The full scale, in-situ investigations of instrumented buildings present an excellent opportunity to observe their dynamic response in as-built environment, which includes all the real physical properties of a structure under study and its surroundings. The recorded responses can be used for better understanding of behavior of structures by extracting their dynamic characteristics. It is significantly valuable to examine the behavior of buildings under different excitation scenarios. The trends in dynamic characteristics, such as modal frequencies and damping ratios, thus developed can provide quantitative data for the variations in the behavior of buildings. Moreover, such studies provide invaluable information for the development and calibration of realistic models for the prediction of seismic response of structures in model updating and structural health monitoring studies. This thesis comprises two parts. The first part presents an evaluation of seismic responses of two instrumented three storey RC buildings under a selection of 50 earthquakes and behavioral changes after Ms=7.1 Darfield (2010) and Ms=6.3 Christchurch (2011) earthquakes for an instrumented eight story RC building. The dynamic characteristics of the instrumented buildings were identified using state-of-the-art N4SID system identification technique. Seismic response trends were developed for the three storey instrumented buildings in light of the identified frequencies and the peak response accelerations (PRA). Frequencies were observed to decrease with excitation level while no trends are discernible for the damping ratios. Soil-structure interaction (SSI) effects were also determined to ascertain their contribution in the seismic response. For the eight storey building, it was found through system identification that strong nonlinearities in the structural response occurred and manifested themselves in all identified natural frequencies of the building that exhibited a marked decrease during the strong motion duration compared to the pre-Darfield earthquakes. Evidence of foundation rocking was also found that led to a slight decrease in the identified modal frequencies. Permanent stiffness loss was also observed after the strong motion events. The second part constitutes developing and calibrating finite element model (FEM) of the instrumented three storey RC building with a shear core. A three dimensional FEM of the building is developed in stages to analyze the effect of structural, non-structural components (NSCs) and SSI on the building dynamics. Further to accurately replicate the response of the building following the response trends developed in the first part of the thesis, sensitivity based model updating technique was applied. The FEMs were calibrated by tuning the updating parameters which are stiffnesses of concrete, NSCs and soil. The updating parameters were found to generally follow decreasing trends with the excitation level. Finally, the updated FEM was used in time history analyses to assess the building seismic performance at the serviceability limit state shaking. Overall, this research will contribute towards better understanding and prediction of the behavior of structures subjected to ground motion.
Welcome to the Recover newsletter Issue 6 from the Marine Ecology Research Group (MERG) of the University of Canterbury. Recover is designed to keep you updated on our MBIE-funded earthquake recovery project called RECOVER (Reef Ecology, Coastal Values & Earthquake Recovery). This 6th instalment features the ‘new land’ created by the earthquake uplift of the coastline, recreational uses of beaches in Marlborough, and pāua survey work and hatchery projects with our partners in Kaikōura.
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.
This thesis investigates life-safety risk in earthquakes. The first component of the thesis utilises a dataset of earthquake injuries and deaths from recent earthquakes in New Zealand to identify cause, context, and risk factors of injury and death in the 2011 MW6.3 Christchurch earthquake and 2016 MW7.8 Kaikōura earthquake. Results show that nearly all deaths occurred from being hit by structural elements from buildings, while most injuries were caused by falls, strains and being hit by contents or non-structural elements. Statistical analysis of injured cases compared to an uninjured control group found that age, gender, building damage, shaking intensity, and behaviour during shaking were the most significant risk factors for injury during these earthquakes. The second part of the thesis uses the empirical findings from the first section to develop two tools for managing life-safety risk in earthquakes. The first tool is a casualty estimation model for health system and emergency response planning. An existing casualty model used in New Zealand was validated against observed data from the 2011 Christchurch earthquake and found to underestimate moderate and severe injuries by an order of magnitude. The model was then updated to include human behaviour such as protective actions, falls and strain type injuries that are dependent on shaking intensity, as well as injuries and deaths outside buildings. These improvements resulted in a closer fit to observed casualties for the 2011 Christchurch earthquake. The second tool that was developed is a framework to set seismic loading standards for design based on fatality risk targets. The proposed framework extends the risk-targeted hazard method, by moving beyond collapse risk targets, to fatality risk targets for individuals in buildings and societal risk in cities. The framework also includes treatment of epistemic uncertainty in seismic hazard to allow this uncertainty to be used in risk-based decision making. The framework is demonstrated by showing how the current New Zealand loading standards could be revised to achieve uniform life-safety risk across the country and how the introduction of a new loading factor can reduce risk aggregation in cities. Not on Alma, moved and emailed. 1/02/2023 ce
