Search

found 4 results

Research papers, University of Canterbury Library

Advanced seismic effective-stress analysis is used to scrutinize the liquefaction performance of 55 well-documented case-history sites from Christchurch. The performance of these sites during the 2010-2011 Canterbury earthquake sequence varied significantly, from no liquefaction manifestation at the ground surface (in any of the major events) to severe liquefaction manifestation in multiple events. For the majority of the 55 sites, the simplified liquefaction evaluation procedures, which are conventionally used in engineering practice, could not explain these dramatic differences in the manifestation. Detailed geotechnical characterization and subsequent examination of the soil profile characteristics of the 55 sites identified some similarities but also important differences between sites that manifested liquefaction in the two major events of the sequence (YY-sites) and sites that did not manifest liquefaction in either event (NN-sites). In particular, while the YY-sites and NN-sites are shown to have practically identical critical layer characteristics, they have significant differences with regard to their deposit characteristics including the thickness and vertical continuity of their critical zones and liquefiable materials. A CPT-based effective stress analysis procedure is developed and implemented for the analyses of the 55 case history sites. Key features of this procedure are that, on the one hand, it can be fully automated in a programming environment and, on the other hand, it is directly equivalent (in the definition of cyclic resistance and required input data) to the CPT-based simplified liquefaction evaluation procedures. These features facilitate significantly the application of effective-stress analysis for simple 1D free-field soil-column problems and also provide a basis for rigorous comparisons of the outcomes of effective-stress analyses and simplified procedures. Input motions for the analyses are derived using selected (reference) recordings from the two major events of the 2010-2011 Canterbury earthquake sequence. A step-by-step procedure for the selection of representative reference motions for each site and their subsequent treatment (i.e. deconvolution and scaling) is presented. The focus of the proposed procedure is to address key aspects of spatial variability of ground motion in the near-source region of an earthquake including extended-source effects, path effects, and variation in the deeper regional geology.

Research papers, The University of Auckland Library

This thesis presents the application of data science techniques, especially machine learning, for the development of seismic damage and loss prediction models for residential buildings. Current post-earthquake building damage evaluation forms are developed for a particular country in mind. The lack of consistency hinders the comparison of building damage between different regions. A new paper form has been developed to address the need for a global universal methodology for post-earthquake building damage assessment. The form was successfully trialled in the street ‘La Morena’ in Mexico City following the 2017 Puebla earthquake. Aside from developing a framework for better input data for performance based earthquake engineering, this project also extended current techniques to derive insights from post-earthquake observations. Machine learning (ML) was applied to seismic damage data of residential buildings in Mexico City following the 2017 Puebla earthquake and in Christchurch following the 2010-2011 Canterbury earthquake sequence (CES). The experience showcased that it is readily possible to develop empirical data only driven models that can successfully identify key damage drivers and hidden underlying correlations without prior engineering knowledge. With adequate maintenance, such models have the potential to be rapidly and easily updated to allow improved damage and loss prediction accuracy and greater ability for models to be generalised. For ML models developed for the key events of the CES, the model trained using data from the 22 February 2011 event generalised the best for loss prediction. This is thought to be because of the large number of instances available for this event and the relatively limited class imbalance between the categories of the target attribute. For the CES, ML highlighted the importance of peak ground acceleration (PGA), building age, building size, liquefaction occurrence, and soil conditions as main factors which affected the losses in residential buildings in Christchurch. ML also highlighted the influence of liquefaction on the buildings losses related to the 22 February 2011 event. Further to the ML model development, the application of post-hoc methodologies was shown to be an effective way to derive insights for ML algorithms that are not intrinsically interpretable. Overall, these provide a basis for the development of ‘greybox’ ML models.