Indranil Kongar

Post-earthquake assessment and management for infrastructure systems: learning from the Canterbury (New Zealand) and L’Aquila (Italy) earthquakes

Both the April 6, 2009 L’Aquila (Italy) earthquake, and the 2010–2011 Canterbury (New Zealand) earthquake sequence provided unprecedented opportunity to enhance the understanding on earthquake performance of infrastructure systems, and to analyse still-opened issues affecting the post-earthquake assessment and management of infrastructure. This paper provides a succinct and holistic overview on the physical and functional performances of the gas, water, waste water, road and electric networks (this one to a limited extent for the L’Aquila case-study), following the moment magnitude (Mw) 6.3 L’Aquila earthquake, and two main events of the Canterbury earthquake sequence, namely: the Mw 7.1 September 4, 2010 Darfield and the Mw 6.2 February 22, 2011 Christchurch earthquakes. A structured format, based on internationally recognised taxonomies and damage descriptors, is introduced to present the assets and to report on the earthquake-induced physical impacts for both above-ground and underground components. Functional impacts, interdependency issues and resilience attributes observed during the emergency management and recovery phases for the same infrastructure systems are furthermore discussed in the paper. It is envisaged that the data and overview on the seismic performance and management of infrastructure systems presented in the paper can be used to test the effectiveness of existing models and to inform the development of new models for seismic risk assessment and resilience analysis. Also, the structured framework presented within this paper can form the basis for defining specific and standardised survey tools for post-earthquake assessment of infrastructure systems.

Seismic performance of buried electrical cables: evidence-based repair rates and fragility functions

The fragility of buried electrical cables is often neglected in earthquakes but significant damage to cables was observed during the 2010–2011 Canterbury earthquake sequence in New Zealand. This study estimates Poisson repair rates, similar to those in existence for pipelines, using damage data retrieved from part of the electric power distribution network in the city of Christchurch. The functions have been developed separately for four seismic hazard zones: no liquefaction, all liquefaction effects, liquefaction-induced settlement only, and liquefaction-induced lateral spread. In each zone six different intensity measures (IMs) are tested, including peak ground velocity as a measure of ground shaking and five metrics of permanent ground deformation: vertical differential, horizontal, maximum, vector mean and geometric mean. The analysis confirms that the vulnerability of buried cables is influenced more by liquefaction than by ground shaking, and that lateral spread causes more damage than settlement alone. In areas where lateral spreading is observed, the geometric mean permanent ground deformation is identified as the best performing IM across all zones when considering both variance explained and uncertainty. In areas where only settlement is observed, there is only a moderate correlation between repair rate and vertical differential permanent ground deformation but the estimated model error is relatively small and so the model may be acceptable. In general, repair rates in the zone where no liquefaction occurred are very low and it is possible that repairs present in this area result from misclassification of hazard observations, either in the raw data or due to the approximations of the geospatial analysis. Along with hazard intensity, insulation material is identified as a critical factor influencing cable fragility, with paper-insulated lead covered armoured cables experiencing considerably higher repair rates than cross-linked polyethylene cables. The analysis shows no trend between cable age and repair rates and the differences in repair rates between conducting materials is shown not to be significant. In addition to repair rate functions, an example of a fragility curve suite for cables is presented, which may be more useful for analysis of network connectivity where cable functionality is of more interest than the number of repairs. These functions are one of the first to be produced for the prediction of damage to buried cables.

The Effectiveness of Existing Methodologies for Predicting Electrical Substation Damage Due to Earthquakes in New Zealand

This paper tests the applicability of two existing international methodologies, HAZUS (USA) and SYNER-G (Europe), for predicting electrical substation damage. It compares observed damage from the September 2010 and February 2011 Canterbury earthquakes in New Zealand with damage/failure probabilities generated by the two methodologies based on observed ground accelerations. Only two substations were damaged in the September earthquake and only one in the February earthquake. For both methodologies, failure probabilities were calculated for each substation and a short Monte Carlo simulation exercise was run, in which the probabilities were used to generate 1,000 potential city-wide damage scenarios. Both simulations yielded results which over-predicted the collective level of damage, with probabilities of less than 1 in 1000 that the observed scenarios could occur. This indicates that neither method is appropriate and that new fragility functions should be developed for New Zealand for future seismic risk assessments.

