publications

2024

1. Developing a multi-level european-wide composite indicator to assess vulnerability dynamics across time and space

   Andrea Sibilia, Gustav Eklund, Sepehr Marzi, and 7 more authors

   International Journal of Disaster Risk Reduction, Oct 2024

   Abs DOI

   The escalating frequency of extreme climatic events and ongoing urbanisation expose European communities to increasing disaster risks, which are determined not only by the hazardous events themselves, but also by the exposure and vulnerability to these hazards. Consequently, effective risk management strategies cannot overlook a comprehensive understanding of the factors influencing vulnerability of the communities. This paper addresses this need by presenting a European-wide framework for the development of a Vulnerability Index (VI) that evaluates vulnerability at both national and subnational scales. Adopting a multi-dimensional and multi-level approach, the VI captures socio-economic, political, environmental, and physical factors contributing to community resilience. A standardised, supranational methodology is employed, providing harmonised cross-country information and time series data for vulnerability and its underlying indicators. This comprehensive assessment facilitates the understanding of socio-economic dynamics, enabling the formulation of targeted policy actions at both country and subnational levels. By offering insights into current vulnerability trends, the VI underlines the importance of governance, economic factors, and disaster preparedness in reducing vulnerability at different administrative levels, while highlighting the role of social factors, such as poverty and social exclusion, in community vulnerability at sub-regional levels.

2. Cross-border and emerging risks in Europe

   CORBANE Christina, EKLUND Gustav, GYENES Zsuzsanna, and 58 more authors

   Oct 2024

   DOI

2023

1. Development of a seismic loss prediction model for residential buildings using machine learning – Ōtautahi / Christchurch, New Zealand

   Samuel Roeslin, Quincy Ma, Pavan Chigullapally, and 2 more authors

   Natural Hazards and Earth System Sciences, Mar 2023

   Abs DOI

   Abstract. This paper presents a new framework for the seismic loss prediction of residential buildings in Ōtautahi / Christchurch, New Zealand. It employs data science techniques, geospatial tools, and machine learning (ML) trained on insurance claims data from the Earthquake Commission (EQC) collected following the 2010–2011 Canterbury earthquake sequence (CES). The seismic loss prediction obtained from the ML model is shown to outperform the output from existing risk analysis tools for New Zealand for each of the main earthquakes of the CES. In addition to the prediction capabilities, the ML model delivered useful insights into the most important features contributing to losses during the CES. ML correctly highlighted that liquefaction significantly influenced building losses for the 22 February 2011 earthquake. The results are consistent with observations, engineering knowledge, and previous studies, confirming the potential of data science and ML in the analysis of insurance claims data and the development of seismic loss prediction models using empirical loss data.

2021

1. Predicting Seismic Damage and Loss for Residential Buildings using Data Science

   Samuel Roeslin

   University of Auckland, Sep 2021

2020

1. Displacement-based design of tall earthquake-resistant diagrid systems

   Amador Terán-Gilmore, Samuel Roeslin, Edgar Tapia-Hernández, and 1 more author

   Journal of Building Engineering, Nov 2020

   DOI
2. Feature Engineering for a Seismic Loss Prediction Model Using Machine Learning, Christchurch Experience

   Samuel Roeslin, Quincy Ma, Pavan Chigullapally, and 2 more authors

   In Proceeding of the 17th World Conference on Earthquake Engineering, 17WCEE, Nov 2020
3. A machine learning damage prediction model for the 2017 Puebla-Morelos, Mexico, earthquake

