Journal of Infrastructure Systems

Inductive loop detectors have been widely used in transportation system, particularly to monitor the performance of highway systems such as traffic flow, speed, and vehicle classification. Accurate data are dependent on the quality of the detector mechanism and the level of maintenance of these devices. Yet, this aspect of maintenance has been neglected both in the field and in research studies. A review of the lifetime data of loops revealed the following features: (1) lifetime data for a specified loop was not available from existing maintenance records; (2) lifetime data were censored; and (3) the lifetimes of loops located in different stations were dissimilar. For these reasons, a censored random-effect panel data duration model was used in this study that can deal with heterogeneity and censored duration data. A formula was developed to estimate the lifetimes for individual loops, despite the unavailability of lifetime data in the original maintenance database. Because vehicles of different classifications were distributed differently in adjoining right lanes, different models were adopted for two different traveling lanes. The models indicate that the lifetimes of loops are highly determined by the volumes of truck traffic; a finding that can be used to assist loop detection maintenance scheduling.

Infrastructure systems are essential to the operation of health care facilities and do not exist in isolation of one another—telecommunications networks require electricity, transportation networks require systems information to operate, emergency systems require transportation networks, and so forth. During a disaster event, health care facilities are expected to operate efficiently to provide sufficient care to injured patients. However, medical care for injured patients can be affected if health care facilities do not have a sufficient supply of electricity, water, access to road transportation networks, etc. This paper presents a methodology to assist in the analysis of the operational vulnerability of a health care facility during disaster events, considering the impact of disruption of a selected number of critical infrastructure systems in the flow of patients. It integrates the analysis of external infrastructure systems and the internal capabilities of the facility. The analysis of external infrastructure systems is performed using mathematical models that use optimization techniques to determine the unsatisfied demand in the major infrastructure systems and the impact of this shortage of resources on the operation of the hospital. A system dynamics simulation model is used to represent the internal operation of the health care facility, including the interaction between the different service areas, the flow of patients within the facility, and the condition of the external infrastructure systems that supply its resources. The framework and modeling presented in this paper can assist in determining cost-effective operational strategies in a health care facility that better respond to disaster events because they consider the interdependencies between infrastructure systems.

Studies indicate an urgent need to upgrade the nation’s aging and deteriorating water distribution systems so they can provide customers with reliable and safe water supplies. In response, water utilities apply performance measurement initiatives including pipeline asset management, which requires condition assessment to identify failure-prone pipes and prioritize their renewal. However, the below-ground location of pipes and lack of standard guidelines or tools make pipeline assessment and renewal decisions difficult. This paper presents a model that uses data commonly found in utilities to assist them with their pipe renewal decisions. The model has unique features that increase its responsiveness to industry needs, and it was developed and tested with collaboration of utility professionals. Two case studies illustrate the model’s application to the study of pipes in early and late performance stages while addressing the data limitations and risk averse characteristics of the industry.

Count data arise in a number of infrastructure assessment problems such as modeling traffic accidents, pipe breaks in water distribution systems, and electric power outages. A common goal in these problems is to model the number of occurrences of an event of interest in the future based on past data. There is usually a great deal of variability in the past data, but there is a considerable amount of other information available that can help inform the models. A number of statistical models have been proposed and used for modeling count data in infrastructure assessment, including linear regression and generalized linear models. This paper summarizes these approaches and their past uses in infrastructure assessment. It then gives an overview of a class of models called generalized additive models that can incorporate nonlinear relationships between explanatory variables and counts of events in a flexible manner. Throughout the paper, the focus is on the practical usefulness of the different models, and an actual data set is used to demonstrate the different models.

Critical infrastructures maintain our society’s stability, security, and quality of life. These systems are also interdependent, which means that the disruption of one infrastructure system can significantly impact the operation of other systems. Because of the heavy reliance on electricity production, it is important to assess possible vulnerabilities. Determining the source of these vulnerabilities can provide insight for risk management and emergency response efforts. This research uses data warehousing and visualization techniques to explore the interdependencies between coal mines, rail transportation, and electric power plants. By merging geospatial and nonspatial data, we are able to model the potential impacts of a disruption to one or more mines, rail lines, or power plants, and visually display the results using a geographical information system. A scenario involving a severe earthquake in the New Madrid Seismic Zone is used to demonstrate the capabilities of the model when given input in the form of a potentially impacted area. This type of interactive analysis can help decision makers to understand the vulnerabilities of the coal distribution network and the potential impact it can have on electricity production.

