Six buildings in the Wellington region and the upper South Island, instrumented as part of the GeoNet Building Instrumentation Programme, recorded strong motion data during the 2016 Kaikoura earthquake. The response of two of these buildings: the Bank of New Zealand (BNZ) Harbour Quays, and Ministry of Business, Innovation, and Employment (MBIE) buildings, are examined in detail. Their acceleration and displacement response was reconstructed from the recorded data, and their vibrational characteristics were examined by computing their frequency response functions. The location of the BNZ building in the CentrePort region on the Wellington waterfront, which experienced significant ground motion amplification in the 1–2 s period range due to site effects, resulted in the imposition of especially large demands on the building. The computed response of the two buildings are compared to the intensity of ground motions they experienced and the structural and nonstructural damage they suffered, in an effort to motivate the use of structural response data in the validation of performance objectives of building codes, structural modelling techniques, and fragility functions. Finally, the nature of challenges typically encountered in the interpretation of structural response data are highlighted.

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Recent earthquakes in New Zealand not only highlighted the vulnerabilities of the existing building stock but also the need for: (i) a better understanding of the building inventory, and (ii) easy access to information for quicker response after an event. In the case of Wellington, efforts over the years by the City Council and other stakeholders have produced a number of useful datasets about the building inventory. These available datasets when put together are critical in understanding the composition and characteristics of the building inventory in Wellington. This paper describes the available information, and the process to combine the different strands of data possessed by multiple stakeholders into an effective and usable multi-disciplinary building inventory database for Wellington’s CBD. The uses and future directions for this collated database are also discussed.

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Sponsoring Agency Code Supplementary Notes AbstractThe objectives of this research were to retrospectively study the feasibility for using truck transponder data to produce freight corridor performance measures (travel times) and real-time traveler information. To support this analysis, weighin-motion data from each of the twenty-two stations in Oregon were assembled, processed, and uploaded in the WIM data archive is housed under the Portland Transportation Archive Listing (PORTAL) umbrella at Portland State University's Intelligent Transportation Systems Lab. Nearly 42,000,000 truck records were successful uploaded to the archive dating back to July 2005. Two separate algorithms necessary for this research were scripted, tested, and validated. The closest stations are 38.3 miles apart; the greatest are 258 miles apart. The first algorithm matched transponders between of all vehicles in a time window between the upstream and downstream stations. The second algorithm filtered these matches for through trucks. The filter was validated by comparing estimated travel times during a winter weather-induced delay. The analysis showed that corridor-level travel times for trucks for 2007 and 2008 could be generated from the archived data. To explore the feasibility using these same data for real-time traveler information, ground truth probe vehicle data were collected. Travel time estimates from the WIM data and the probes were used to establish a simple linear relationship between passenger car and truck performance. It was concluded that the long distances between stations was a primary challenge to directly adapting the WIM data to real-time use. Recommendations were given on increased sensor spacing and filter improvement. Finally, potential performance metrics for station level, matched trucks, and filtered matched truck data were shown 17.

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