Unreinforced masonry (URM) structures comprise a majority of the global built heritage. The masonry heritage of New Zealand is comparatively younger to its European counterparts. In a country facing frequent earthquakes, the URM buildings are prone to extensive damage and collapse. The Canterbury earthquake sequence proved the same, causing damage to over _% buildings. The ability to assess the severity of building damage is essential for emergency response and recovery. Following the Canterbury earthquakes, the damaged buildings were categorized into various damage states using the EMS-98 scale. This article investigates machine learning techniques such as k-nearest neighbors, decision trees, and random forests, to rapidly assess earthquake-induced building damage. The damage data from the Canterbury earthquake sequence is used to obtain the forecast model, and the performance of each machine learning technique is evaluated using the remaining (test) data. On getting a high accuracy the model is then run for building database collected for Dunedin to predict expected damage during the rupture of the Akatore fault.
After a high-intensity seismic event, inspections of structural damages need to be carried out as soon as possible in order to optimize the emergency management, as well as improving the recovery time. In the current practice, damage inspections are performed by an experienced engineer, who physically inspect the structures. This way of doing not only requires a significant amount of time and high skilled human resources, but also raises the concern about the inspector’s safety. A promising alternative is represented using new technologies, such as drones and artificial intelligence, which can perform part of the damage classification task. In fact, drones can safely access high hazard components of the structures: for instance, bridge piers or abutments, and perform the reconnaissance by using highresolution cameras. Furthermore, images can be automatically processed by machine learning algorithms, and damages detected. In this paper, the possibility of applying such technologies for inspecting New Zealand bridges is explored. Firstly, a machine-learning model for damage detection by performing image analysis is presented. Specifically, the algorithm was trained to recognize cracks in concrete members. A sensitivity analysis was carried out to evaluate the algorithm accuracy by using database images. Depending on the confidence level desired,i.e. by allowing a manual classification where the alghortim confidence is below a specific tolerance, the accuracy was found reaching up to 84.7%. In the second part, the model is applied to detect the damage observed on the Anzac Bridge (GPS coordinates -43.500865, 172.701138) in Christchurch by performing a drone reconnaissance. Reults show that the accuracy of the damage detection was equal to 88% and 63% for cracking and spalling, respectively.
The Canterbury Earthquake Sequence (CES), induced extensive damage in residential buildings and led to over NZ$40 billion in total economic losses. Due to the unique insurance setting in New Zealand, up to 80% of the financial losses were insured. Over the CES, the Earthquake Commission (EQC) received more than 412,000 insurance claims for residential buildings. The 4 September 2010 earthquake is the event for which most of the claims have been lodged with more than 138,000 residential claims for this event only. This research project uses EQC claim database to develop a seismic loss prediction model for residential buildings in Christchurch. It uses machine learning to create a procedure capable of highlighting critical features that affected the most buildings loss. A future study of those features enables the generation of insights that can be used by various stakeholders, for example, to better understand the influence of a structural system on the building loss or to select appropriate risk mitigation measures. Previous to the training of the machine learning model, the claim dataset was supplemented with additional data sourced from private and open access databases giving complementary information related to the building characteristics, seismic demand, liquefaction occurrence and soil conditions. This poster presents results of a machine learning model trained on a merged dataset using residential claims from the 4 September 2010.
The Bachelor of Youth and Community Leadership (BYCL) was launched by the University of Canterbury (UC) in 2020. The genesis of this new degree was a Stage One service-learning course that, in turn, arose from the innovative and active response of many of the university’s students in the aftermath of the Christchurch earthquakes in 2010 and 2011. That innovative action saw the formation of the Student Volunteer Army as well as the adoption of a new set of Graduate Attributes for every undergraduate at the university. The idea of a specialist undergraduate degree that captured this unique chain of events began to take form from 2016. The resulting degree was developed as a flexible, transdisciplinary programme for young (and not so young) leaders wanting an academic grounding for their passions in community leadership and social action. In 2020, the inaugural intake of students commenced their studies. In this reflection, we discuss our experience of teaching within the BYCL for the first time, using a collaborative approach to teaching that we based on what we understand, individually and collectively, to draw on principles of democratic pedagogy.
Lake Taupō in New Zealand is associated with frequent unrest and small to moderate eruptions. It presents a high consequence risk scenario with immense potential for destruction to the community and the surrounding environment. Unrest associated with eruptions may also trigger earthquakes. While it is challenging to educate people about the hazards and risks associated with multiple eruptive scenarios, effective education of students can lead to better mitigation strategies and risk reduction. Digital resources with user-directed outcomes have been successfully used to teach action oriented skills relevant for communication during volcanic crisis [4]. However, the use of choose your own adventure strategies to enhance low probability risk literacy for Secondary school outreach has not been fully explored. To investigate how digital narrative storytelling can mediate caldera risk literacy, a module “The Kid who cried Supervolcano” will be introduced in two secondary school classrooms in Christchurch and Rotorua. The module highlights four learning objectives: (a) Super-volcanoes are beautiful but can be dangerous (b) earthquake (unrest) activity is normal for super-volcanoes (c) Small eruptions are possible from super-volcanoes and can be dangerous in our lifetimes (d) Super-eruptions are unlikely in our lifetimes. Students will create their digital narrative using the platform Elementari (www.elementari.io). The findings from this study will provide clear understanding of students’ understanding of risk perceptions of volcanic eruption scenarios and associated hazards and inform the design of educational resources geared towards caldera risk literacy.
High-quality ground motion records are required for engineering applications including response history analysis, seismic hazard development, and validation of physics-based ground motion simulations. However, the determination of whether a ground motion record is high-quality is poorly handled by automation with mathematical functions and can become prohibitive if done manually. Machine learning applications are well-suited to this problem, and a previous feed-forward neural network was developed (Bellagamba et al. 2019) to determine high-quality records from small crustal events in the Canterbury and Wellington regions for simulation validation. This prior work was however limited by the omission of moderate-to-large magnitude events and those from other tectonic environments, as well as a lack of explicit determination of the minimum usable frequency of the ground motion. To address these shortcomings, an updated neural network was developed to predict the quality of ground motion records for all magnitudes and all tectonic sources—active shallow crustal, subduction intraslab, and subduction interface—in New Zealand. The predictive performance of the previous feed-forward neural network was matched by the neural network in the domain of small crustal records, and this level of predictive performance is now extended to all source magnitudes and types in New Zealand making the neural network applicable to global ground motion databases. Furthermore, the neural network provides quality and minimum usable frequency predictions for each of the three orthogonal components of a record which may then be mapped into a binary quality decision or otherwise applied as desired. This framework provides flexibility for the end user to predict high-quality records with various acceptability thresholds allowing for this neural network to be used in a range of applications.
