More Like this

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 character-istics and response of in situ soil deposits. Much of the data generated by a major earth-quake 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 geotech-nical engineering design procedures. New technologies are being employed to capture earthquake-induced ground deformation, including Light Detection and Ranging, Struc-ture-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. Moreo-ver, electronic data enables effective sharing and archiving of the measurements. Unantic-ipated 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. 

   more » « less
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. 

   more » « less
3. Geotechnical and geological reconnaissance observations of the 6 February 2023 Türkiye earthquakes

   https://doi.org/10.1177/87552930241281007  

   Moss, Robb_ES; Altunel, Erhand; Bassal, Patrick; Bray, Jonathan_D; Buckreis, Tristan_E; Cetin, Kemal_Onder; Clahan, Kevin; Duman, Emre; Frost, David; Hashash, Youssef; et al (October 2024, Earthquake Spectra)

   The 6 February 2023 Türkiye earthquakes and the accompanying aftershocks were a once-in-a-century catastrophe that has greatly impacted Türkiye and Syria. The repercussions of these events will have a lasting effect on the entire region. This article documents the geotechnical and geological observations performed by GEER (Geotechnical Extreme Events Reconnaissance) immediately following the events. Observations of ground damage, including surface fault rupture, liquefaction and lateral spreading, landslides and rock falls, and foundation failure of buildings, dams, and other civil infrastructure, are described herein. This article summarizes the key findings that were originally reported in the joint GEER-EERI (Earthquake Engineering Research Institute) reconnaissance report. The goal of these reconnaissance efforts is to document perishable data and disseminate it widely so that lessons can be learned from these events. 

   more » « less
4. Machine Learning Applications in Geotechnical Earthquake Engineering: Progress, Gaps, and Opportunities

   https://doi.org/10.1061/9780784484692.050  

   Cheng, Katherine; Ziotopoulou, Katerina (March 2023, Geo-Congress 2023)

   Rathje, E.; Montoya, B.; Wayne, M. (Ed.)

   The rise of data capture and storage capabilities have led to greater data granularity and sharing of data sets in geotechnical earthquake engineering. This broader shift to big data requires ways to process and extract value from it and is aided by the progress in methodologies from the computer science domain and advancements in computer hardware capabilities. General machine learning (ML) models typically receive a set of input parameters and run them through an algorithm to gain outputs with no constraints on the parameters or algorithm process. Three topic areas of ML applications in geotechnical earthquake engineering are reviewed and summarized in this paper: seismic response, liquefaction triggering analysis, and performance-based assessments (lateral displacements and settlement analysis). The current progress of ML is summarized, while the challenges and potential in adopting such approaches are addressed. 

   more » « less
5. Earthquake-Induced Liquefaction Manifestation Multiclass Prediction Utilizing Random Forests for the Canterbury Earthquake Sequence

   https://doi.org/10.1061/9780784485316.024  

   Cheng, Katherine; Busch, Pablo; Ziotopoulou, Katerina (February 2024, American Society of Civil Engineers)

   The abundance of post-earthquake data from the Canterbury, New Zealand (NZ), area can be leveraged for exploring machine learning (ML) opportunities for geotechnical earthquake engineering. Herein, random forest (RF) is chosen as the ML model to be utilized as it is a powerful non-parametric classification model that can also calculate global feature importance post-model building. The results and procedure are presented of building a multiclass liquefaction manifestation classification RF model with features engineered to preserve special relationships. The RF model hyperparameters are optimized with a two-step fivefold crossvalidation grid search to avoid overfitting. The overall model accuracy is 96% over six ordinal categories predicting over the Canterbury earthquake sequence measurements from 2010, 2011, and 2016. The resultant RF model can serve as a blueprint for incorporation of other sources of physical data such as geological maps to widen the bounds of model usability. 

   more » « less