Multivariate Feature Selection for Predicting Scour-Related Bridge Damage using a Genetic Algorithm

Abstract:

Scour and hydraulic damage are the most common cause of bridge failure, reported to be responsible for over 60% of bridge failure nationwide. Scour is a complex process, and is likely an epistatic function of both bridge and stream conditions that are both stationary and in dynamic flux. Bridge inspections, conducted regularly on bridges nationwide, rate bridge health assuming a static stream condition, and typically do not include dynamically changing geomorphological adjustments. The Vermont Agency of Natural Resources stream geomorphic assessment data could add value into the current bridge inspection and scour design. The 2011 bridge damage from Tropical Storm Irene served as a case study for feature selection to improve bridge scour damage prediction in extreme events. The bridge inspection (with over 200 features on more than 300 damaged and 2,000 non-damaged bridges), and the stream geomorphic assessment (with over 300 features on more than 5000 stream reaches) constitute “Big Data”, and together have the potential to generate large numbers of combined features (“epistatic relationships”) that might better predict scour-related bridge damage. The potential combined features pose significant computational challenges for traditional statistical techniques (e.g., multivariate logistic regression). This study uses a genetic algorithm to perform a search of the multivariate feature space to identify epistatic relationships that are indicative of bridge scour damage. The combined features identified could be used to improve bridge scour design, and to better monitor and rate bridge scour vulnerability.