Identification and Characterization of Damaging Road Events

In the field of vehicle durability, many individuals are focusing on methods for better replicating the durability a user will experience throughout the typical design lifespan of a vehicle (e.g., 100,000 miles). To estimate user durability a means of understand the types of damaging events and driving styles of uses must be understood. The difficulty with accurately estimating customer usage is, firstly, there is a large pool of possible roads for a user to drive along, for example, there are over 4 million miles of public roads in the United States, alone [1]. In addition, while measurements of these surfaces could be collected it would be impractical for two reasons, the first is the financial and extreme time burden this would take. Second, when collecting measurements of a road surface only the current state of a road surface can be measured, thus as a road deteriorates or is repaved the measurements collected would no longer be an accurate representation of the road. It should be mentioned that even, if all of the road surfaces were measured performing simulation and analysis of all of these road surfaces would be computationally intensive. Instead, it would be beneficial if select events that account for a significant portion of the damage a vehicle experiences can be identified. These damaging events could then be used in more complex vehicle simulation models and as input and validation of proving ground and laboratory durability testing. The objective of this research is to provide a means for improved estimation of vehicle durability, specifically a means for identifying, characterizing, and grouping unique separable damaging events from a road profile measurement.

In order to achieve this objective a measure that can be used to identify separate damaging events from a road profile is developed. This measure is defined as Localized Pseudo Damage (LPD), which identifies the amount of damage each individual road excitation makes to the total accumulated damage for a single load path in a vehicle system. LPD is defined as a damage density to minimize the effect of measurement spacing on the resulting metric. The developed LPD measure is causal in that the value of LPD at a location is not affected by any future locations. In addition, for a singular event (e.g., impulse or step) in the absences of other excitations, the LPD value at the singular event location is equivalent to the total pseudo damage divided by the step size at the location.

Once a measure of pseudo damage density is known at multiple locations along a road profile for multiple load paths of interest, then separable damaging events can be identified. To identify separable damaging events the activity of the vehicle system must be considered because separate damaging events can only occur when a region of inactivity is present across all load paths. Subsequently, an optimization problem is formed to determine the optimal active regions to maintain. The cost function associated with the optimization problem is defined to minimize the cost (number of locations maintained in damaging events) and maximize the benefit (the amount of pseudo damage maintained).

Lastly, a statistical test is developed to assess if separate damaging events can be considered to be from the same general class of events based on their damage characteristics. The developed assessment methods establish the similarity between two more separable damaging events based on application specific user defined inputs. In the development, two example similarity metrics are defined. The first similarity metric is in terms of distance and the second is in terms of likelihood (probability). The developed statistical analysis uses the current state-of-the-art in clustering algorithms to allow for multiple damaging events to be identified and grouped together.