Editorial: Disaster Reconnaissance Missions: Is a Hybrid Approach the Way Forward?

Each existing building is required to be assessed before an impending severe earthquake utilizing Rapid Visual Screening (RVS) methods for its seismic safety since many buildings were constructed before seismic standards, without taking into account current regulations, and because they have a limited lifetime and safety based on how they were designed and maintained. Building damage brought on by earthquakes puts lives in danger and causes significant financial losses. Therefore, the fragility of each building needs to be determined and appropriate precautions need to be taken. RVS methods are used when assessing a large building stock since further in-depth vulnerability assessment methods are computationally expensive and costly to examine even one structure in a large building stock. RVS methods could be implemented in existing buildings in order to determine the damage potential that may occur during an impending earthquake and take necessary measures for decreasing the potential hazard. However, the reliability of conventional RVS methods is limited for accurately assessing large building stock. In this study, building inspection data acquired after the 2015 Gorkha, Nepal earthquake is used to train nine different machine learning algorithms (Decision Tree Classifier, Logistic Regression, Light Gradient Boosting Machine Classifier, eXtreme Gradient Boosting Classifier, Gradient Boosting Classifier, Random Forest Classifier, Support Vector Machines, K-Neighbors Classifier, and Cat Boost Classifier), which ultimately led to the development of a reliable RVS method. The post-earthquake building screening data was used to train, validate, and ultimately test the developed model. By incorporating advanced feature engineering techniques, highly sophisticated parameters were introduced into the developed RVS method. These parameters, including the distance to the earthquake source, fundamental structural period, and spectral acceleration, were integrated to enhance the assessment capabilities. This integration enabled the assessment of existing buildings in diverse seismically vulnerable areas. This study demonstrated a strong correlation between determining building damage states using the established RVS method and those observed after the earthquake. When comparing the developed method with the limited accuracy of conventional RVS methods reported in the literature, a test accuracy of 73% was achieved, surpassing conventional RVS methods by over 40% in accurately classifying building damage states. This emphasizes the importance of detailed data collection after an earthquake for the effective development of RVS methods.

Developing a machine learning-based rapid visual screening method for seismic assessment of existing buildings on a case study data from the 2015 Gorkha, Nepal earthquake

Bektaş,

Kegyes-Brassai

2024

Utilizing digital technologies for rapid damage assessment and reconnaissance: the February 6, 2023 Kahramanmaraş-Türkiye earthquakes (Mw 7.7 and Mw 7.6)

Ersoz,

Pekcan,

Altun

et al. 2024

This paper presents a comprehensive overview of the rapid damage assessment and reconnaissance efforts following the devastating earthquakes on February 6, 2023, in Türkiye. It specifically focuses on implementing the SiteEye Disaster Plugin, an additional component of SiteEye software developed by i4 Company engineers and Middle East Technical University researchers. This tool played a critical role in managing and analyzing a massive dataset comprising over 28,000 images and videos. The research highlights the plugin’s innovative features, such as offline data collection, georeferenced-based layering, and an integrated damage classification system, significantly improving earthquake impact assessments’ accuracy and efficiency. It also underscores the importance of interdisciplinary collaboration involving national and international teams and the role of open data in disaster management. The findings demonstrate how digital technologies can transform the field of disaster response, offering new approaches for rapid assessment and effective management in the aftermath of seismic events. This research contributes valuable insights into enhancing disaster preparedness and response strategies, particularly in earthquake-prone areas.

A hybrid model for post-earthquake performance assessments in challenging contexts

Kijewski-Correa,

Canales,

Hamburger

et al. 2024

Disasters provide an invaluable opportunity to evaluate contemporary design standards and construction practices; these evaluations have historically relied upon experts, which inherently limited the speed, scope and coverage of post-disaster reconnaissance. However, hybrid assessments that localize data collection and engage remote expertise offer a promising alternative, particularly in challenging contexts. This paper describes a multi-phase hybrid assessment conducting rapid assessments with wide coverage followed by detailed assessments of specific building subclasses following the 2021 M7.2 earthquake in Haiti, where security issues limited international participation. The rapid assessment classified and assigned global damage ratings to over 12,500 buildings using over 40 non-expert local data collectors to feed imagery to dozens of remote engineers. A detailed assessment protocol then conducted component-level evaluations of over 200 homes employing enhanced vernacular construction, identified via machine learning from nearly 40,000 acquired images. A second mobile application guided local data collectors through a systematic forensic documentation of 30 of these homes, providing remote engineers with essential implementation details. In total, this hybrid assessment underscored that performance in the 2021 earthquake fundamentally depended upon the type and consistency of the bracing scheme. The developed assessment tools and mobile apps have been shared as a demonstration of how a hybrid approach can be used for rapid and detailed assessments following major earthquakes in challenging contexts. More importantly, the open datasets generated continue to inform efforts to promote greater use of enhanced vernacular architecture as a multi-hazard resilient typology that can deliver life-safety in low-income countries.