Test results are presented for wall-diaphragm plate anchor connections that were axially loaded to rupture. These connection samples were extracted post-earthquake by sorting through the demolition debris from unreinforced masonry (URM) buildings damaged in the Christchurch earthquakes. Unfortunately the number of samples available for testing was small due to the difficulties associated with sample collection in an environment of continuing aftershocks and extensive demolition activity, when personal safety combined with commercial activity involving large demolition machinery were imperatives that inhibited more extensive sample collection for research purposes. Nevertheless, the presented data is expected to be of assistance to structural engineers undertaking seismic assessment of URM buildings that have existing wall-diaphragm anchor plate connections installed, where it may be necessary to estimate the capacity of the existing connection as an important parameter linked with determining the current seismic capacity of the building and therefore influencing the decision regarding whether supplementary connections should be installed.
The context of this study is the increasing need for public transport as issues over high private vehicle usage are becoming increasingly obvious. Public transport services need to compete with private transport to improve patronage, and issues with reliability need to be addressed. Bus bunching affects reliability through disruptions to the scheduled headways. The purpose of this study was to collect and analyse data to compare how travel time and dwell time vary, to explore the variation of key variables, and to better understand the sources of these variations. The Orbiter bus service in Christchurch was used as a case study, as it is particularly vulnerable to bus bunching. The dwell time was found to be more variable than travel time. It appeared the Canterbury earthquake had significantly reduced the average speeds for the Orbiter service. In 1964, Newell and Potts described a basic bus bunching theory, which was used as the basis for an Excel bus bunching model. This model allows input variables to vary stochastically. Random values were generated from four specified distributions derived from manually collected data, allowing variance across all bus platforms and buses. However the complexity resulted in stability and difficulty in achieving convergence, so the model was run in single Monte Carlo simulations. The outputs were realistic and showed a higher degree of bunching behaviour than previous models. The model demonstrated bunching phenomena that had not been observed in previous models, including spontaneously un-pairing, overtaking of buses delayed at platforms, and odd-numbered bunches of three buses. Furthermore, the study identified areas of further research for data collection and model development.
