Motivation This poster aims to present fragility functions for pipelines buried in liquefaction-prone soils. Existing fragility models used to quantify losses can be based on old data or use complex metrics. Addressing these issues, the proposed functions are based on the Christchurch network and soil and utilizes the Canterbury earthquake sequence (CES) data, partially represented in Figure 1. Figure 1 (a) presents the pipe failure dataset, which describes the date, location and pipe on which failures occurred. Figure 1 (b) shows the simulated ground motion intensity median of the 22nd February 2011 earthquake. To develop the model, the network and soil characteristics have also been utilized.
The operation of telecommunication networks is critical during business as usual times, and becomes most vital in post-disaster scenarios, when the services are most needed for restoring other critical lifelines, due to inherent interdependencies, and for supporting emergency and relief management tasks. In spite of the recognized critical importance, the assessment of the seismic performance for the telecommunication infrastructure appears to be underrepresented in the literature. The FP6 QuakeCoRE project “Performance of the Telecommunication Network during the Canterbury Earthquake Sequence” will provide a critical contribution to bridge this gap. Thanks to an unprecedented collaboration between national and international researchers and highly experienced asset managers from Chorus, data and evidences on the physical and functional performance of the telecommunication network after the Canterbury Earthquakes 2010-2011 have been collected and collated. The data will be processed and interpreted aiming to reveal fragilities and resilience of the telecommunication networks to seismic events
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.
