Journal of Infrastructure Systems

Current trends along with results of the 1995 ASCE Education Conference are reviewed and discussed herein. In addition, several suggestions concerning civil infrastructure systems and engineering education are offered to stimulate readers' interest for further improvement.

The objective of life-cycle costing (LCC) analysis is to optimize the total cost of ownership over the life span of an asset. A survey was conducted in the early months of 1995 of the largest municipalities in the U.S. to investigate the use of LCC analysis. The survey indicated that 40% of the municipalities use LCC analysis and that some had been using it for over 20 years. The reasons why 60% of the municipalities did not use LCC analysis include the lack of formal guidelines and the difficulty of estimating future costs and incomes. Other aspects of the survey included the typical profile of the LCC analysis process including project types, work categories, and project stages. The criteria used in selecting projects for LCC implementation appear to be arbitrary. Finally, most municipalities assessed their LCC analysis process as a successful or a somewhat successful operation.

Planning for the maintenance and improvement of water navigation infrastructure in the United States is a difficult task exacerbated by the uncertainties and variability in natural, engineering, and economic conditions. The Risk Analysis for Water Resources Investments Research Program in the Institute for Water Resources of the U.S. Army Corps of Engineers has been developing methods and tools to address this issue by incorporating risk analysis and management directly into the Corp's planning and decision making processes. In this paper we present the development of a risk-based spatial decision support system intended to assist in the planning of maintenance dredging activities for navigation channels. The spatial decision support system is based upon a mixed-integer mathematical programming model, the Reliability Based Dynamic Dredging Decision (RBD ³ ) model, that addresses the trade-offs in project costs and channel reliability for annual budgeting and dredge scheduling. A sample application is provided to represent the type of input requirements and to illustrate the types of output and analysis provided by this system.

Statistical ranking models are employed to extract information from an historical database on the prioritization of maintenance and repair operations for embankment dams within the U.S. Army Corps of Engineers. This information is used to develop weighting functions that express the relative importance of general classes of maintenance and repair operations in an effort to implement a Function-Based Condition Indexing methodology. In particular, the weighting functions are used to convert the condition index vector [a representation of the condition of the embankment dam under several repair, evaluation, maintenance, and rehabilitation (REMR) objectives, one element per objective] into a condition indexing scalar (a representation of the overall condition of the embankment dam). Goodness-of-fit statistics are used to evaluate the ranking criteria models. The procedure is demonstrated on embankment dams for the REMR objectives of “Prevention of Surface Erosion” and “Collection of Performance Information” using the Automated Budget System (ABS) database of the Corps of Engineers. The statistical analysis indicates that physical parameters such a dam age and height have had a tremendous influence on the historical prioritizations for individual operations under the two REMR objectives studied even though these may not have been explicitly considered during the ranking process. Other important parameters such as reservoir size, fetch, downstream hazard, and economic effect of the dam should be investigated in the future to determine the weighting functions for the final version of the condition indexing system.

This paper reviews several prominent state of the practice prioritization methods, describes the ranking methodology for highway mobility improvements used by the Washington State Department of Transportation (WSDOT), and presents empirical results. The new mobility prioritization methodology constitutes a flexible, inclusive framework for assessing independent projects relating to all highway modes and responding directly to state policy goals. It uses a mathematical ranking algorithm to determine the optimal priority order of projects for programming on the basis of cost-efficiency, degree of community support, environmental impacts, mode integration, and land-use-plan compatibility. Because dollar values cannot be assigned to a full range of quantifiable criteria, the project benefit cost ratios are supplemented by the other four categories of risk criteria with disparate unit measures. Based on the first year of implementation, a strict adherence to priority order using the WSDOT prioritization method results in a slightly lower overall program cost-efficiency than might otherwise be achieved by optimizing the program's net present value. The overall effect is minor, but the nonmonetary criteria do improve the priority order of several projects while lowering the rankings of other projects. From a practical standpoint, the methodology is a tool for selecting a subset of highway mobility projects that provides increased knowledge of investment trade-offs given the budget constraints.