV. The site-to-site standard deviations are computed and spatial correlation analysis is carried out for I A and CAV, considering both within-event residuals and within-event single-site residuals for individual events as well as for the combined dataset.

In November 2012 EEFIT launched its first ever return mission to an earthquake affected site. The L'Aquila Earthquake site was chosen as this is a recent European event of interest to the UK and European earthquake engineering community. The main aims of this return mission were to document the earthquake recovery process and this paper presents an overview of the post-disaster emergency phase and transition to reconstruction in the Aquila area after the earthquake. It takes an earthquake engineering perspective, highlighting areas mainly of interest to the fields of structural/seismic engineering and reconstruction management. Within the paper, reference is made to published literature, but also to data collected in the field during the return mission that would not otherwise have been available.

This study explores the performance of GEOCAN, a remote-sensing and crowdsourcing platform for assessing earthquake damage, by using geo-referenced ground-based damage assessments. This paper discusses methods for the application of remote sensing in post-earthquake damage assessment and reports on a GEOCAN crowd-sourcing study following the 22 February 2011 Christchurch event and its validation using field studies. It describes the principal data sets used, discusses in detail the problems of validation, and considers the extent of omission and commission errors. It is clear that although commission errors in the GEOCAN damage estimation are low, the omission error is significant (64%); the extent of these and the causal factors are analyzed with a decision model. The results show that the image-based analysis in this case does not reproduce the spatial pattern or magnitude of the damage impact. Finally, recommendations to improve the performance of GEOCAN in subsequent deployments are made.

Understanding how container routing stands to be impacted by different scenarios of liner shipping network perturbations such as natural disasters or new major infrastructure developments is of key importance for decision-making in the liner shipping industry. The variety of actors and processes within modern supply chains and the complexity of their relationships have previously led to the development of simulation-based models, whose application has been largely compromised by their dependency on extensive and often confidential sets of data. This study proposes the application of optimisation techniques less dependent on complex data sets in order to develop a quantitative framework to assess the impacts of disruptive events on liner shipping networks. We provide a categorization of liner network perturbations, differentiating between systemic and external and formulate a container assignment model that minimises routing costs extending previous implementations to allow feasible solutions when routing capacity is reduced below transport demand. We develop a base case network for the Southeast Asia to Europe liner shipping trade and review of accidents related to port disruptions for two scenarios of seismic and political conflict hazards. Numerical results identify alternative routing paths and costs in the aftermath of port disruptions scenarios and suggest higher vulnerability of intra-regional connectivity.