A Platform for Proactive Risk-based Slope Asset Management Phase II – year 2 (2013-14)
PI: Keith Cunningham (UAF)
Co-Investigators: Michael J. Olsen (OSU), Joseph Wartman (UW)
Dates: 11/1/2013 – 6/30/2015
Unstable slopes, including coherent landslides, rock falls, and debris flows, present significant risk to safety and regional commerce. This risk is a long-term concern that highway managers contend with on an on-going basis. The widespread spatial and temporal distribution of these landslides poses a number of challenges when deciding when, where, and how to allocate funds for mitigation efforts to maintain these assets. This challenge is compounded by the high level of effort currently required to survey, inspect and sample slopes for the purpose of condition assessment as part of an asset management program. Slope assessment has traditionally been costly and laborious, limiting it to a few sites. However, routine assessment is altogether necessary due to the potential consequences of a failure. Current best-practices for management do not necessarily facilitate proactive slope management – identifying and remediating hazardous conditions before a failure occurs. Current inventory systems are time consuming to complete (years) and generally only provide basic information after a collapse has occurred and likely caused damage. As such, they do not provide an understanding of how risk varies with time and location. However, a proactive, near-automated approach for the identification of possibly unstable locations prior to catastrophic failure offers the potential to significantly enhance public safety and reduce overall operation and repair costs. Advanced technologies such as Mobile Laser scanning (MLS) show great promise in quickly and frequently assessing large sections of highway. Time-series datasets from the MLS system enable remote assessment of slope stability with a higher level of confidence than current probabilistic studies based on inventories due to a significantly higher spatial and temporal resolution achievable with MLS.The scope of the current PACTRANS-funded project Phase I, entitled “A Platform for Proactive Risk-based Slope Assessment” includes the development of qualitative relative risk model for slope stability assessment using terrain models created from MLS data. In the second phase of the work, we will focus on quantitative time-series analysis using MLS data and integrating this information into the model developed in the first phase of research and into an agency’s transportation asset/performance management program.