Journal of Infrastructure Systems

In general, the nature of permanent earth retaining structures (ERSs) in highway engineering is well suited to the concept of asset management as a valuable tool for operational efficiency and cost control. Earth retaining structure failures can be detrimental to the roadway and the surroundings, and may pose potential hazards to the safety of the public. Work in this paper utilized field survey results from 11 ERSs to illustrate the disadvantages of using a numerical rating system that relies on a single average number, and the applicability of the alternative system presented herein. The ERSs evaluated include mechanically stabilized earth (MSE), soil nail, anchored, gravity, and cantilever earth retaining wall types. Results indicated that the use of a single-value numerical rating can mask deficiencies in ERS elements that are critical to the stability and function of the ERS system. In addition, the identification of specific elements in distress is not apparent when using such an approach. An alternative two-part rating system provides a summary rating that articulates both the condition of the ERS and identifies potential problems with specific ERS elements. As transportation agencies attempt to align with Moving Ahead for Progress in the 21st Century Act (MAP-21), a two-part condition rating system is recommended to better execute an ERS asset management program and integrate it into a systemwide plan.

Because failures on railway lines have nonzero probabilities of occurrence and can result in significant costs if they occur, railway infrastructure managers are interested in determining intervention programs that best reduce this risk, taking into consideration their budget constraints. In this paper, a base model is proposed to determine optimal risk-reducing intervention programs for railway lines based on the states of its objects and budget availability. The base model is an integer nonlinear model with an objective function that maximizes net benefit without exceeding budget constraints. The net benefit is the difference between the amount of risk reduction and the costs of executing interventions in terms of both direct costs (i.e., materials and labor forces) and indirect costs (i.e., the travel time costs). Three variations of the base model, i.e., the object model, the block model, and the line model, are used to investigate the trade-offs between increasingly simplified models and decreasing ability to determine the optimal intervention program due to decreasing ability to accurately estimate costs and benefits. In the object model, objects are considered in isolation, i.e., while each object fails and is restored all other objects in the line are fully functional. In the block model, blocks are observed in isolation, i.e., while each block fails and is restored all objects in the line are fully functional, whereas the multiple objects within a block may fail and be restored simultaneously. In the line model, all objects in the line may fail and be restored simultaneously. Fault tree analysis is used in the block and line models to estimate the costs of the combined failures of multiple objects. The three models are demonstrated by using them to determine the optimal intervention program for a fictive railway line between two stations that consisted of eight track sections, a bridge, two switches, and two signals. The intervention programs determined using the three models in terms of the interventions included and the net benefit obtained are compared. It is shown that all three variations produce useful results but that there are significant differences in the estimation of the net benefits using the three different models, and that these differences lead to different interventions being included in the determined intervention programs, and consequently in the net benefit that will be achieved through their implementation. It was also shown that each improvement in the estimation of accuracy comes with an increase in modeling complexity.

When airlines and air traffic controllers in the United States decide how to divert flights en route to an airport that is experiencing a complete or partial outage, their decisions determine the resiliency of the aviation network: the speed at which passengers, crew, and the aircraft are recovered. In the following study, the relationship between the choice of diversion airport and the characteristics of an intended destination airport and the individual flights are investigated. Using a binomial logit formulation and data for every diverted domestic flight between 2010 and 2016, it is found that flights are more likely to divert to small airports—those that do not act as airline hubs and carry relatively few passengers and flights—when the intended destination airport of the flight is experiencing a complete or partial outage; the effect is even stronger when the airport experiencing this complete/partial outage is a hub of the airline operating the flight to be diverted. The findings indicate that, when faced with a full or partial airport outage, airlines favor a strategy of diverting flights nearby their intended destination, particularly if the airport experiencing the completely/partial outage is a hub, and waiting until the outage clears, instead of diverting to a distant airport with many flights that would allow passengers to be reaccommodated. While this strategy allows airlines the possibility of recovering their operations (with delay), it may not offer a resilient outcome when airlines are recovering from climate events, which may be long in duration and far-reaching such that proximate diversion airports are affected. As climate events threaten to become more frequent and intense at coastal airports, and the frequency and intensity of events may be more uncertain, airlines should modify their diversion plans and preferences to focus on passenger reaccommodation.

