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On 14th August 2021, a magnitude 7.2 earthquake struck the Tiburon Peninsula in the Caribbean nation of Haiti, approximately 150 km west of the capital Port-au-Prince. Aftershocks up to moment magnitude 5.7 followed and over 1,000 landslides were triggered. These events led to over 2,000 fatalities, 15,000 injuries and more than 137,000 structural failures. The economic impact is of the order of US$1.6 billion. The on-going Covid pandemic and a complex political and security situation in Haiti meant that deploying earthquake engineers from the UK to assess structural damage and identify lessons for future building construction was impractical. Instead, the Earthquake Engineering Field Investigation Team (EEFIT) carried out a hybrid mission, modelled on the previous EEFIT Aegean Mission of 2020. The objectives were: to use open-source information, particularly remote sensing data such as InSAR and Optical/Multispectral imagery, to characterise the earthquake and associated hazards; to understand the observed strong ground motions and compare these to existing seismic codes; to undertake remote structural damage assessments, and to evaluate the applicability of the techniques used for future post-disaster assessments. Remote structural damage assessments were conducted in collaboration with the Structural Extreme Events Reconnaissance (StEER) team, who mobilised a group of local non-experts to rapidly record building damage. The EEFIT team undertook damage assessment for over 2,000 buildings comprising schools, hospitals, churches and housing to investigate the impact of the earthquake on building typologies in Haiti. This paper summarises the mission setup and findings, and discusses the benefits, and difficulties, encountered during this hybrid reconnaissance mission.
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The development of Gaussian process regression for effective regional post‐earthquake building damage inference
Abstract Post‐earthquake reconnaissance survey of structural damage is an effective way of documenting and understanding the impact of earthquakes on structures. This article aims at providing an efficient data‐based framework that reduces the required time for reconnaissance missions and predicts the damage intensities for every building in the affected region. We hypothesize that a joint selection of necessary structural and earthquake parameters along with sparse damage observations are sufficient to train a supervised learning algorithm and accurately infer the damage for other buildings in the region. Gaussian process regression is employed to prove the hypothesis for probabilistic inference of different damage indices. The algorithm performs efficiently by selecting a set of diverse and representative buildings for damage observations using K‐medoids clustering. To validate the hypothesis and the proposed method, the algorithm framework is implemented on two severe earthquake simulation testbeds. The impacts of different building and ground motion variables on the damage inference performance are discussed. Furthermore, the effectiveness of observation sampling by clustering in the post‐earthquake damage inference is compared with random sampling.
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- Award ID(s):
- 1839833
- PAR ID:
- 10246023
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Computer-Aided Civil and Infrastructure Engineering
- Volume:
- 36
- Issue:
- 3
- ISSN:
- 1093-9687
- Page Range / eLocation ID:
- p. 264-288
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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