Performance Prediction Modeling of Concrete Bridge Deck Condition Using an Optimized Approach

Abstract

Developing an accurate and reliable model for concrete bridge deck deterioration rates is a significant step in improving the condition assessment process. The main goal of this study is to develop a deterioration prediction model based on the condition ratings of concrete bridge decks over the past 25 years as reported in the National Bridge Inventory (NBI) database. While the literatures have typically suggested the Markov chain method as the most common technique used in condition assessment of bridges, the analysis in this pilot study suggests that the lognormal distribution function is a better model for concrete bridge deck condition data. This paper compares the two approaches and presents a new approach that combines the more commonly used Markov chain method with the lognormal distribution function to arrive at an optimal model for predicting bridge deck deterioration rates. The prediction error in the combined model is less than each of the two models (i.e. Markov and Lognormal). Additionally, the steel structure type illustrated the highest deterioration rates within condition ratings from 8 to 4 Comparing with other types. The bridge decks that have ADT of more than 4,000 (vehicles/day) deteriorated faster than of those with ADT less than 4,000 with the same type of structure and skew angle. Bridge decks with skew angles more than 30º deteriorate faster than of those with skew angles less than 30°. Furthermore, it showed that most new Michigan concrete bridge decks may take at least 40 years before dropping gradually from 9 to 3.