Reinforced concrete (RC) frame buildings designed according to modern design standards achieved life-safety objectives during the Canterbury earthquakes in 2010-11 and the Kaikōura earthquake in 2016. These buildings formed ductile plastic hinges as intended and partial or total building collapse was prevented. However, despite the fact that the damage level of these buildings was relatively low to moderate, over 60% of multi-storey RC buildings in the Christchurch central business district were demolished due to insufficient insurance coverage and significant uncertainty in the residual capacity and repairability of those buildings. This observation emphasized an imperative need to improve understanding in evaluating the post-earthquake performance of earthquake-damaged buildings and to develop relevant post-earthquake assessment guidelines. This thesis focuses on improving the understanding of the residual capacity and repairability of RC frame buildings. A large-scale five-storey RC moment-resisting frame building was tested to investigate the behaviour of earthquake-damaged and repaired buildings. The original test building was tested with four ground motions, including two repeated design-level ground motions. Subsequently, the test building was repaired using epoxy injection and mortar patching and re-tested with three ground motions. The test building was assessed using key concepts of the ATC-145 post-earthquake assessment guideline to validate its assessment procedures and highlight potential limitations. Numerical models were developed to simulate the peak storey drift demand and identify damage locations. Additionally, fatigue assessment of steel reinforcement was conducted using methodologies as per ATC-145. The residual capacity of earthquake-strained steel reinforcement was experimentally investigated in terms of the residual fatigue capacity and the residual ultimate strain capacity. In addition to studying the fatigue capacity of steel reinforcement, the fatigue damage demand was estimated using 972 ground motion records. The deformation limit of RC beams and columns for damage control was explored to achieve a low likelihood of requiring performance-critical repair. A frame component test database was developed, and the deformation capacity at the initiation of lateral strength loss was examined in terms of the chord rotation, plastic rotation and curvature ductility capacity. Furthermore, the proposed curvature ductility capacity was discussed with the current design curvature ductility limits as per NZS 3101:2006
The rapid classification of building damage states or placards after an earthquake is vital for enabling an efficient emergency response and informed decision-making for rehabilitation and recovery purposes. Traditional methods rely heavily on inspector-led on-site surveys, which are often time-consuming, resource-intensive, and susceptible to human error. This study introduces a machine learning-supported surrogate model designed to streamline the assessment of building damage, focusing on the automated assignment of damage placards within the context of New Zealand's post-earthquake evaluation frameworks. The study evaluates two key safety evaluation protocols—Rapid Building Assessment (RBA) and Detailed Damage Evaluation (DDE)—and integrates corresponding databases derived from the 2010–2011 Canterbury Earthquake Sequence (CES) in Christchurch. Six ML classifiers—Multilayer Perceptron (MLP), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Gradient Boosting Classifier (GBC), and Gradient Bagging (GBag)—were rigorously tested across both databases. The results indicate that the RF-based surrogate model outperforms the other classifiers across both RBA and DDE protocols. Two distinct sets of critical predictors have been further identified for each protocol, allowing for the rapid retrieval of essential data for future on-site surveys, while retaining the RF model's predictive accuracy. The developed surrogate model provides a pragmatic tool for practising engineers to rapidly assign placards to damaged structures and for policymakers and building owners to make informed recovery decisions for earthquake-affected buildings
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