With research studies continuing to provide strong evidence that climatic conditions significantly impair the physical condition and longevity of civil engineering infrastructure, the importance of reliable climate data cannot be overemphasized. Climate considerations play a critical role in infrastructure deterioration prediction, attribution of infrastructure repair responsibilities across the load and nonload factors of deterioration (for cost allocation purposes), and numerous other agency functions involving infrastructure planning, design, operations, monitoring, and repair. Often, for such applications, the secondary indicators of climate, primarily the freeze index and freeze-thaw cycles, are of particular interest. Unfortunately, at the current time, data on the secondary climate attributes stored in existing infrastructure-related climate databases are either outdated or exist in the form of highly aggregate qualitative characterizations such as climate zones or regions. The impact of these limitations becomes even more salient when viewed against the backdrop of incipient long-term climate change and the variability of climate conditions within each climate zone. To address these limitations, this paper uses data from the contiguous United States to first examine the temporal changes in primary climate attributes using the climate normals from two overlapping time windows. ANOVA and Tukey multiple comparison procedures are applied to investigate whether there have been statistically significant changes in climate attribute levels across the two time windows and magnitude of any such changes. The results show that over the study period under consideration, states in the Northern and Rocky Mountain regions particularly experienced significant changes in their secondary climate attributes. Furthermore, the paper provides updated values for the two secondary climate attributes that are of particular interest to infrastructure engineers. To determine these attributes, interpolation methods in a geographic information system (GIS) are used to develop isarithmic maps of the climate attributes to facilitate their interpretation and application. In summary, this paper provides an improved and updated characterization of climate conditions in the contiguous United States and makes location-specific secondary climate attributes available to infrastructure engineers and managers for use in their various agency functions.

Bridges, as all objects in road networks, are built to provide a specified level of service over a specified time period. This level of service ensures that acceptable levels of health, safety, and prosperity of society are guaranteed. The level of service, required from the transportation infrastructure, changes over time, as does the ability of infrastructure to provide it. The extent of maintenance is influenced by gradual deterioration, such as that caused by chloride-induced corrosion of concrete, and sudden deterioration such as that caused by the occurrence of scour resulting from extreme floods. To ensure that infrastructure provides the required service levels, its performance needs to be monitored. Determining how monitoring is to be done is a trade-off between accuracy and cost. Ideally, one will have access to accurate but inexpensive monitoring techniques. This paper contains the results of an investigation into the use of an unmanned aerial vehicle and modern photogrammetric technology to obtain topographical information to apply in bridge risk assessment. The unmanned aerial vehicle was used to take georeferenced images. With the images and photogrammetric technology, a three-dimensional (3D) mesh of the terrain was generated. This mesh was then converted to a computational mesh, which could be used to run computational fluid dynamic simulations during a bridge risk assessment. The investigated bridge was a single span concrete bridge in the Canton of Grisons, Switzerland. The hydraulic events, predicted by the developed model, correspond with historical observations, indicating that the topographical information collected is sufficiently accurate to be used to simulate complex flow situations, which can be used in bridge risk assessments.

