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.
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.