The objective of this research was to determine the performance characteristics of thermoplastic pavement markings in North Carolina and to create viable degradation models. Additionally, an evaluation of paint pavement-marking materials was conducted and models were created for them as well. The resulting models provide pavement-marking managers with valuable tools that will allow them to focus limited resources where they are most needed and avoid replacing materials with effective life still remaining in them. Consideration was given to the analysis of the relationships between pavement-marking retroreflectivity values and variables such as time, traffic volume, and marking color. Unique to this research is the inclusion of lateral location as a key variable affecting the performance of pavement markings. Another key contribution to pavement-marking management from this research was the development and use of level of service increments for describing the status of a given marking at some point in time. Using the tools reported herein an expected service life for thermoplastic and paint pavement markings in North Carolina can be estimated. A key finding of the research is that the markings have a far greater life expectancy than originally expected. Combining our predictive tool with level of service increments provides a holistic infrastructure management approach to pavement markings.

Management of our critical infrastructures is a vital component of our economic and homeland security policy. The degree to which these infrastructures are interdependent, due to increasing system complexity and technology, further complicates the task of infrastructure managers to maintain service. In this paper we use economic models of the U.S. economy to identify hidden interdependencies in the supply chains of infrastructure. Critical connections between infrastructure systems, whether direct or indirect, pose a risk of one disruption causing a ripple effect across the economy. Our analysis shows that in many cases, the highest interdependencies between critical infrastructures occur upstream (e.g., in the second or third-level of the supply chain). Specific results are shown for the large upstream interdependencies (up to 10 times the direct dependencies) between transportation and power generation sectors. By revealing these upstream interdependencies infrastructure managers can take further preventative measures or make additional investments to avoid future infrastructure disruptions.

This study analyzed the environmental impacts of a single-story residential building using different exterior wall systems. The environmental impacts over the complete life cycle of the residential buildings were determined and compared using the following indicators: air pollution index, energy consumption (embodied and operational), global warming potential, resource use, solid waste emissions, and water pollution index. The exterior wall systems analyzed were concrete block, poured in-place concrete, insulated concrete, traditional $0.05\text{m}\times 0.1\text{m}$ by 0.4 m ( $2\text{in}\text{.}\times 4\text{in}\text{.}$ by 16 in.) on center wood frame, traditional $0.05\text{m}\times 0.15\text{m}$ by 0.6 m ( $2\text{in}\text{.}\times 6\text{in}\text{.}$ by 24 in.) on center wood frame, and steel stud framing. Additionally, structural insulated panels were modeled for the operational energy of the building. For each scenario, the designs were based on the minimum $R$ value required by the International Building Code. ATHENA, a life-cycle assessment software tool, and eQuest, an energy usage modeling software tool that calculated the operational energy of the building, were used to evaluate the environmental impacts of the building, during the construction, use, and end of life phases, for each scenario. The results show that in the preuse phase, the insulated concrete buildings produce the greatest impact on the environment followed by the concrete block, poured in-place concrete, and steel stud buildings. Traditional wood frames had the fewest environmental impacts when considering only the preuse phase. In the use phase, the insulated concrete buildings had the lowest impacts to the environment because they required less operational energy, whereas concrete block, poured in-place concrete, traditional $0.05\text{m}\times 0.15\text{m}$ by 0.6 m ( $2\text{in}\text{.}\times 6\text{in}\text{.}$ by 24 in.) on center wood frame, traditional $0.05\text{m}\times 0.1\text{m}$ by 0.4 m ( $2\text{in}\text{.}\times 4\text{in}\text{.}$ by 16 in.) on center wood frame, and steel stud framing had progressively larger environmental impacts. The end of life phase was negligible with respect to the other phases. In the complete life-cycle assessment (LCA), 50-year life span, insulated concrete exterior walls used around 700 GJ (5%) less energy than traditional wood $0.05\text{m}\times 0.15\text{m}$ by 0.6 m ( $2\text{in}\text{.}\times 6\text{in}\text{.}$ by 24 in.) on center wall systems. The results also indicate the importance of exterior wall’s thermal mass for the energy performance of a building, especially for a city located in a hot climate zone such as Phoenix, Arizona, and the importance of a holistic approach, such as LCA, to properly assess the negative environmental impact of different technologies.

