This paper presents on-going challenges in the present paradigm shift of earthquakeinduced ground motion prediction from empirical to physics-based simulation methods. The 2010-2011 Canterbury and 2016 Kaikoura earthquakes are used to illustrate the predictive potential of the different methods. On-going efforts on simulation validation and theoretical developments are then presented, as well as the demands associated with the need for explicit consideration of modelling uncertainties. Finally, discussion is also given to the tools and databases needed for the efficient utilization of simulated ground motions both in specific engineering projects as well as for near-real-time impact assessment.
he 2016 Building (Earthquake Prone Building) Amendment Act aims to improve the system for managing earthquake-prone buildings. The proposed changes to the Act were precipitated by the Canterbury earthquakes, and the need to improve the seismic safety of New Zealand’s building stock. However, the Act has significant ramifications for territorial authorities, organisations and individuals in small New Zealand towns, since assessing and repairing heritage buildings poses a major cost to districts with low populations and poor rental returns on commercial buildings.
The latest two great earthquake sequences; 2010- 2011 Canterbury Earthquake and 2016 Kaikoura Earthquake, necessitate a better understanding of the New Zealand seismic hazard condition for new building design and detailed assessment of existing buildings. It is important to note, however, that the New Zealand seismic hazard map in NZS 1170.5.2004 is generalised in effort to cover all of New Zealand and limited to a earthquake database prior to 2001. This is “common” that site-specific studies typically provide spectral accelerations different to those shown on the national map (Z values in NZS 1170.5:2004); and sometimes even lower. Moreover, Section 5.2 of Module 1 of the Earthquake Geotechnical Engineering Practice series provide the guidelines to perform site- specific studies.
Semi-empirical models based on in-situ geotechnical tests have become the standard of practice for predicting soil liquefaction. Since the inception of the “simplified” cyclic-stress model in 1971, variants based on various in-situ tests have been developed, including the Cone Penetration Test (CPT). More recently, prediction models based soley on remotely-sensed data were developed. Similar to systems that provide automated content on earthquake impacts, these “geospatial” models aim to predict liquefaction for rapid response and loss estimation using readily-available data. This data includes (i) common ground-motion intensity measures (e.g., PGA), which can either be provided in near-real-time following an earthquake, or predicted for a future event; and (ii) geospatial parameters derived from digital elevation models, which are used to infer characteristics of the subsurface relevent to liquefaction. However, the predictive capabilities of geospatial and geotechnical models have not been directly compared, which could elucidate techniques for improving the geospatial models, and which would provide a baseline for measuring improvements. Accordingly, this study assesses the realtive efficacy of liquefaction models based on geospatial vs. CPT data using 9,908 case-studies from the 2010-2016 Canterbury earthquakes. While the top-performing models are CPT-based, the geospatial models perform relatively well given their simplicity and low cost. Although further research is needed (e.g., to improve upon the performance of current models), the findings of this study suggest that geospatial models have the potential to provide valuable first-order predictions of liquefaction occurence and consequence. Towards this end, performance assessments of geospatial vs. geotechnical models are ongoing for more than 20 additional global earthquakes.
