Journal of Infrastructure Systems

Electrified vehicles (EVs) couple transportation and electrical infrastructures, impacting vehicle sustainability, transportation resiliency, and electrical grid stability. These impacts occur across timescales; grid stability at the millisecond scale, resiliency at the daily scale, and sustainability over years and decades. Integrated models of these systems must share data to explore timescale dependencies, and reveal unanticipated outcomes. This paper examines EV adoption for sustainability, resiliency, and stability effects. Sustainability findings, consistent with previous studies, indicate that electrification generally reduces lifecycle greenhouse gas (GHG) emissions, and increases ${\mathrm{SO}}_x$ and ${\mathrm{NO}}_x$. Electrified vehicles enhance vehicle resiliency (ability of vehicle to complete typical trips during fuel outage). Coupled results enhance EV resilience research, finding that a 16-km (10-mi) all-electric range plug-in hybrid EV improves resiliency $\sim 50\%$ versus a gasoline-only vehicle. Increasing EV market share reduces grid stability. Stability depends upon charging profiles and background electrical demand. Stability-related grid outages increase with EV market penetration. This paper modeled these systems in their coupled form across timescales yielding results not obvious if the systems were modeled in isolation.

Transportation agencies use pavement management systems (PMS) for their maintenance and rehabilitation planning, programming, and budgeting. PMS is used to make decisions regarding when maintenance and rehabilitation should be applied. To support these decisions, it is important to have reliable data on pavement conditions and accurate performance models for predicting pavement condition. The data on pavement condition typically come from regular field surveys resulting in distress, condition, and ride scores. PMS data sets are often incomplete (for some locations and some years) as a result of operational limitations reducing the predictive power of the performance models. Model-free and model-based replacement techniques for estimating missing data points have been designed and successfully used in other application areas like statistics, economics, marketing, medicine, psychometrics, and political science. It is therefore reasonable to apply these methods to the PMS databases. Statistical techniques are assembled and used in a robust approach to systematically analyze the effect of applying these techniques to rebuild missing performance data. As a case study, continuous reinforced concrete pavement (CRCP) sections were selected to test the proposed statistical systematic approach from a pavement management information system (PMIS) maintained by the Texas Department of Transportation (TxDOT). A major effect was observed in the results of predicting the distress scores when applying the developed approach.

Preventive maintenance of drainage networks is an essential component of urban infrastructure management. Large cities require significant budgetary and other resources to perform the necessary prescheduled cleaning and flushing activities at various locations around the city at regular intervals. However, planning and scheduling of these activities can be challenging because of the wide variation of actual on-site flushing duration, which depends on a number of factors such as location, properties of the pipes, frequency of flushing, time of day, and season. This study develops a model for estimating the on-site duration of high pressure flushing (HPF), based on such predictor variables. The model is developed and validated using historical data from the City of Edmonton, where 5,500 km of network is maintained through more than 1,400 prescheduled preventive maintenance locations for HPF. The panel data set utilized in this study is obtained by integrating several databases, one of which is the historical data collected by the global positioning system (GPS) device installed in the flushing trucks. The framework presented here first uses ordered probit analysis to estimate the probability of a number of stops to flush a given set of pipes and then forecasts the flushing duration by means of a multiple regression model. This approach is applicable for similar municipalities and can be effectively used for resource optimization, maintenance scheduling, sensitivity analysis, and performance evaluation.

For purposes that include oversight and accountability, the U.S. Department of Transportation and the General Accountability Office continue to emphasize the need for regular systemwide monitoring of transportation infrastructure performance in response to system expenditures. To address this issue, it is necessary to ascertain the nature of the relationship between the funds expended on a jurisdiction’s infrastructure system preservation and the resulting performance. Duly recognizing past research efforts in this direction, this paper investigates such a relationship specifically for interstate highway pavements. This is an aggregate analysis and therefore does not consider site-specific design variables. Acknowledging that there exist jurisdiction-specific variables besides spending amounts that affect interstate pavement performance, this paper removes some of this bias by incorporating the system size as a normalizing variable and by considering state-specific values of the key deterioration variables, including the average traffic load intensity and climatic severity. Using data spanning 2000–2008 and lagged panel model specifications, this paper quantifies the expenditure–performance connection and uses the resulting model parameters to develop, for interstate pavements in the various states, an overall index of performance that considers normalized values of the pavement condition, preservation expenditure, climatic severity, and traffic load. The paper offers plausible explanations of the observed differences in the resulting overall performance across the states. Ultimately, the framework and results of this paper can help oversight agencies in their bid to foster overall accountability for taxpayer dollars expended on their infrastructure.

Civil infrastructure systems such as power, water, and communications are critical for the well-being and functioning of citizens, industries, and organizations both under normal and disaster conditions. Infrastructure systems cannot be seen as independent entities because they rely a great deal on each other for services needed for operation. Damage to one system can have cascading effects throughout that system, and throughout all of the systems that rely on it. To obtain a meaningful prediction of disruption, these interdependencies among the systems must be considered. This paper presents two models to predict and analyze the effect that a hurricane can have on the performance of interdependent infrastructure systems. The first model uses a Monte Carlo simulation and statistical methods to predict the damage caused by a hurricane on the systems being considered. The second model applies optimization techniques to determine the cascading effects that the damage has throughout the infrastructure systems considered. A case study was conducted using an artificial interdependent infrastructure data set called “Clarc” County. Examples from this case study show that there is a benefit in considering interdependencies among infrastructure systems when predicting disruptions. In addition, the effect of these interdependencies can be assessed in time to support decision making for restoration of services either during an event or as a training exercise.

