Hybrid broadband simulation methods typically compute high-frequency portion of ground-motions using a simplified-physics approach (commonly known as “stochastic method”) using the same 1D velocity profile, anelastic attenuation profile and site-attenuation (κ0) value for all sites. However, these parameters relating to Earth structure are known to vary spatially. In this study we modify this conventional approach for high-frequency ground-shaking by using site-specific input parameters (referred to as “site-specific”) and analyze improvements over using same parameters for all sites (referred to as “generic”). First, we theoretically understand how different 1D velocity profiles, anelastic attenuation profiles and site-attenuation (κ0) values affects the Fourier Acceleration Spectrum (FAS). Then, we apply site-specific method to simulate 10 events from the 2010-2011 Canterbury earthquake sequence to assess performance against the generic approach in predicting recorded ground-motions. Our initial results suggest that the site-specific method yields a lower simulation standard deviation than generic case.
Background This study examines the performance of site response analysis via nonlinear total-stress 1D wave-propagation for modelling site effects in physics-based ground motion simulations of the 2010-2011 Canterbury, New Zealand earthquake sequence. This approach allows for explicit modeling of 3D ground motion phenomena at the regional scale, as well as detailed nonlinear site effects at the local scale. The approach is compared to a more commonly used empirical VS30 (30 m time-averaged shear wave velocity)-based method for computing site amplification as proposed by Graves and Pitarka (2010, 2015), and to empirical ground motion prediction via a ground motion model (GMM).
The 14 November 2016 Kaikōura earthquake had major impacts on New Zealand's transport system. Road, rail and port infrastructure was damaged, creating substantial disruption for transport operators, residents, tourists, and business owners in the Canterbury, Marlborough and Wellington regions, with knock-on consequences elsewhere. During both the response and recovery phases, a large amount of information and data relating to the transport system was generated, managed, analysed, and exchanged within and between organisations to assist decision making. To improve information and data exchanges and related decision making in the transport sector during future events and guide new resilience strategies, we present key findings from a recent post-earthquake assessment. The research involved 35 different stakeholder groups and was conducted for the Ministry of Transport. We consider what transport information was available, its usefulness, where it was sourced from, mechanisms for data transfer between organisations, and suggested approaches for continued monitoring.
Knowing how to rapidly rebuild disaster-damaged infrastructure, while deciding appropriate recovery strategies and catering for future investment is a matter of core interest to government decision makers, utility providers, and business sectors. The purpose of this research is to explore the effects of decisions and outcomes for physical reconstruction on the overall recovery process of horizontal infrastructure in New Zealand using the Canterbury and Kaikoura earthquakes as cases. A mixed approach including a systematic review, questionnaire survey and semi-structured interviews is used to capture perspectives of those involved in reconstruction process and gain insights into the effect of critical elements on infrastructure downtime. Findings from this research will contribute towards advancements of a systems dynamics model considering critical decision-making variables across phases of the reconstruction process to assess how these variables affect the rebuild process and the corresponding downtime. This project will improve the ability to explore alternative resilience improvement pathways and test the efficacy of alternative means for facilitating a faster and better reconstruction process.
Natural catastrophes are increasing worldwide. They are becoming more frequent but also more severe and impactful on our built environment leading to extensive damage and losses. Earthquake events account for the smallest part of natural events; nevertheless seismic damage led to the most fatalities and significant losses over the period 1981-2016 (Munich Re). Damage prediction is helpful for emergency management and the development of earthquake risk mitigation projects. Recent design efforts focused on the application of performance-based design engineering where damage estimation methodologies use fragility and vulnerability functions. However, the approach does not explicitly specify the essential criteria leading to economic losses. There is thus a need for an improved methodology that finds the critical building elements related to significant losses. The here presented methodology uses data science techniques to identify key building features that contribute to the bulk of losses. It uses empirical data collected on site during earthquake reconnaissance mission to train a machine learning model that can further be used for the estimation of building damage post-earthquake. The first model is developed for Christchurch. Empirical building damage data from the 2010-2011 earthquake events is analysed to find the building features that contributed the most to damage. Once processed, the data is used to train a machine-learning model that can be applied to estimate losses in future earthquake events.
