The Manchester Courts building was a heritage building located in central Christchurch (New Zealand) that was damaged in the Mw 7.1 Darfield earthquake on 4 September 2010 and subsequently demolished as a risk reduction exercise. Because the building was heritage listed, the decision to demolish the building resulted in strong objections from heritage supporters who were of the opinion that the building had sufficient residual strength to survive possible aftershock earthquakes. On 22 February 2011 Christchurch was struck by a severe aftershock, leading to the question of whether building demolition had proven to be the correct risk reduction strategy. Finite element analysis was used to undertake a performance-based assessment, validating the accuracy of the model using the damage observed in the building before its collapse. In addition, soil-structure interaction was introduced into the research due to the comparatively low shear wave velocity of the soil. The demolition of a landmark heritage building was a tragedy that Christchurch will never recover from, but the decision was made considering safety, societal, economic and psychological aspects in order to protect the city and its citizens. The analytical results suggest that the Manchester Courts building would have collapsed during the 2011 Christchurch earthquake, and that the collapse of the building would have resulted in significant fatalities.
Observations in major earthquakes have shown that rockable structures suffered less to no damage. During rocking, that is, partial and temporary footing separations, the influx of seismic energy is interrupted and thus the impact of the base excitation is reduced. Rocking causes the structure to deform more rigid like. Consequently, the structure experiences less deformation along the height and thus a lower damage potential. Although many researchers have studied the influence of rockable footings, most of these are either analytical or numerical, and only a very few structures have been built with rockable footings worldwide, for example, the chimney at Christchurch Airport and the South Rangitikei Viaduct in New Zealand. Despite these studies, a thorough and understanding is not yet available, especially with respect to experimental validations. This work is the first to investigate the rocking behaviour of bridges with different slenderness using large‐scale shake table experiments. To limit the number of influence factors, a stiff footing support and the same fixed‐base fundamental frequency of the bridges were assumed. The result shows that the girder displacement and the footing rotation of the tall bridge do not always move in phase, which cannot be observed in the short bridge. The results demonstrate the important role of slenderness in the overall responses of rockable bridges. This behaviour cannot be observed in bridges with a commonly assumed fixed base since the slenderness effect cannot be activated.
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.
