Statistical Forecasting of Bridge Deterioration Conditions

Abstract

The United States has more than 615,000 bridges. US national bridge inspection standards developed by the Federal Highway Administration (FHWA) require routine inspections of these bridges every 24 months regardless of bridge characteristics such as age, average daily traffic (ADT), and current deterioration condition of a bridge. Previous studies reported that this routine inspection process is considerably costly and inefficient. If the future condition of a bridge can be predicted accurately, costly routine inspections with uniform intervals can be avoided. The objective of this study is to create a forecasting model that predicts future bridge deterioration conditions based on the bridge characteristics. Historical data of more than 28,000 bridges in the state of Ohio from 1992 to 2017 were used to create an ordinal regression model to statistically examine effects of bridge characteristics on variations in bridge condition and predict future bridge conditions. The outcomes of this study indicate that bridge characteristics such as age, ADT, deck area, structural material, deck material, structure system, maximum length of span, and current condition of the bridge are statistically significant variables that explain variations in bridge deterioration. The results of the forecasting process show that the created ordinal regression model can statistically predict future bridge conditions precisely. This study will help bridge owners and transportation agencies accurately model and predict bridge deterioration and assign inspection and maintenance resources efficiently. The efficient inspection process, customized based on predicted deterioration condition, can result in investing the millions of dollars currently funding unnecessary inspections into much-needed infrastructure development projects.