On 4 September 2010, a 7.1 magnitude earthquake struck near Darfield, 40 kilometres west of Christchurch, New Zealand. The quake caused significant damage to land and buildings nearby, with damage extending to Christchurch city. On 22 February 2011, a 6.3 magnitude earthquake struck Christchurch, causing extensive and significant damage across the city and with the loss of 185 lives. Years on from these events, occasional large aftershocks continue to shake the region. Two main entomological collections were situated within close proximity to the 2010/11 Canterbury earthquakes. The Lincoln University Entomology Research Collection, which is housed on the 5th floor of a 7 storey building, was 27.5 km from the 2010 Darfield earthquake epicentre. The Canterbury Museum Entomology Collection, which is housed in the basement of a multi-storeyed heritage building, was 10 km from the 2011 Christchurch earthquake epicentre. We discuss the impacts of the earthquakes on these collections, the causes of the damage to the specimens and facilities, and subsequent efforts that were made to prevent further damage in the event of future seismic events. We also discuss the wider need for preparedness against the risks posed by natural disasters and other catastrophic events.
Research following the 2010-2011 Canterbury earthquakes investigated the minimum vertical reinforcement required in RC walls to generate well distributed cracking in the plastic hinge region. However, the influence of the loading sequence and rate has not been fully addressed. The new minimum vertical reinforcement limits in NZS 3101:2006 (Amendment 3) include consideration of the material strengths under dynamic load rates, but these provisions have not been validated at a member or system level. A series of tests were conducted on RC prisms to investigate the effect of loading rate and sequence on the local behaviour of RC members. Fifteen axially loaded RC prisms with the designs representing the end region of RC walls were tested under various loading rates to cover the range of pseudo-static and earthquake loading scenarios. These tests will provide substantial data for understanding the local behaviour of RC members, including hysteretic load-deformation behaviour, crack patterns, failure mode, steel strain, strain rate and ductility. Recommendations will be made regarding the effect of loading rate and reinforcement content on the cracking behaviour and ductility of RC members.
Geospatial liquefaction models aim to predict liquefaction using data that is free and readily-available. This data includes (i) common ground-motion intensity measures; and (ii) geospatial parameters (e.g., among many, distance to rivers, distance to coast, and Vs30 estimated from topography) which are used to infer characteristics of the subsurface without in-situ testing. Since their recent inception, such models have been used to predict geohazard impacts throughout New Zealand (e.g., in conjunction with regional ground-motion simulations). While past studies have demonstrated that geospatial liquefaction-models show great promise, the resolution and accuracy of the geospatial data underlying these models is notably poor. As an example, mapped rivers and coastlines often plot hundreds of meters from their actual locations. This stems from the fact that geospatial models aim to rapidly predict liquefaction anywhere in the world and thus utilize the lowest common denominator of available geospatial data, even though higher quality data is often available (e.g., in New Zealand). Accordingly, this study investigates whether the performance of geospatial models can be improved using higher-quality input data. This analysis is performed using (i) 15,101 liquefaction case studies compiled from the 2010-2016 Canterbury Earthquakes; and (ii) geospatial data readily available in New Zealand. In particular, we utilize alternative, higher-quality data to estimate: locations of rivers and streams; location of coastline; depth to ground water; Vs30; and PGV. Most notably, a region-specific Vs30 model improves performance (Figs. 3-4), while other data variants generally have little-to-no effect, even when the “standard” and “high-quality” values differ significantly (Fig. 2). This finding is consistent with the greater sensitivity of geospatial models to Vs30, relative to any other input (Fig. 5), and has implications for modeling in locales worldwide where high quality geospatial data is available.