The importance of transnational public–private partnership (TPPP) has accelerated over the last decade, and the critical risk factors (CRFs) of TPPP have gained increasing attention from both researchers and practitioners. However, a systematic review of the extant literature on TPPP CRFs is unavailable. This study addresses this gap by conducting a systematic review of 37 selected papers on TPPP published in peer-reviewed journals from 1991 to 2015. The papers are reviewed to study the trend in the annual publication on TPPP CRFs, contributions of various countries and researchers to TPPP CRF-related studies, the most frequently identified TPPP CRFs, and research methods adopted in TPPP CRF–related studies. The results of this study indicate an increasing trend in research publications on TPPP CRFs, and the most frequently identified TPPP CRFs are found to be legal risk, cooperation risk, tariff risk, financing risk, and political risk. The research methodologies adopted are case study, qualitative discussion, survey, and hybrid methods. This study provides a useful reference for researchers and practitioners to appreciate the research trend and development of TPPP CRFs and to enhance their understanding of the CRFs for TPPP projects for risk management activities. The developed checklist of TPPP CRFs also makes this paper useful for researchers to conduct further empirical studies.

Given the often-limited availability of real electricity network data, this paper presents methodology for the synthesis of multilevel electricity networks for use in applied network failure and risk analysis. The proposed algorithm is capable of producing networks that preserve a number of important spatial and topological properties of real-world networks including the multilevel structure of subsystems, the geographic distribution of network nodes, the node degree distribution, and the networks spatial connectivity. The algorithm is capable of integrating both synthetic and real data from a range of sources to produce spatially and topologically continuous representations. The flexibility of the algorithm is demonstrated through the synthesis of a regional-scale electricity network. The practicality of the algorithm, in terms of providing new data to conduct applied risk and resilience studies of interdependent infrastructures, is demonstrated through the synthesis of a unique representation of the national integrated electricity network for England and Wales, bridging the transmission, subtransmission, and distribution scales and consisting of more than 160,000 nodes.

Roofs are important components of buildings and can be designed and/or retrofitted with photovoltaic (PV) and green-roof (GR) systems to produce energy and to improve stormwater management. Traditionally, GRs and PVs have been viewed as direct competitors vying for the same roof space. However, with correct design, synergy effects arise when combining both technologies (GR-PV). In this paper, a probabilistic analysis is performed to examine the potential energy and carbon emissions of GR, PV, and GR-PV systems. The analysis demonstrates that a GR-PV system is a low-risk investment generating lower energy and carbon-emission payback time in comparison with separate GR and PV systems. Furthermore, the average of net energy of this technology is 7.3 and 1.3 times higher than separate GR and PV systems, respectively. In addition, the installation of GR-PV systems throughout the City of Toronto could supply 16% of the electricity by PVs and reduce 12% of energy demand (i.e., heating and cooling) by GRs. However, extensive modification of the electrical grid would be needed for efficient collection and transmission of PV-generated power.

United States federal regulations require increasing renewable fuel blending in the transportation sector, a majority of which is corn ethanol. Nationally, ethanol is blended with gasoline up to 10% (E10) for use in conventional vehicles, and up to 85% (E85) for use in flexible fuel vehicles (FFVs). Meeting the blending requirements could mean increasing the ethanol blended with gasoline or E85 use in FFVs. The authors estimate costs typically not quantified for FFV drivers refueling with E85, which are a small component of total costs, and consider the infrastructure costs to expand E85 access in Pennsylvania. Even with a retailer incentive of $0.01 to $\$0.39/\mathrm{gasoline}$ liter equivalent (gle) to encourage ethanol infrastructure installation, an E85 consumer would still also experience higher refueling costs. A E85 consumer refueling and convenience cost of $\$0.95/\mathrm{gle}$ is higher than historical ethanol subsidies. Additionally, although switching from E10 to E85 could reduce emissions, a refueling incentive of $\$\mathrm{1,320}/\mathrm{metric}$ ton ${\mathrm{CO}}_2$ is 36 times larger than the average U.S. social cost of carbon (${\mathrm{CO}}_2$) for 2015.

