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Slope engineering: GIS aids US slope failure prediction

2018 05 p1 image

Multiple slope failures in Minnesota has led to development of tools to help predict such incidents that could have wider benefits

Predicting when geotechnical infrastructure assets will fail is the holy grail for the industry in terms of minimising remediation costs and reducing risk to the travelling public. This aspiration gained considerable impetus in the US state of Minnesota where heavy rainfall in 2014 caused a large number of slope failures and led to new research.

Pilot studies ranking the failure potential of every slope adjacent to the roads in two Minnesota counties could hold the key to predicting failures elsewhere on the network. The research for the Storm-Induced Slope Failure Susceptibility Mapping project was led by the Minnesota Department for Transport with support from wider industry.

Using publicly available data, research findings and geotechnical theory, researchers develop failure models that could then be mapped with geographic information system (GIS) in two topographically dissimilar Minnesota counties.

These maps would identify slopes susceptible to failure in Carlton County and Sibley County so that county highway departments could develop preventive strategies for protecting roadways from potential slope failure or prepare appropriate failure response plans.

“GIS mapping has been applied to very small watersheds, says Barr Engineering Company senior water resources manager Omid Mohseni, who led the project. “The two counties in this study are huge areas in comparison. We used a physics-based approach that shows engineers where slope failure is likely to occur.”

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A detailed GIS map of County Highway 210 was colour-coded to show slope failure susceptibility along the route

Researchers began with a literature review of studies about the causes of slope failure, predictive approaches and mapping. They were particularly interested in research related to potential failure mechanisms, algorithms used for predicting failures and slope-failure susceptibility mapping.

Then investigators collected data on known slope failures in Carlton County in eastern Minnesota and Sibley County in south central Minnesota to identify failure-risk factors not found in the literature. Researchers reviewed various statewide data sets, identifying topographic, hydrologic and soils information that could be used in GIS-based modelling. Next, they developed a GIS-based slope-failure model by incorporating the available data with geotechnical theory and probability factors from hydrologic data, and writing computer code to allow the data to be input into mapping software.

Researchers tested the software on known failure sites to refine soil parameter selection and failure models. The refined models and software were then used to identify and map slope failure risks in Carlton and Sibley counties.

After analysing the literature and the failure and geotechnical data, researchers identified the following key causal factors in slope failure: slope angle, soil type and geology, vegetation, land use and drainage characteristics, soil moisture, and rainfall intensity and duration.

Researchers then developed mapping models for the two counties using three key data sets. The first was data from 3-meter resolution, high-quality lidar, which measures distances with laser range finders and reflected light, available through Minnesota’s Department of Natural Resources website. The team augmented this data with US Department of Agriculture soils survey data, and with National Oceanic and Atmospheric Administration and National Weather Service hydrologic data for precipitation and storm duration information.

Based on research in geotechnical theory, researchers developed algorithms for anticipating failure and built these into the lidar-based topographic mapping model. They also developed input parameters based on the failure factors and established output parameters representing five levels of failure susceptibility: very low, low, moderate, high and very high.

After testing the GIS-based model against a slope along County Highway 210 in Carlton County, researchers confirmed that failure potential correlated well with documented or observed slope failure. The team further validated the model by applying it to several small areas in the adjacent Carver and Sibley counties, finding similarly effective correlation with identifiable failure sites.

Independent geotechnical experts examined the modelling software and further refined geotechnical, soil and hydrologic elements. Finally, the team developed maps of Carlton and Sibley counties that assigned failure susceptibility levels to slopes in the two counties. Viewing maps through the software remains the most useful way to examine slopes, although large-format maps are available.

“If county engineers have higher slopes adjacent to roadways, they can use this basic tool to predict slope failures and then hire a geotechnical consultant to investigate the site,” says Sibley County public works director Tim Becker.

According to the research team, mapping could be extended to every county in Minnesota to further refine failure modelling if further funding was available. Maps may also be useful in identifying structures such as roadways, ecological features, transmission lines and pipelines, bridges and culverts that may be threatened by slope failure susceptibility. Potential risks could be used to prioritise slope treatment plans.

Minnesota Department of Transport has said that another project is already underway to identify, map and rank slopes that are vulnerable to failure that could affect the highway network.

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