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
Past earthquakes have consistently highlighted the vulnerabilities of the built environment. Current building codes, which focus primarily on life safety, fail to address the need for buildings to remain functional after a seismic event. The emerging concept of post-earthquake functional recovery aims to integrate recovery-based objectives into building codes. However, literature reveals a predominant focus on engineering parameters, with limited attention given to the perspectives of building users; a critical gap in understanding a building’s comprehensive functionality and path to functional recovery. Whilst structural integrity is essential, functionality following a disaster is not determined by physical stability alone. Thus, this study addresses this gap by exploring the perceptions and expectations of office building users (both tenants and property owners) regarding building functionality and post-earthquake functional recovery in New Zealand’s major urban centres: Wellington, Auckland, and Christchurch. A qualitative research strategy was employed, utilising interpretative phenomenological and grounded theory methods to develop insights from the lived experiences of twenty-six (26) participants. The study applied a systems thinking approach using a socio-technical systems (STS) framework to develop micro (single construct) and macro (multiple constructs) models of building-system functionality. The findings demonstrate that a building’s functionality is nuanced, multifaceted, and context dependent. It encompasses physical, economic, social, organisational, technical, regulatory, and environmental elements, and is ultimately determined by users’ specific needs and priorities. A key divergence emerged: whilst property owners prioritise economic and environmental factors, tenants focus on the socio-organisational aspect, viewing the physical workspace as an embodiment of corporate identity, culture, and values. Regarding expectations for post-earthquake functional recovery, the post-pandemic era has drastically reshaped outlooks. Unlike previous earthquakes, where tenants scrambled for any available space, there is now increased demand for flexibility due to the hybrid work model. The immediate return to an office following a major earthquake is now expected to support critical business functions, with essential services including power or a standby generator, technology to access files, water, as well as operations of air conditioning and elevators. By advancing socio-technical systems theory and elucidating building users’ perspectives for a building’s functionality and post-disaster functional recovery, this research provides evidence-based, social science insights to inform more holistic and effective risk governance in property and disaster risk management