Towards a decision support tool for assessing, managing and mitigating seismic risk of electric power networks

Recent seismic event worldwide proved how fragile the electric power system can be to seismic events. Decision Support Systems (DSSs) could have a critical role in assessing the seismic risk of electric power networks and in enabling asset managers to test the effectiveness of alternative mitigation strategies and investments on resilience. This paper exemplifies the potentialities of CIPCast, a DSS recently created in the framework of the EU-funded project CIPRNet, to perform such tasks. CIPCast enables to perform risk assessment for Critical Infrastructures (CI) when subjected to different natural hazards, including earthquakes. An ad-hoc customization of CIPCast for the seismic risk analysis and management of electric power networks is featured in this paper. The international literature describes effective and sound efforts towards the creation of software platforms and frameworks for the assessment of seismic risk of electric power networks. None of them, unfortunately, achieved the goal of creating a user-friendly and ready available DDS to be used by asset managers, local authorities and civil protection departments. Towards that and building on the international literature, the paper describes metrics and methods to be integrated within CIPCast for assessing the earthquake-induced physical and functional impacts of the electric power network at component and system level. The paper describes also how CIPCast can inform the service restoration process.

Evaluating desktop methods for assessing liquefaction-induced damage to infrastructure for the insurance sector

The current method used by insurance catastrophe models to account for liquefaction simply applies a factor to shaking-induced losses based on liquefaction susceptibility. There is a need for more sophisticated methods but they must be compatible with the data and resource constraints that insurers have to work with. This study compares five models: liquefaction potential index (LPI) calculated from shear-wave velocity; two implementations of the HAZUS software methodology; and two models based on USGS remote sensing data. Data from the September 2010 and February 2011 Canterbury (New Zealand) earthquakes is used to compare observed liquefaction occurrences to predictions from these models using binary classification performance measures. The analysis shows that the best performing model is LPI although the correlation with observations is only moderate and statistical techniques for binary classification models indicate that the model is biased towards positive predictions of liquefaction occurrence.

Damage to Buildings: Modeling

Sonia Giovinazzi*, Indranil Kongar, Gian Maria Bocchini and Daria Ottonelli Department of Civil and Natural Resources Engineering, University of Canterbury, Christchurch, New Zealand EPICentre, Department of Civil, Environmental and Geomatic Engineering, University College London, London, UK Institute of Geodynamics, National Observatory of Athens, Athens, Greece Department of Civil, Chemical and Environmental Engineering, University of Genoa, Genoa, Italy

EVALUATING THE POST-EARTHQUAKE ACCESSIBILITY OF HOSPITALS FOR MITIGATION STRATEGY DECISION SUPPORT BY FRAGILITY AND CONNECTIVITY MODELLING

The aim of this paper is to present a trivial proof of concept for analysing the impact of post-earthquake damage to lifelines on indicators of social resilience. The conceptual methodology is demonstrated by carrying out a GIS-based analysis of the impact of damage to highways bridges on accessibility to emergency healthcare facilities in the Santa Clarita suburb ofLos Angeles. A magnitude 6.9 earthquake from the Santa Susana fault zone was used as a scenario event with bridge damage predicted using the HAZUS methodology to calculate exceedance probabilities and uniform random sampling to assign damage states. Moderate damage state was used as the threshold for bridge closure. The system performance was measured as the mean travel time between neighbourhoods and the local hospital, weighted to account for population. The distribution of delay amongst the population has also been derived. The analysis was repeated for three further scenarios to identify the critical node for prioritisation of mitigation works by comparing the impacts with the post-earthquake scenario. The analysis was based on the Los Angeles County disaster route network with straight-line approximations for travel distance on local roads. Results were compared to those obtained using the actual path distance on local roads and this showed that interpretation of the results could vary depending on the measurement method and the decision variable used. Therefore straight-line distance is not a safe approximation. The methodology proposed here will be expanded in the future for a more detailed study assessing the riskfrom damage to multiple lifelines and using a wider range of indicators.