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
The latest two great earthquake sequences; 2010- 2011 Canterbury Earthquake and 2016 Kaikoura Earthquake, necessitate a better understanding of the New Zealand seismic hazard condition for new building design and detailed assessment of existing buildings. It is important to note, however, that the New Zealand seismic hazard map in NZS 1170.5.2004 is generalised in effort to cover all of New Zealand and limited to a earthquake database prior to 2001. This is “common” that site-specific studies typically provide spectral accelerations different to those shown on the national map (Z values in NZS 1170.5:2004); and sometimes even lower. Moreover, Section 5.2 of Module 1 of the Earthquake Geotechnical Engineering Practice series provide the guidelines to perform site- specific studies.
Natural catastrophes are increasing worldwide. They are becoming more frequent but also more severe and impactful on our built environment leading to extensive damage and losses. Earthquake events account for the smallest part of natural events; nevertheless seismic damage led to the most fatalities and significant losses over the period 1981-2016 (Munich Re). Damage prediction is helpful for emergency management and the development of earthquake risk mitigation projects. Recent design efforts focused on the application of performance-based design engineering where damage estimation methodologies use fragility and vulnerability functions. However, the approach does not explicitly specify the essential criteria leading to economic losses. There is thus a need for an improved methodology that finds the critical building elements related to significant losses. The here presented methodology uses data science techniques to identify key building features that contribute to the bulk of losses. It uses empirical data collected on site during earthquake reconnaissance mission to train a machine learning model that can further be used for the estimation of building damage post-earthquake. The first model is developed for Christchurch. Empirical building damage data from the 2010-2011 earthquake events is analysed to find the building features that contributed the most to damage. Once processed, the data is used to train a machine-learning model that can be applied to estimate losses in future earthquake events.
The 2010-2011 Canterbury earthquake sequence, and the resulting extensive data sets on damaged buildings that have been collected, provide a unique opportunity to exercise and evaluate previously published seismic performance assessment procedures. This poster provides an overview of the authors’ methodology to perform evaluations with two such assessment procedures, namely the P-58 guidelines and the REDi Rating System. P-58, produced by the Federal Emergency Management Agency (FEMA) in the United States, aims to facilitate risk assessment and decision-making by quantifying earthquake ground shaking, structural demands, component damage and resulting consequences in a logical framework. The REDi framework, developed by the engineering firm ARUP, aids stakeholders in implementing resilience-based earthquake design. Preliminary results from the evaluations are presented. These have the potential to provide insights on the ability of the assessment procedures to predict impacts using “real-world” data. However, further work remains to critically analyse these results and to broaden the scope of buildings studied and of impacts predicted.
