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1. Earthquake Geotechnical Engineering Reconnaissance Methods and Advances

   Bray, J.D. (July 2019, 7th International Conference on Earthquake Geotechnical Engineering)

   Earthquake geotechnical engineering is an experience-driven discipline. Field observations are particularly important, because it is difficult to replicate in the laboratory the characteristics and response of in situ soil deposits. Much of the data generated by a major earthquake is perishable, so it is critical that it is collected soon after an event occurs. Detailed mapping and surveying of damaged and undamaged areas provides the data for the well-documented case histories that drive the development of many of the earthquake geotechnical engineering design procedures. New technologies are being employed to capture earthquake-induced ground deformation, including Light Detection and Ranging, Structure-from-Motion, and Unmanned Aerial Vehicles. Post-earthquake reconnaissance has moved beyond taking photographs and field notes to taking advantage of technologies that can capture ground and structure deformations more completely and accurately. Moreover, electronic data enables effective sharing and archiving of the measurements. Unanticipated observations from major events often catalyze new research directions. Important advancements are possible through post-event research if their effects are captured and shared effectively. An overview of some of recent integrated technology deployments and their role in advancing knowledge are presented. 

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2. Combining remote sensing techniques and field surveys for post-earthquake reconnaissance missions

   https://doi.org/10.1007/s10518-023-01716-9  

   Giardina, Giorgia; Macchiarulo, Valentina; Foroughnia, Fatemeh; Jones, Joshua N; Whitworth, Michael_R Z; Voelker, Brandon; Milillo, Pietro; Penney, Camilla; Adams, Keith; Kijewski-Correa, Tracy (May 2024, Bulletin of Earthquake Engineering)

   Remote reconnaissance missions are promising solutions for the assessment of earthquake-induced structural damage and cascading geological hazards. Space-borne remote sensing can complement in-field missions when safety and accessibility concerns limit post-earthquake operations on the ground. However, the implementation of remote sensing techniques in post-disaster missions is limited by the lack of methods that combine different techniques and integrate them with field survey data. This paper presents a new approach for rapid post-earthquake building damage assessment and landslide mapping, based on Synthetic Aperture Radar (SAR) data. The proposed texture-based building damage classification approach exploits very high resolution post-earthquake SAR data integrated with building survey data. For landslide mapping, a backscatter intensity-based landslide detection approach, which also includes the separation between landslides and flooded areas, is combined with optical-based manual inventories. The approach was implemented during the joint Structural Extreme Event Reconnaissance, GeoHazards International and Earthquake Engineering Field Investigation Team mission that followed the 2021 Haiti Earthquake and Tropical Cyclone Grace. 

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3. Learning from Earthquakes Using the Automatic Reconnaissance Image Organizer (ARIO)

   S. J. Dyke, X. Liu (September 2021, Proceeding of the 17th World Conference on Earthquake Engineering)

   null (Ed.)

   In the aftermath of earthquake events, reconnaissance teams are deployed to gather vast amounts of images, moving quickly to capture perishable data to document the performance of infrastructure before they are destroyed. Learning from such data enables engineers to gain new knowledge about the real-world performance of structures. This new knowledge, extracted from such visual data, is critical to mitigate the risks (e.g., damage and loss of life) associated with our built environment in future events. Currently, this learning process is entirely manual, requiring considerable time and expense. Thus, unfortunately, only a tiny portion of these images are shared, curated, and actually utilized. The power of computers and artificial intelligence enables a new approach to organize and catalog such visual data with minimal manual effort. Here we discuss the development and deployment of an organizational system to automate the analysis of large volumes of post-disaster visual data, images. Our application, named the Automated Reconnaissance Image Organizer (ARIO), allows a field engineer to rapidly and automatically categorize their reconnaissance images. ARIO exploits deep convolutional neural networks and trained classifiers, and yields a structured report combined with useful metadata. Classifiers are trained using our ground-truth visual database that includes over 140,000 images from past earthquake reconnaissance missions to study post-disaster buildings in the field. Here we discuss the novel deployment of the ARIO application within a cloud-based system that we named VISER (Visual Structural Expertise Replicator), a comprehensive cloud-based visual data analytics system with a novel Netflix-inspired technical search capability. Field engineers can exploit this research and our application to search an image repository for visual content. We anticipate that these tools will empower engineers to more rapidly learn new lessons from earthquakes using reconnaissance data. 

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4. Intersectional Impacts of the 2021 Mw 7.2 Nippes, Haiti, Earthquake from Geotechnical and Social Perspectives

   https://doi.org/10.1785/0120220118  

   Cabas, A.; Lorenzo-Velazquez, C.; Ingabire Abayo, N.; Ji, C.; Ramirez, J.; Garcia, F.; Pérodin, J.; Hwang, Y.; Dashti, Y.; Ganapati, N.; et al (January 2023, Bulletin of the Seismological Society of America)

   Eric Calais; Silvia Chacón-Barrantes; Roby Douilly; O’Leary Gonzalez; Xyoli Pérez-Campos; Richard Robertson, Elizabeth Vanacore (Ed.)

   The Mw 7.2 Nippes, Haiti, earthquake occurred on 14 August 2021 in Haiti’s southwest peninsula and in the midst of significant social, economic, and political crises. A hybrid reconnaissance mission (i.e., combined remote and field investigation) was coordinated to document damage to the built environment after the event. This article evaluates two ground‐motion records available in Haiti in the context of the geology of the region and known areas with significant damage, such as Les Cayes. We also present a new map of time‐averaged shear‐wave velocity values to 30 m depth (⁠VS30 ⁠) for Les Cayes and Port‐au‐Prince based on the geostatistical approach of kriging and accounting for region‐specific geology proxies and field measurements of VS30 ⁠. Case studies of ground failure observations, including landslides and liquefaction triggering, are described as well as the intersection of social and engineering observations. Maps depicting this important intersection are provided to facilitate the assessment of how natural hazards and social conflicts have influenced the vulnerability of Haiti’s population to earthquakes. 

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5. Nonlinear Structural Model Parameter Updating Using Residual Drift Measurements from Sequential Seismic Events

   https://doi.org/10.1061/JSENDH.STENG-15117  

   De, Budhaditya; Burton, Henry V (October 2025, Journal of Structural Engineering)

   Model parameter updating can enhance the use of nonlinear structural response simulation to guide decision-making in the post-earthquake environment. Since most structures in high seismic regions are not instrumented with sensors, the response history during ground shaking is usually not available after an earthquake. Nevertheless, technologies such as Light Detection and Ranging (LiDAR) and drone-mounted imaging devices have increased the feasibility of measuring residual deformations after the shaking has subsided. It is within this context that a framework for performing nonlinear structural model parameter updating based only on residual drift measurements is proposed. The considered setting is one where a structure is subjected to a sequence of ground motions (without repairs), whereby after each event, the structural model parameters are updated using a Bayesian formulation and the measured residual drift. The methodology is demonstrated by using experimental data from a reinforced concrete bridge pier subjected to six back-to-back ground motions with significant residual drifts recorded after the third, fourth and fifth records in the sequence. The results showed that the updating procedure is able to incrementally (after each record) improve the accuracy of both the concrete and steel model parameters which also enhanced the estimates of the simulated peak and residual drifts. 

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