| description abstract | Significant bridge inspection data has been collected over the years at the component and element level to improve management practices in the United States. A widely adopted systematic approach to correlate the weight or importance of the bridge elements to the overall bridge performance, which influences the maintenance, repair, and replacement (MRR) schedule and resource allocation for structures, does not exist given the existing data. Some transportation agencies use a cost-based approach to assign weights to bridge elements, which can be in terms of the loss accrued during downtime or the amount needed for the replacement of the element. However, this approach does not consider the bridge elements’ structural relevance to the overall performance of the bridge. This study proposes a novel framework to synthesize component and element-level bridge data to showcase their relationship using the random forest algorithm, which is essentially an ensemble of decision trees to evaluate the importance of different elements relative to the overall condition of the bridge. The analysis focused on eight bridge design types predominant in Delaware, Maryland, Pennsylvania, Virginia, and West Virginia, and analyzed 104,699 bridge records consisting of the condition rating and element-level data from the National Bridge Inventory (NBI). The random forest algorithm showed that bridge elements’ weight (or importance) is not constant as implied by the cost-based approach; rather, bridge elements’ weight varies based on their relevance to the bridge’s structural performance. The resultant bridge elements’ weight, which is the element weight multiplied by the component weight, can be used to improve the existing Bridge Health Index (BHI) equation found in the Manual for Bridge Evaluation (MBE) using this data-driven approach. Given more available component and element-level bridge data, this formulation provides a framework for transportation personnel to determine which set of bridge elements to prioritize in their maintenance actions and ascertain if the elements receiving the highest priority in the MRR schedule and budget allocation are also the same set of elements that bridge inspection reports regard as needing attention. The United States bridge inventory is made up of several bridge design types with distinct deterioration characteristics based on their structural configuration and needed to make decisions about maintenance and repair strategies. However, the method currently adopted by bridge owners to prioritize the repair of the many bridge parts (or elements) is largely dependent on the cost of repair and economic loss at the downtime of such elements as decided by experts, which introduces personal bias and does not account for the distinctions among the different bridge types available in the inventory (Chase et al. 2016; Inkoom and Sobanjo 2018). Given nationwide efforts to collect bridge inspection data, it is essential to consider a data-driven approach that derives the bridge elements’ importance from historical bridge inspection data and separates the bridge inventory into design types to innovatively determine the bridge elements’ importance (or weight) needed to compute the overall bridge health using historical condition state and condition rating data of bridge elements and components. This helps to capture in real time how the deterioration of one bridge part affects another part, which in turn helps to identify the bridge elements that most influence the overall condition of the bridge when prioritizing bridge repairs using the random forest algorithm. This paper showcases a data-driven approach within a novel framework used to assess the overall bridge health using random forest algorithms that track how the deterioration of small bridge elements affects the condition of the bridge components they are attached to, and the overall bridge condition, thus potentially improving the method for computing bridge element weights within the existing Bridge Health Index (BHI) formulation documented in the Manual for Bridge Evaluation (MBE). | |