Modern infrastructure systems form complex networks that are organized hierarchically in communities of tightly integrated elements. This paper presents three new community-based metrics to identify the critical elements of a network system. Two of these metrics assess intracommunity and intercommunity behavior for any community structure, and the third metric accounts for the multiple levels of community structure. First, these metrics are studied to establish their characteristics with different community structures, and then the Great Britain Railway Network is used as a case study to demonstrate the usefulness of these new metrics. The results show that an assessment of the system using these metrics leads to the identification of not only those elements that are critical at the global level, but also those that greatly affect the local performance of the communities. Such identification of the critical components at the community and global levels would enable a better understanding of system behavior by stakeholders with competing demands.

Public-private partnerships (P3) are increasingly being pursued for infrastructure procurement because of their ability to alleviate pressure on government budgets, promote innovation, and implement new technologies. These types of contracts tend to be long term and often need to account for future yet-to-be-seen variables that potentially impact the feasibility of this procurement method. This is especially true when the P3 exists within a portfolio of competing assets across transportation infrastructure systems. The current research presents a system dynamics model that is used to analyze the complexity of a transportation infrastructure asset procured through a P3 within such a portfolio. An illustrative case demonstrates how discrete and continuous events potentially impact the successful procurement of infrastructure within a portfolio of competing assets comprising a regional transportation system. This paper contributes to the existing body of knowledge by demonstrating how a system dynamics model can simulate the real-world causal relationships that impact the procurement of infrastructure through P3s. The system dynamics model is used for the valuation of real options in order to promote public initiatives, encourage private participation and enhance the economic sustainability of P3s.

A road network consists of multiple objects that deteriorate over time with different speeds of deterioration. In order to provide an adequate level of service over time, these objects will eventually require interventions. As road managers are trying, in general, to maximize the benefit obtained from the road network, it is in their interest to determine intervention programs, which consist of the grouping of interventions in work zones. The determination of optimal intervention programs is relatively complicated when considering single objects, but it becomes even more so when considering multiple objects embedded within a network. The objects to be included in the work zones at each time interval depend on many factors, such as the interventions to be executed on the objects, the maximum allowable length of the work zones, the traffic configurations to be used in the work zones and the available financial resources. Although some initial research in this area has been conducted, none has determined the optimal set of work zones on large infrastructure networks in a geographical information system (GIS) framework, which is necessary in the world of modern infrastructure management. In the work presented in this paper, a GIS-based program was developed to determine optimal intervention programs for large infrastructure networks using a linear optimization model, which can be linked directly to a GIS. The model includes constraints on the amount of available resources, on the length of the work zone, and on the distance between two work zones. A constraint-constructing algorithm is used in order to set up the latter two constraints. The program is illustrated by determining the optimal set of work zones for an example road network similar to the one in the canton of Wallis, Switzerland, including more than 2,000 bridges, tunnels, and road sections.

The increase in maximum air temperature and annual rainfall caused by climate change can shorten the lives of pavements and hence increase the mileage of roads that would require rehabilitation at any point in the future. Most agencies are aware of the potential of the use of better materials such as high-modulus asphalt in reducing the negative impacts, thereby making roads resilient to climate change. However, using better materials does involve higher investments; currently, the major hindrance is the inability to decide on a justifiable level of investment. This is a challenge because climate change predictions have major uncertainties associated with them, and there could be competing needs for the limited budget that is available. This paper presents a rational framework that is based on the use of system dynamics and Monte Carlo simulations to identify an appropriate level of investment for making roads resilient to climate change. The process consists of predicting the mileage of roads that will need rehabilitation because of climate change with and without the use of better materials, considering the reduction in this mileage caused by using better materials as a future “return” on a present investment on better materials, and then comparing that return with one that could be expected from a regular investment with a reasonable interest rate. The comparisons are made probabilistically, so that depending on the level of selected confidence level, one can identify the investment that would result in the same return in a certain number of years as the return from resilient pavements in the same number of years. This investment would then be used as an appropriate and justifiable number for investing in better materials for making roads resilient to climate change. A worked-out example is presented, which shows an appropriate investment (that is far greater than one would spend for a conventional pavement) of approximately $2,400 per 1.6 km (1 mi) of a roadway network to obtain the same benefit from the reduction of cost of rehabilitation as an amount that could be obtained from an interest-paying investment in 15 years. The framework allows users to utilize their preferred method of pavement analysis, costs, mileage of roads, and results of laboratory and/or field study on pavement life–enhancing ability of better materials.

