Knowledge-driven stochastic reliable modeling for steel bridge deck condition rating prediction

Abstract

ABSTRACT The structurally deficient bridges increased from 6.2% to 7% of total bridges in California state. With this percentage, 7%; California state occupies one of the top states for bridges in “poor„ condition. Steel bridges represent about 11% of its bridge networks, so determining the condition rating objectively instead of subjectively is crucial. This paper aims to help significantly optimize the maintenance process by providing a rational basis for making decisions. This paper has integrated knowledge, stochastic analysis, Regression technique, and modeling to help the highway agencies to make a more reliable decision for future maintenance based on predicted conditions. Stochastic Regression models have been built using a training dataset extracted from the National Bridge Inventory (NBI) database for California State steel bridges, considering structural and operational parameters. A validation test has been performed using a new real dataset to measure observed data's correspondence to the predicted values. The results of Average Validity Percentage (85.6%) and Coefficient of Determination (R2 = 91.5%) show that the models' accuracy, the power, and scalability of integrating the knowledge-driven models are acceptable. The integrated developed models provide the infrastructure authority with actionable insights for smarter planning and maintenance decisions as better future outcomes.