Condition assessment of subway stations is a major issue facing public transit authorities worldwide. The Société de Transport de Montreal (STM) requires a rehabilitation budget of CAD 643.6 million (2006–2010) for its aged stations. The STM and most transit authorities lack planning strategies that reflect this increase due to deficiency of condition assessment models and scarcity of existing models. The research presented in this paper assists in developing a condition assessment model (subway station diagnosis index). The model identifies and evaluates the weights of different functional (structural/architectural, electrical, mechanical, and security/communication functions) condition criteria for subway stations using the analytical hierarchy process. It also utilizes both the Preference Ranking Organization METHod of Enrichment Evaluation and the Multiattribute Utility Theory to determine the station diagnosis index (SDI). Data are collected from experts through questionnaires and interviews. A case study in the STM subway stations network is performed. Data analysis shows that structural and security criteria are the most important (36.1 and 27.3%, respectively). The STM stations are found deficient, with an average SDI of 4.4 out of 10. This research is relevant to industry practitioners and researchers, since it provides a condition assessment tool and a unified universal scale for subway stations.

Although transportation agencies in the United States have been developing asset management systems (AMS) as decision-support tools for specific types of infrastructure assets, there are several barriers to the implementation of AMS. In particular, implementation and development costs are critical issues. Without evidence of AMS benefits, further implementation and development of AMS may be constrained. This paper documents the development of a generic methodology for quantifying the benefits derived from implementation of AMS and justifying investment in AMS implementation. The generic methodology involves three analysis methods: descriptive analysis, regression analysis, and benefit-cost analysis. These methods draw on basic principles of engineering economic analysis and apply to two types of evaluations: an ex post facto evaluation and an ex ante evaluation depending on the time frame and the availability of time series data. While the concepts are relatively simple, the challenge lies in identifying data to support the application of the methodology. This paper demonstrates how the methodology can be applied to evaluate the implementation of a pavement management system in terms of efficacy, effectiveness, and efficiency (three Es).

An index-based method for the evaluation of the environmental impact of water distribution systems is introduced. The method is developed according to a set of methodological criteria to assess the environmental effect of water networks. A number of environmental measures are incorporated into the index-based method to account for nonrenewable resource consumption, greenhouse gas emissions, and emissions to air, land, and water. The index-based method can be included into a multiobjective optimization program to find design solutions that minimize cost and environmental impact. The index-based method was applied to the design of a water transmission system over a 20-year design horizon. Design alternatives were compared on the basis of the environmental impact index and cost. For the system analyzed, results indicated that of the hydraulically feasible alternatives, the five most environmentally acceptable alternatives were the five most cost-effective alternatives. Results also showed that the environmental impact index is mainly influenced by pumping energy and partially influenced by pipe diameter selection.

Swedish contracting of winter maintenance is combined with weather-regulated payment since the beginning of the 1990’s. It reduces the weather risk for the contractor and has helped performance-based specifications to find acceptance. The performance-based approach was introduced to save costs and create incentives for innovation. Theory and practice are often unconnected since the mathematical links are lacking behind the political rhetoric around performance-based contracting. This study attempts to bridge some of the gap between theory and practice by linking a climate factor, snow, to highway maintenance costs. To handle the complexity of weather, an iterative process was assumed necessary, with simplification as a first step. An answer was achieved to the two research questions: which stations correlated best with costs and what the winter maintenance cost would be, given the snowfall at these stations. Once having achieved this answer under simplified circumstances (one weather parameter, one region, few observations, linear relationship), the assumptions were relaxed, one after the other, in second and third iterations, to increase the generalizeability of the method and the results. Maintenance cost and weather data of Washington state were used. The method can be used anywhere where the actual costs of the maintenance are known. In countries and regions with privatized highway maintenance, like in Sweden, British Columbia, Alberta, and Ontario, the actual costs are not public. The lessons learned from this study may therefore be of particular interest to such privatized systems. The study supports that the Swedish road weather information system stations are in the right spots and proposes snowfall to weather regulate the payment to the contractor.