Rockfall Forecasting: A Probabilistic Approach

Rockfall along transportation corridors presents a significant danger to passing motorists in addition to potentially large capital cost associated with maintenance and road closures. Existing rockfall hazard rating systems, implemented by numerous state transportation agencies across the United States, serve to identify key slopes most prone to rockfall but do not provide any information regarding specific rocks that may fall or the timing of rockfall events. High resolution remote sensing technologies (such as LiDAR, photogrammetry, and GBInSAR), however, have permitted detailed identification of problematic blocks and their associated displacements leading up to failure. Such information has facilitated an ability to forecast the time to failure of specific rocks through repeated measurements at localized sites. Key drawbacks, however, are the high degree of uncertainty associated with these forecasts and the limited frequency at which measurements can practically be made. This ultimately undermines the ability to provide meaningful information that could be used, for example, to shut down a corridor precursory to a rockfall event. This paper presents a rockfall forecasting methodology cast within a probabilistic framework to assess probability bounds on the time to failure as well as update time to failure estimates as new information is gathered. This work is being conducted within a broader framework to incorporate remote sensing technology for effective and efficient characterization and prioritization of numerous sites on a network scale spanning several hundreds of miles.

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References;
  • Pagination: pp 211-225
  • Monograph Title: Proceedings of the 68th Highway Geology Symposium (HGS 2017)

Subject/Index Terms

Filing Info

  • Accession Number: 01642819
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Jul 13 2017 6:04PM