   Samuel Roeslin, Quincy Ma, Hugon Juárez-Garcia, and 3 more authors

   Earthquake Spectra, Jul 2020

   http://journals.sagepub.com/doi/10.1177/8755293020936714

   Abs DOI

   The 2017 Puebla, Mexico, earthquake event led to significant damage in many buildings in Mexico City. In the months following the earthquake, civil engineering students conducted detailed building assessments throughout the city. They collected building damage information and structural characteristics for 340 buildings in the Mexico City urban area, with an emphasis on the Roma and Condesa neighborhoods where they assessed 237 buildings. These neighborhoods are of particular interest due to the availability of seismic records captured by nearby recording stations, and preexisting information from when the neighborhoods were affected by the 1985 Michoacán earthquake. This article presents a case study on developing a damage prediction model using machine learning. It details a framework suitable for working with future post-earthquake observation data. Four algorithms able to perform classification tasks were trialed. Random forest, the best performing algorithm, achieves more than 65% prediction accuracy. The study of the feature importance for the random forest shows that the building location, seismic demand, and building height are the parameters that influence the model output the most.

4. The September 19th, 2017 Puebla, Mexico earthquake

   Samuel Roeslin, Hugón Juárez-Garcia, Kenneth Elwood, and 2 more authors

   Bulletin of the New Zealand Society for Earthquake Engineering, Sep 2020

   Abs DOI

   This report presents the observations and findings following the 2017 Puebla earthquake that occurred inMexico on September 19th, 2017. The reconnaissance mission was a collaboration between the New ZealandSociety of Earthquake Engineering (NZSEE), the Universidad Autónoma Metropolitana (UAM) Azcapotzalco,the American Concrete Institute (ACI) Disaster Reconnaissance team, and the Colegio de Ingenieros Civilesde Mexico (CICM). During the earthquake, 77 buildings suffered partial or total collapse and more than8,000 buildings experienced damage ranging from slight damage to significant structural damage necessitatingdemolition. As observed in previous earthquakes, the unique soil conditions of Mexico City resulted inextensive damage to the city’s infrastructure, primarily due to local site effects. The earthquake causedrelatively more damage to buildings built on transition and soft soil zones (i.e. between hard and deep softsoils) than those on hard soils.The NZSEE and UAM team focussed on areas with widespread and extensive damage. They also assessedthe performance of repaired and retrofitted buildings after the 1985 Michoacán earthquake. It was found thatthe lessons learnt from the 1985 Michoacán earthquake led to some risk mitigation measures which benefitedseveral buildings in the 2017 earthquake. Retrofitted buildings were found to have performed very well withlittle or no damage when compared to other buildings.

2018

1. Damage Assessment on Buildings Following the 19th September 2017 Puebla, Mexico Earthquake

   Samuel Roeslin, Quincy T. M. Ma, and Hugón Juárez García

   Frontiers in Built Environment, Dec 2018

   DOI
2. The September 19th, 2017 Puebla, Mexico Earthquake – Preliminary report

   Samuel Roeslin, Kenneth J. Elwood, Hugon Juarez-Garcia, and 2 more authors

   In Proceedings of the 2018 New Zealand Society for Earthquake Engineering (NZSEE) Conference, Dec 2018
3. A New Approach for the Seismic Loss Estimation of Existing Buildings

   Samuel Roeslin and Quincy T. Ma

   In Proceedings of the 11th National Conference in Earthquake Engineering, Earthquake Engineering Research Institute (EERI), Dec 2018

   https://11ncee.org/images/program/papers/11NCEE-000550.pdf
   
   https://researchspace.auckland.ac.nz/handle/2292/43110

   Abs

   Past seismic events have shown that economic losses following an earthquake disaster could significantly exceed any previous loss expectation. Modern engineering design methodologies define the seismic risk based on life safety criteria but do not explicitly specify the essential criteria leading to economic losses. This research attempts to identify the preponderant parameters effecting financial losses due to building damage following an earthquake event. Existing pre-event data collection frameworks are reviewed. A new approach based on the Pareto principle is introduced. Financial and structural survey data from the Christchurch earthquakes are analyzed with the purpose of identifying the principal sources of economic losses in buildings. As a result of the desktop study, a new post-earthquake assessment form based on the GEM building taxonomy is proposed. Remarks following its application in Mexico City following the 2017 Puebla earthquake are presented. Introduction