A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Probabilistic Field Assessment of Sinkhole Occurrence Using the Raveling Index
The electric cone penetrometer test (CPT) is becoming a popular subsurface investigation tool to understand a site’s soil stratigraphy and to estimate geotechnical design parameters. Moreover, CPTs are also a valuable tool to identify potential subsurface geohazards, such as potential liquefiable sand layers or subsurface karst anomalies indicative to sinkhole collapse. In this study, the authors review the current methodologies to detect and characterize subsurface karst soil anomalies and implement statistical regression procedures to set critical values of the sinkhole raveling index (RI). This was performed through analysis of 150 CPT soundings performed in Central Florida at sinkhole active sites. The RI value was calculated for each CPT, and the classification of CPT was categorized into severities of sinkhole formation based on its proximity to the reported sinkhole incident. Probability functions were then developed for the datasets, in turn relating RI data to the general probability of sinkhole manifestation at the ground surface. The results suggest that an RI value greater than 0.50 corresponds to an approximate range of 58–62% chance of sinkhole manifestation at the ground surface. Although this analysis technique does not incorporate the physical mechanism of internal erosion (i.e., raveling propagation over time), the developed analysis can still be used to better understand the potential of sinkhole formation within a project site when internally eroded soils are detected.
Probabilistic Field Assessment of Sinkhole Occurrence Using the Raveling Index
The electric cone penetrometer test (CPT) is becoming a popular subsurface investigation tool to understand a site’s soil stratigraphy and to estimate geotechnical design parameters. Moreover, CPTs are also a valuable tool to identify potential subsurface geohazards, such as potential liquefiable sand layers or subsurface karst anomalies indicative to sinkhole collapse. In this study, the authors review the current methodologies to detect and characterize subsurface karst soil anomalies and implement statistical regression procedures to set critical values of the sinkhole raveling index (RI). This was performed through analysis of 150 CPT soundings performed in Central Florida at sinkhole active sites. The RI value was calculated for each CPT, and the classification of CPT was categorized into severities of sinkhole formation based on its proximity to the reported sinkhole incident. Probability functions were then developed for the datasets, in turn relating RI data to the general probability of sinkhole manifestation at the ground surface. The results suggest that an RI value greater than 0.50 corresponds to an approximate range of 58–62% chance of sinkhole manifestation at the ground surface. Although this analysis technique does not incorporate the physical mechanism of internal erosion (i.e., raveling propagation over time), the developed analysis can still be used to better understand the potential of sinkhole formation within a project site when internally eroded soils are detected.
Probabilistic Field Assessment of Sinkhole Occurrence Using the Raveling Index
Shamet, Ryan (author) / Nam, Boo Hyun (author)
Geo-Congress 2020 ; 2020 ; Minneapolis, Minnesota
Geo-Congress 2020 ; 602-611
2020-02-21
Conference paper
Electronic Resource
English
Probabilistic Field Assessment of Sinkhole Occurrence Using the Raveling Index
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