The Impulse Response (IR) method is a nondestructive method that has been successfully used to detect voids or loss of support underneath rigid pavement and to evaluate the pavement subgrade. Voids or loss of support can be detected due to changes in the pavement’s mobility spectra, while the subgrade is evaluated based on the static stiffness. The fundamental assumption of the IR method is that the response of rigid pavements can be represented by a single degree of freedom (SDOF) system, with the controlling stiffness being that of the subgrade. Although simple to analyze, this oversimplification of a complex dynamic problem introduces deficiencies that are primarily attributed to a deviation of the shape of the impedance function for rigid pavement systems from that of an SDOF system for frequencies beyond the fundamental frequency. Other dynamic models [i.e., cone and lumped parameter (LP)] are investigated as alternatives to the SDOF model and are implemented in the reduction of field data collected with the Seismic Pavement Analyzer (SPA). These alternative models with additional degrees of freedom provide a significantly better representation of the effect of individual pavement layers and the subgrade on the overall pavement response, and a better fit to the pavement dynamic response curve. In general, the SDOF model results in a higher modulus compared to the cone and lumped parameter models. However, no consistent trend was established between the moduli from the different models, especially when there is a loss of pavement support or void underneath the pavement. Part of the field data used in the analysis came from a grouting program to repair a rigid pavement with voids underneath the slabs. The IR method was successfully used to evaluate support conditions before and after grouting and to determine the effectiveness of the program.

Many types of structural stormwater best management practice (BMP) have been employed since the late 1990s in the hope of mitigating the impervious surface hydrology (indicated by, e.g., high runoff volume and peak flow rates, sewer and ditch runoff conveyance, and short time to peak flow) and deteriorating water quality due to land development and human activities. It is time to examine the status of the BMP deployment from an infrastructure perspective and reflect the best strategies for low-impact development (LID). This paper presents a 22-month field investigation for 279 structural BMPs and 227 major outfalls (defined as storm drain outfall pipes with an equivalent diameter of 91 cm or larger) along highways in Prince George’s County, Maryland, an area that has LID BMP research and employment history for decades. This study offers a snapshot about the state and effectiveness of structural BMPs in mitigating highway runoff in this region. The results indicate that the majority of structural BMPs deployed along the regional highway system are infiltration trenches and stormwater ponds, both of which need extensive maintenance. Due to safety concerns for traveling vehicles, fast runoff conveyance and end-of-pipe treatment are sometimes required in highway systems, prompting challenges for the LID designs. Stormwater discharged from 51% of the major outfalls is not treated, indicating strong demands for stormwater green infrastructure expansion and retrofit. This paper identifies general problems and maintenance needs of different highway structural BMP designs as well as providing recommendations for improvement to engineers and highway administrators.

Governments throughout the world have been actively engaging in public–private partnership (PPP) arrangements to upgrade and develop their road networks. The transportation sector and particularly the road sector has remarkably increased its use of PPP arrangements for building, upgrading, operating, and financing these investments due to a shortage of public spending, which is necessary to satisfy infrastructure needs of growing economies. Fast-growing economies, such as Brazil, are facing bottlenecks in their economic development due to a lack of proper transportation infrastructures. This paper discusses the use of PPP contracts in the road sector, focusing the analysis on the Brazilian case study MG-050. This road was the first PPP project in the road sector in Minas Gerais (Brazil) during the recent so-called PPP wave and can provide valuable lessons for future development of road projects, not only in Brazil but also in other fast-growing economies, such as sub-Saharan Africa, the Middle East, or Southeast Asia countries.

The Missouri River flooding in 2011 caused significant damage to secondary roadway infrastructure systems in western Iowa. This paper documents results of an investigation designed to evaluate unpaved roads and bridge approaches in the flood-affected areas. In situ testing was conducted as flood waters receded and several months later to evaluate performance and recovery conditions. In situ tests included falling weight deflectometer, dynamic cone penetrometer, and ground penetrating radar scanning. Field results indicated statistically significant differences over time in roadway support characteristics between flooded and nonflooded areas. Support characteristics in some flooded areas did not recover over the duration of the research reported in this paper (7–8 months after flooding). Multivariate statistical analysis indicated that the dynamic composite stiffness of the flooded unpaved roads was highly dependent on the stiffness of the underlying subgrade layer. Voids were detected in culvert and bridge abutment backfill materials shortly after flooding, and several months after flooding in spite of reconstruction. The results showed that damage from flooding can cause extended problems after the event, and that secondary infrastructure systems are typically not designed to withstand flood events.

Adaptation of existing infrastructure is a response to climate change that can ensure a viable, safe, and robust transportation network. However, deep uncertainties associated with climate change pose significant challenges to adaptation planning. Specifically, current transportation planning methods are ill-equipped to address deep uncertainties, as they rely on designing responses to a few predicted futures, none of which will occur exactly as envisioned. In this paper, we propose using dynamic adaptive planning (DAP), an emerging general strategic planning method, to account for deep uncertainties by building flexibility and learning mechanisms into plans that enable continuous adaptation throughout implementation. This paper first reviews uncertainty in general, introduces what is meant by deep uncertainty, and then introduces DAP. Then, DAP is applied to a case study of the Oakland approach to the San Francisco-Oakland Bay Bridge, which was initially assessed under the 2010–2011 FHWA Climate Change Vulnerability Assessment Pilot program, to illustrate how DAP could be applied as a response to climate change in the context of evolving transportation infrastructure adaptation planning practices in the United States. We conclude that DAP is well suited to account for the deep uncertainties of climate change in transportation and infrastructure planning, and provide suggestions for further research to better apply DAP in this field.