The establishment of clear performance measures can help agencies to assess the extent to which a bridge program, project, or policy has succeeded or is expected to succeed in achieving intended goals and objectives. Chosen properly, a set of performance measures can adequately describe the full consequences of competing bridge actions and thereby help identify the most desirable. Typical bridge management goals and performance measures considered by bridge decision makers are identified. The research summarizes the best practices in quantifying a number of performance measures related to these goals. There is discussion of desirable properties not only of individual performance measures but also of any set of performance measures intended for any particular bridge evaluation problem.
In the current era of greater stakeholder participation, limited funding, and increased user awareness, bridge managers are using multiple-criteria techniques in their decision-making processes so that their decisions can be more accountable and transparent. Issues that arise in making such decisions include the multiplicity of the performance criteria and associated decision constraints and the large bridge inventories in most states. These issues translate into increased problem dimensionality, many variables, and excessive computational time. This paper presents alternative formulations and methods for decision problems, identifies alternative algorithms for implementing the solutions, and evaluates the performance of the algorithms. The algorithms include the incremental utility–cost ratio (IUC), Lagrangian relaxation, and pivot and complement methods. In investigating the suitability of these algorithms for agencywide bridge management, the study carried out computational experiments with field data. The criteria for evaluating the performance of the algorithms were computational speed, accuracy, simplicity, and robustness. The results were unequivocal: of the three heuristics, IUC consistently performed the best. IUC provided the simplest and quickest way to compute the optimal solution when small changes were made to the input parameters without the need to redo the entire optimization for each budget level.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.