Binary logistic regression approach for decision making in bridge management

Bridge management professionals need effective tools to help guide the decision making process and maintain quality infrastructure in a region. Using logistic regression for a novel binary response defined by at-risk and not-at-risk bridges based on their existing overall condition scores, the probability of a bridge being at risk is expressed in terms of the primary bridge factors age, load, the type of construction material and structural design and conditions of the deck, superstructure and substructure. These estimated probabilities multiplied by specified consequence values are used to introduce the risk classes and their ranks. Employing the method for training and validating sets of sizes 13 540 and 3385 in 2017 and 13 481 and 3370 in 2018 data in the National Bridge Inventory (NBI) Indiana, a statistically significant model is established containing the age, load and conditions of both superstructure and substructure. Moreover, at-risk bridges are identified from Indiana NBI data in both years and for a subset from Connecticut in 2017. The innovative bridge-ranking tool prioritises bridges for maintenance purposes such as replacing or repairing and hence efficiently guides the management in the decision making process for capital expenditures and perhaps for predicting the missing overall bridge condition.