Probabilistic Bridge Deterioration Prediction, Applied Analytics, and Predictive Modeling

BackgroundFrom the onset of modern civilization, bridges have been iconic images and vital transportation components that have helped connect our world.From early design forms using natural resources to modern engineering techniques and advances in building materials, bridge construction has evolved with humankind's increasing demands.Today's bridges are a part of daily routes for most people, and as a result, transportation agencies face a myriad of challenges to optimize the flow of goods and people.From ensuring the operability of aging structures to planning, financing, rehabilitating, and constructing modern replacements that improve accessibility, finding solutions to bridge challenges is essential to our social and economic development.Bridges serve as critical features of an infrastructure system and require regular inspections to ensure their safe and operable condition for the public.According to a Federal Highway Administration (FHWA) report [2], approximately 600,000 bridges exist in the United States as of 2010, more than 25% of which are either structurally deficient or functionally obsolete and in need of maintenance, AbstractDesigning a new bridge structure or designing the rehabilitation of an existing bridge is a time-consuming process.It is important for bridge owners to proactively assess their structures to make sure that by the time rehabilitation or replacement is required, the design phase elements of such a project, including necessary permits, are ready.Assessing future bridge structure condition also helps the owner to evaluate a variety of design choices or options based on the risk imposed by each alternative.Federal regulations (MAP-21) have required state transportation agencies to implement asset management performance measures.Asset management performance strategies are arguably principal in guiding transportation infrastructure investments.Predicting future asset condition helps businesses and engineers to allocate and utilize the available resources in a direction that keeps the existing bridge inventory in the best possible condition, as well as serves the public.Prediction of bridge future condition also assists engineers and bridge owners in making decisions based upon quality information and well-defined and considered objectives [1].The core element of any bridge management system is the database containing physical condition-rating data obtained through regular inspection and maintenance activities over a significant amount of time.Consequently, asset management relies on having an accurate inventory and condition assessment of assets in real time.One significant problem lies in the stochastic nature of traffic that transpires over time.This research focused on using the Markov process to predict future bridge condition state based on bridge inspection data inputs.Markov chain modeling was selected for this study because bridge condition ratings have a stochastic nature that requires an appropriate deterioration prediction method.The goal was to create a computational tool to predict bridge and highway degradation and assess such a structure's long-term performance.As a proposed update to the National Bridge Inspection Standard, this study offers two methods for making changes to bridge inspection intervals.Both ways consider risk a significant contributing factor to the deciding criteria, for which this paper presents methodology to perform such a risk calculation.