This literature review uses research informed by disasters including the Christchurch Earthquakes, Hurricane Katrina, Red River floods, War in Israel and natural disasters in Indonesia to identify key aspects within teacher-student relationships which result in an increase in the emotional stability of our students. These aspects include prior knowledge of students and their development, psycho-social interventions and incorporation of the disaster into the curriculum. Teacher-student relationships are highlighted as vital to a child’s healing and resilience after experiencing disaster trauma.
Knowing how to rapidly rebuild disaster-damaged infrastructure, while deciding appropriate recovery strategies and catering for future investment is a matter of core interest to government decision makers, utility providers, and business sectors. The purpose of this research is to explore the effects of decisions and outcomes for physical reconstruction on the overall recovery process of horizontal infrastructure in New Zealand using the Canterbury and Kaikoura earthquakes as cases. A mixed approach including a systematic review, questionnaire survey and semi-structured interviews is used to capture perspectives of those involved in reconstruction process and gain insights into the effect of critical elements on infrastructure downtime. Findings from this research will contribute towards advancements of a systems dynamics model considering critical decision-making variables across phases of the reconstruction process to assess how these variables affect the rebuild process and the corresponding downtime. This project will improve the ability to explore alternative resilience improvement pathways and test the efficacy of alternative means for facilitating a faster and better reconstruction process.
The level of destruction from the 2011 Christchurch earthquakes led to changes in the New Zealand seismic building code. The destruction showed that the NZ building codes did not fully performed to expectation and needed Improvement to ensure that impact of future earthquakes would be minimised. The building codes have been amended to improve buildings resilience to earthquake and other related extreme loading conditions. Rebuilding Christchurch with the new modifications in the seismic building code comes with its own unique challenges to the entire system. This project investigates the impact of rebuilding Christchurch with the new seismic Building codes in terms of how the new changes affected the building industry and the management of construction.
Objectives • To develop a system dynamics model of Christchurch post-quake reconstruction process that captures all the critical dynamics influencing its pathway • To investigate the implications of current rebuild pathway • To build a reconstruction module to be integrated in MERIT (Measuring the Economics of Resilient Infrastructure Tool)
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
