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This data was gathered during the Geotechnical Extreme Events Reconnaissance (GEER) efforts following the February 6, 2023, Kahramanmaraş earthquake sequence. This dataset is comprised of terrestrial lidar scan point clouds that aim to capture liquefaction-induced building settlement, building-ground interactions, and ground deformations. The objective of the reconnaissance efforts was to capture perishable data on ground failures and liquefaction-induced infrastructure damage due to these earthquakes. Reconnaissance was performed from March 27 to April 1, 2023 in and around İskenderun, Hatay; Gölbaşı, Adıyaman; and Antakya, Hatay. Lidar scans were performed in İskenderun and Gölbaşı at selected liquefaction building sites. The reconnaissance sites were selected as those where there was evidence of liquefaction (e.g., ejecta) and liquefaction-induced building settlements, as well as building-ground interactions, and site access. The processed lidar data are included as .las point cloud files; raw data are included as .fls files. The point cloud data may be viewed and analyzed in point cloud analysis software, including the opensource software CloudCompare. Additional images of the surveyed buildings are included for reference. An explanation of the data types and structure is found in the README.pdf file. These data may be used to investigate earthquake liquefaction-induced building settlements, building-ground interactions, and liquefaction-induced ground deformations. These data will be of use and interest to engineers and researchers working in the area of liquefaction ground failures and building-ground interactions. Additional information and data from this reconnaissance are available in the GEER reports, which are referenced in the "Related Works" section.
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RapidLiq: Software for Near-Real-Time Prediction of Soil Liquefaction
RapidLiq is a Windows software program for predicting liquefaction-induced ground failure using geospatial models, which are particularly suited for regional scale applications such as: (i) loss estimation and disaster simulation; (ii) city planning and policy development; (iii) emergency response; and (d) post-event reconnaissance (e.g., to remotely identify sites of interest). RapidLiq v1.0 includes four such models. One is a logistic regression model developed by Rashidian and Baise (2020), which has been adopted into United States Geological Survey (USGS) post-earthquake data products, but which is not often implemented by individuals owing to the geospatial variables that must be compiled. The other three models are machine and deep learning models (ML/DL) proposed by Geyin et al. (2021). These models are driven by algorithmic learning (benefiting from ML/DL insights) but pinned to a physical framework (benefiting from mechanics and the knowledge of regression modelers). While liquefaction is a physical phenomenon best predicted by mechanics, subsurface traits lack theoretical links to above-ground parameters, but correlate in complex, interconnected ways - a prime problem for ML/DL. All four models are described in an acompanying paper manuscript. All necessary predictor variables are compiled within RapidLiq, making user implementation trivial. The only required input is a ground motion raster easily downloaded within minutes of an earthquake, or available for enumerable future earthquake scenarios. This gives the software near-real-time capabilities, such that ground failure can be predicted at regional scale within minutes of an earthquake. The software outputs geotiff files mapping the probabilities of liquefaction-induced ground failure. These files may be viewed within the software or explored in greater detail using GIS or one of many free geotiff web explorers (e.g., http://app.geotiff.io/). The software also allows for tabular input, should a user wish to enter specific sites of interest and ground-motion parameters at those sites, rather than study the regional effects of an earthquake. RapidLiq v.1.0 operates in the contiguous U.S. and completes predictions within 10 seconds for most events.
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- Award ID(s):
- 1751216
- PAR ID:
- 10344904
- Publisher / Repository:
- Designsafe-CI
- Date Published:
- Subject(s) / Keyword(s):
- soil liquefaction geospatial modelling near-real-time prediction
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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This data was gathered during the Geotechnical Extreme Events Reconnaissance (GEER) efforts following the February 6, 2023, Kahramanmaraş earthquake sequence. This dataset is comprised of terrestrial lidar scan point clouds that aim to capture liquefaction-induced building settlement, building-ground interactions, and ground deformations. The objective of the reconnaissance efforts was to capture perishable data on ground failures and liquefaction-induced infrastructure damage due to these earthquakes. Reconnaissance was performed from March 27 to April 1, 2023 in and around İskenderun, Hatay; Gölbaşı, Adıyaman; and Antakya, Hatay. Lidar scans were performed in İskenderun and Gölbaşı at selected liquefaction building sites. The reconnaissance sites were selected as those where there was evidence of liquefaction (e.g., ejecta) and liquefaction-induced building settlements, as well as building-ground interactions, and site access. The processed lidar data are included as .las point cloud files; raw data are included as .fls files. The point cloud data may be viewed and analyzed in point cloud analysis software, including the opensource software CloudCompare. Additional images of the surveyed buildings are included for reference. An explanation of the data types and structure is found in the README.pdf file. These data may be used to investigate earthquake liquefaction-induced building settlements, building-ground interactions, and liquefaction-induced ground deformations. These data will be of use and interest to engineers and researchers working in the area of liquefaction ground failures and building-ground interactions. Additional information and data from this reconnaissance are available in the GEER reports, which are referenced in the "Related Works" section.more » « less
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