In large infrastructure systems, the technical and operational requirements that are necessary to face future unexpected events cannot be defined up front; on the contrary, they evolve as a result of internal or external events (e.g., maintenance, failures, and updating) that occur through time. Thus, the success of any project depends on the combination of both technical and mechanical considerations and operational decisions. The objective of this paper is to present a model that approaches engineering design strategically by combining the need to manage a fix demand with the possibility of accommodating unexpected events. Because this approach is consistent with flexible and adaptable design, instead of designing and building a system capable of withstanding the demand to which it will be exposed during its time mission, the system is systematically redesigned at one or several points in time along its lifetime. This approach leads to more cost-efficient designs and equips the system with the flexibility necessary to handle uncertainty. The theoretical formulation evaluates the lifecycle cost and is illustrated with some examples.

In some countries, it is common for two roads with the same origin and destination to compete in the same corridor. Usually one of the roads is a toll highway of better quality, whereas the parallel single road is toll-free. The users have to decide whether it is worth paying for the advantages offered by the toll road. This problem, known as the untolled alternative, has been studied extensively. Particular attention has been paid to calculating the optimal welfare toll that maximizes the economic efficiency. However, there is a gap in the literature regarding how the income distribution affects the optimal toll. The main objective of this paper is to add knowledge in that area by analyzing the influence of the distribution of the values of travel time (VTT) of the users, which is closely related to their income distribution, on the optimal toll price. To solve this problem, the authors defined a mathematical model aimed at obtaining the optimal welfare price for this kind of corridor under the hypothesis that the drivers assume that the average flow conditions will prevail. Under the assumptions made in the paper and for the type of network studied, the results showed that the higher was the average VTT, the higher was the optimal price; and the higher was the dispersion (variance) of this VTT, the lower was the optimal price.

Interdependent networks are a collection of individual networks that contain nodes that have interdependent relationships with nodes of other networks. Recognizing that many real-life networks are interdependent, approaches that were previously developed to study vulnerability in single networks are being extended to interdependent network applications (e.g., intentional attack of selected nodes). As such, this study proposes an approach for finding the best set of protection strategies for a given set of interdependent networks exposed to random or intentional attacks through the development of a multiobjective optimization formulation. A simple diffusion model [susceptible-infected-recovered (SIR)], based on cellular automata is used to describe the dynamics of the spread of the disruptive event through each individual network, and Monte Carlo simulation enables simulating the stochastic evolution of the disruption between the nodes of the interdependent networks. Several criteria considering the results of the propagation dynamics in each network or in the whole set of interdependent networks are proposed to assess the security characteristics of the network, and Pareto-optimal solutions are derived using the multiobjective evolutionary Nondominated Sorting Genetic Algorithm (NSGA-II). An example using the topology of an Italian electric power network and the set of communities previously defined illustrates the approach.

Decision making on the reinstatement of the Christchurch sewer system after the Canterbury (New Zealand) earthquake sequence in 2010–2011 relied strongly on damage data, in particular closed circuit television (CCTV). This paper documents that process and considers how data can influence decision making. Data are analyzed on 33,000 pipes and 13,000 repairs and renewals. The primary findings are that (1) there should be a threshold of damage per pipe set to make efficient use of CCTV; (2) for those who are estimating potential damage, care must be taken in direct use of repair data without an understanding of the actual damage modes; and (3) a strong correlation was found between the ratio of faults to repairs per pipe and the estimated peak ground velocity. Taken together, the results provide evidence of the extra benefit that damage data can provide over repair data for wastewater networks and may help guide others in the development of appropriate strategies for data collection and wastewater pipe decisions after disasters.