Ground-Motion Prediction Models for Arias Intensity and Cumulative Absolute Velocity for Japanese Earthquakes Considering Single-Station Sigma and Within-Event Spatial Correlation

Arias intensity (I-A) and cumulative absolute velocity (CAV) are ground-motion 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

The Impact of Ground Motion Uncertainty on Earthquake Loss Estimation

This thesis examines the ground motion prediction component of earthquake loss estimation (ELE) frameworks and is based on the assertion that reducing the uncertainty in ground motion prediction will result in improved accuracy of loss estimates. The objective is to obtain improved ground motion predictions by identifying and quantifying the sources of uncertainty in the predictions, with particular focus on the portion of the uncertainty that can be reduced. The work presented in this thesis starts with an examination of ground motion measures commonly used in ELE and their relative utility. The ground motion measure Arias Intensity is identified as well-suited to application in a number of problems in earthquake engineering and this along with the lack of a robust equation for its prediction, leads to the development of a new predictive equation for Arias Intensity. Next, the prediction of Arias Intensity at spatially separated locations is studied in order to develop a model for the spatial correlation of Arias Intensity so that loss estimates for spatially distributed portfolios may be obtained. Thirdly, the sources of uncertainties in the predicted values of Arias Intensity are investigated and the uncertainties are characterised and quantified in order to establish whether or not they may be reduced. The impacts of these uncertainties on the new predictive equation for Arias Intensity are also examined. The final part of the thesis focusses on the use of GIS to display the information described in the previous sections on ground motion prediction. Particular attention is given to enhancing the display of uncertainties in ground motion predictions. This thesis demonstrates that the impacts of uncertainty on ground motion predictions and therefore earthquake loss estimation are significant, making this research of particular importance in this field

Incorporation of the Spatial Correlation of Arias Intensity Within Earthquake Loss Estimation

Arias Intensity (Ia) has been identified as an efficient intensity measure for the purpose of estimating the likelihood and extent of landslides. This efficiency implies that Arias intensity may logically be used within earthquake loss estimation applications in order to ultimately estimate the damage to spatially-distributed systems or portfolios. In order to estimate the effects of ground motions on such spatially-distributed systems it is important to take into account the spatial correlation of the intensity measure. However, existing landslide loss-estimation models, which use Ia as an input, do not take this aspect of the ground motion into account. Due to the areal nature of landslides, accounting for the spatial distribution of Ia is important if one wishes to accurately predict the probability of landslides occurring, and their subsequent displacements. In this paper, a model for the spatial correlation of Arias intensity is proposed. In order to obtain this model, a new empirical prediction equation for Arias intensity is first developed. The empirical predictive model is developed using recordings from the PEER NGA database while the model for spatial correlation makes use of the well-recorded events from this database, i.e. the Northridge and Chi-Chi earthquakes

Modelling the impact of liner shipping network perturbations on container cargo routing: Southeast Asia to Europe application

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

A predictive model for Arias intensity at multiple sites and consideration of spatial correlations

Arias intensity, Ia, has been identified as an efficient intensity measure for the estimation of earthquake-induced 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 loss-estimation models, which use Ia 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 well-recorded events from this database, that is the Northridge and Chi-Chi earthquakes

A Framework for Understanding Uncertainty in Seismic Risk Assessment

A better understanding of the uncertainty that exists in models used for seismic risk assessment is critical to improving risk-based decisions pertaining to earthquake safety. Current models estimating the probability of collapse of a building do not consider comprehensively the nature and impact of uncertainty. This article presents a model framework to enhance seismic risk assessment and thus gives decisionmakers a fuller understanding of the nature and limitations of the estimates. This can help ensure that risks are not over- or underestimated and the value of acquiring accurate data is appreciated fully. The methodology presented provides a novel treatment of uncertainties in input variables, their propagation through the model, and their effect on the results. The study presents ranges of possible annual collapse probabilities for different case studies on buildings in different parts of the world, exposed to different levels of seismicity, and with different vulnerabilities. A global sensitivity analysis was conducted to determine the significance of uncertain variables. Two key outcomes are (1) that the uncertainty in ground-motion conversion equations has the largest effect on the uncertainty in the calculation of annual collapse probability; and (2) the vulnerability of a building appears to have an effect on the range of annual collapse probabilities produced, i.e., the level of uncertainty in the estimate of annual collapse probability, with less vulnerable buildings having a smaller uncertainty

Using Remote Sensing for Building Damage Assessment: GEOCAN Study and Validation for 2011 Christchurch Earthquake

This study explores the performance of GEOCAN, a remote-sensing and crowdsourcing platform for assessing earthquake damage, by using georeferenced 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