Abstract
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.
Highlights
When considering the potential or observed impacts of earthquakes, the predominant focus within the engineering community is towards building damage, because of its potential for casualties
The scatter implies that the functions are most usefully employed as part of a probabilistic assessment but it is up to the individual analyst to process the information provided and make their own judgment as to whether the scale of error is acceptable based on specific project/application requirements
Insulation material is a critical factor that influences cable damage as demonstrated by the fact that repair rates in paper-insulated lead covered armoured (PILCA) cables are considerably higher than those observed in XLPE cables
Summary
When considering the potential or observed impacts of earthquakes, the predominant focus within the engineering community is towards building damage, because of its potential for casualties. Not as important as building damage for immediate life safety, the impacts on infrastructure can be significant during the emergency phase, causing delays to repair work and impeding emergency services operations. Fragility functions estimate the likelihood of damage given a specified level of intensity measure (IM), and are the most common tools adopted for characterizing the robustness of infrastructure elements with respect to earthquake hazards (NIBS 2003; Cavalieri et al 2014a). Whilst numerous fragility functions exist for predicting damage to buildings, fewer fragility functions exist for infrastructure systems. This is partly due to the lack of publicly available observational data of infrastructure performance on which to base empirical fragility functions
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