Infrastructure Condition Assessment, Deterioration Modeling, and Maintenance Decision Making: Methodological Advances and Practical Considerations

Infrastructure systems have long been recognized as a fundamental foundation of societal and economic functions such as communication, energy distribution, transportation, wastewater collection, and water supply. These infrastructure systems are both geographically extensive and long-lived. Providing and managing the physical infrastructure over spatially extensive areas and long time spans is costly. The spatial and temporal extensiveness of infrastructure systems also impose a high degree of uncertainty, which complicates the planning for future infrastructure provision, as well as for maintenance, repair, and reconstruction of existing systems. High costs, tight budgets, and previous decisions that were based on erroneous predictions of infrastructure performance seem to be resulting in serious consequences. The National Council on Public Works Improvement concluded in its 1988 report Fragile foundations: A report on America’s public works, that “the quality of America’s infrastructure is barely adequate to fulfill current requirements, and insufficient to meet the demands of future economic growth and development.” Similar and even more serious shortfalls in infrastructure systems around the world were highlighted by the World Bank in World development report 1994: Infrastructure for development (Oxford University Press, Oxford, U.K., 1994).

Over the past two decades, serious effort has been directed at improving and managing infrastructure systems. In particular, the academic community has focused on methods for better inspection, condition assessment, deterioration forecasting, and maintenance decision-making. However, as noted in a June 22, 1987 letter to the above named council by George Latimer, former mayor of the City of Saint Paul, Minnesota, the infrastructure problem is “much more likely to be solved—eventually—through deliberate remedial steps rather than through a fast, dramatic ‘conversion’ of practices and attitudes.” Indeed, while the complexities that infrastructure systems entail call for more research, transferring new methods into practice appears equally critical in achieving measurable success. As several of the papers published in this special issue attest, researchers are becoming increasingly cognizant of the importance of this technology transfer by addressing relevant problems, better utilizing more pertinent data, and explicitly addressing barriers to practical implementation.

This special issue is the first of two reflecting recent advances in infrastructure condition assessment, deterioration modeling, and optimal maintenance, repair, and reconstruction. The papers published in this issue relate to a variety of infrastructure sectors, including coastal steel facilities, roadway pavements, sanitary sewers, and water distribution pipes.

Grigg summarizes the state-of-the-art condition assessment for water distribution pipes, identifies principal issues faced by utilities regarding condition assessment, and presents an extensive discussion of the research needs as motivated by the state-of-the-practice. He also proposes a framework to organize the necessary assessment activities in an effective manner for asset management. The framework is developed to take deliberate account of the decisions the condition assessment results are intended to support, as well as the regulatory controls presently in place. He also discusses barriers that limit the ability of utilities to achieve effective condition assessment and suggests options to overcome these barriers.

Melchers presents recent advances in understanding the corrosion of coastal steel facilities. Such advances are captured by the identification of environmental and material-related factors influencing corrosion and by the development of statistical models estimated from both laboratory and field observations, allowing for better characterization of in-situ conditions. An example application is presented to illustrate the value of such rich models in forecasting the effects of corrosion.

The rutting progression model estimated by Archilla takes into account the effects of design, resurfacing, loading, and the environment. The mixed-effects estimation approach he uses takes full advantage of the panel nature of the data set generated from the AASHO road test, to capture unobserved heterogeneity across the repeatedly observed pavement sections. The approach produces parameter estimates that are markedly different from those resulting from more restrictive estimation approaches. In addition, the relative impacts of the causal factors are also found to be different.

Motivated by the practical need to plan for sewer rehabilitation in California, Wright et al. developed a prioritization approach using discriminant analysis to account for the disproportionately high cost of misclassifying deficient sewer pipes. Their research is largely motivated from the authors’ observations of the poor performance of traditional regression techniques in estimating conditions of unobserved pipes. The practicality of the approach and the need to consider the results as aids to prioritization and cost estimation, rather than as strict prescriptions for rehabilitation, are demonstrated on the large sanitary sewer system that motivated the authors to seek practical, but effective innovations.

Hong et al. motivated and derived an approach to optimize replacement timing of aging buried municipal water pipes in light of the uncertainty inherent to predicting pipe failures. As opposed to some approaches considered in previous studies, where the alternatives evaluated have different planning horizons, the authors minimized the expected cost averaged over time. They also claimed that doing so will benefit sustainable planning. Numerical results illustrate that this approach can lead to systematic differences in optimal replacement timing.

Most researchers and practitioners recognize that infrastructure facility deterioration is not deterministic. However, as Madanat et al. recognize, the stochastic models and model parameters that are best used to represent the stochastic deterioration are also uncertain. Whereas empirical observations of deterioration data can reduce the uncertainty, the authors point out that producing the wide range of empirical observations that best facilitate learning and ultimately lead to lower cost actions can conflict with taking what are considered optimal actions based on present, and likely erroneous, representations of the uncertainty. Consequently, they propose the use of systematic probing to balance what are perceived to be cost minimizing actions based on present knowledge, with the importance of refining the deterioration model to improve future actions. The authors formulate a systematic probing approach in the context of selecting optimal infrastructure maintenance, repair, and reconstruction policies, and present numerical results to demonstrate the benefits of this approach.

These papers consider issues relevant to the practice of infrastructure management. For example, Grigg’s proposed condition assessment framework is situated in the practical setting utilities confront. Similarly, the method developed by Wright et al. was motivated when the authors encountered a limitation in solving an actual sanitary sewer prioritization problem; while Hong et al. explored alternative formulations for the optimal replacement timing of buried water distribution pipes—a serious problem facing many municipal water supply organizations. As for accurately forecasting conditions for the purpose of making sound provision and maintenance decisions, infrastructure managers can only hope to achieve effective results over the lifecycle of facilities when the complexities of deterioration phenomena are realistically captured by models, such as those presented and developed by Melchers and Archilla. Finally, Madanat et al. showed sensitivity to implementation issues relating to the systematic probing approach they develop for maintenance decision-making, by suggesting that much of the probing could be conducted on test facilities that are becoming more prevalent.

Equally important, the papers also reflect a broad set of methodologies that draw from a spectrum of disciplines, including material science, statistics, econometrics, stochastic processes, and operations research. For example, Archilla demonstrates that more than four decades after the AASHO road test, rigorous analysis and advanced statistical methods are able to generate a new and much richer understanding of pavement deterioration. Also, Madanat et al. showed the value of applying an innovative operations research approach to the longstanding problem of maintenance, repair, and reconstruction decision-making. The range of sectors, disciplines, and advanced methodologies reflected in the papers of this special issue, illustrates the value of forming an integrated infrastructure systems community, whose members rely on interdisciplinary approaches when addressing the pertinent problems at hand. It is such a community that the Journal of Infrastructure Systems continues to strive to build.

A decade after the publication of the Fragile foundations report discussed in the introduction, ASCE picked up the task of assessing and reporting on the state of infrastructure systems in the United States through its Report Card for America’s Infrastructure $⟨$ http://www.asce.org/reportcard/2005/ $⟩$ . While the mediocre to poor letter grades given for each of the infrastructure sectors by the original council, and later by ASCE, are highly subjective in nature, it is noteworthy that these grades have, for the most part, either remained constant or worsened between 1988 and 2005. Introducing rigorous interdisciplinary analyses and methods to address problems and issues relevant to practice across multiple sectors, as reflected in the papers of this special issue, is an encouraging sign for the eventual improvement of the state of infrastructure systems. Nevertheless, much more effort needs to be devoted to development, testing, demonstration, and dialogue to close the gap between research and practice.