Predicting Seismic-Induced Rockfall Hazard for Targeted Site Mitigation

Principle Investigator(s): Professor Michael J Olsen (OSU)
Associate Investigator(s): Professor Ben Leshchinsky (OSU), Professor Joseph Wartman (UW), Dr Chris Massey (GNS Science)
Graduate Student(s): Andrew Senogles
Starting Year: June 2017
Digital Appendix

Problem Statement:

Given the negative economic and community impacts of rockfalls, a targeted method for identifying the highest-risk rockfall areas along state routes is crucial to ensure a safe, efficient transportation system that can function during emergency events such as a Cascadia earthquake, while also maximizing the use of ODOT’s limited mitigation funding. Oregon’s highways traverse particularly unstable terrain throughout the state. Steep slopes, weak soil and rock, high rainfall, and unfavorable geology result in frequent maintenance, system unreliability due to frequent closures and restrictions, and safety hazards due to landslides and rockfalls. With over 4,000 unstable slopes manually identified to date at an average mitigation cost of over $3 million per site, together with a permanent mitigation budget of only $6 million per biennium in the STIP, thorough mitigation of all unstable slopes is neither economically feasible nor realistic. Currently, rockfalls are rarely stabilized and addressed beyond initial cleanup, even though the safety risk from rockfalls is high. Further complicating rockfall mitigation planning, those sites currently mitigated are nearing the end of their design lives. Moreover, none of these installed mitigations are designed for seismic events—which greatly increase rockfall activities and associated damage. This proposed research will develop a method to predict seismic rockfall areas by integrating two new complementary research products:

1) A lidar database of terrestrial highway surveys of adjacent rock slopes that span multiple earthquake events, and

2) A streamlined lidar-based rockfall hazard assessment method called RAI (Rockfall Activity Index).