Journal of Infrastructure Systems

Unexpected foreign exchange (FX) rate changes represent an important risk factor, especially in public-private partnership (PPP) infrastructure projects in developing countries. The risk exists because PPP projects typically sell their outputs domestically and generate revenues in local currency, whereas their financing costs and operating and maintenance costs are denominated in major currencies. Multidisciplinary experience and engineering judgment are needed to control and manage FX exposure during construction, operation, and maintenance of infrastructure. In this context the paper aims to establish a quantitative model that is linked to engineering parameters and cost assumptions to quantify economic FX exposure in PPP infrastructure projects. First, the FX index terminology will be introduced, based on a first-order second-moment reliability method. Second, the methodology is illustrated on a PPP coal-fired power project in Southeast Asia. It is also shown that the proposed dispersion ellipsoid implementation is much faster in computation time compared with commonly used Monte Carlo simulations in PPP infrastructure projects.

This paper presents a computational model for establishing the interdependence among municipal assets. To create a multiyear plan for the maintenance of municipal asset networks, it is necessary to know the interdependencies among these assets. With this knowledge, it is then possible to avoid costly duplication of work and disruption of services to the public. The model offers a methodology to systematically group assets based on their location and geometry to support what is known as corridor rehabilitation of municipal assets. The paper contains an overview of the concepts of infrastructure asset interdependency and introduces a method that computationally generates a complete list of asset-to-asset relationships and quantifies the amount of physical overlap between them. The developed model is implemented in a geographic information systems environment to facilitate further use of operation and maintenance data that is commonly saved by a number of municipalities in that environment. A case study is presented to demonstrate the use of the proposed model. The study utilizes a small section of the road and water main distribution network in the district of Pointe-de Sainte-Foy, Quebec, Canada. The benefits of the developed model are discussed along with potential applications to support integrated municipal infrastructure management.

The bridge network, as part of the critical civil infrastructure, is susceptible to natural and man-made hazards. It is essential that the network retains its traffic-carrying capacity after a disastrous earthquake to ensure efficient evacuation of at-risk population to safe zones and timely dispatch of emergency response resources to the impacted area. Because of limited resources, it is important to prioritize bridge retrofit projects and manage disaster mitigation resources under a strategic budget plan. This paper proposes a methodology to find the optimal bridge retrofit program that aims to maximize the postdisaster network evacuation capacity. The uncertainties of earthquake intensity, bridge structural damage, and bridge traffic-carrying capacities are addressed by using a Monte Carlo simulation framework with established bridge fragility curves and damage-functionality relationships, and the effectiveness of preserving evacuation capacity is calculated on the basis of a network design model. The proposed methodological framework is demonstrated with the transportation network in Memphis, Tennessee, and numerical experiments show that the proposed framework solves the problem efficiently. The modeling framework can help transportation agencies maximize the effectiveness of investment. Emergency managers can also use the model to enhance preparedness and emergency response efficiency, which in turn improves the infrastructure systems’ resilience against extreme events.

This paper describes a synthesis of findings pertaining to rehabilitation of concrete T-beam bridges with externally bonded fiber-reinforced polymer (FRP) composites from a Pennsylvania Department of Transportation District 3 (PennDOT D3) project, with the purpose of answering common questions of concern mainly by state DOT engineers and officials. A method for selecting applicable candidate bridges for suitability of repair with externally bonded FRP composites is described. Three levels of repair are identified, Level 1 (contract), Level 2 (contract/department force), and Level 3 (department force). From this classification, a candidate bridge was selected for a contract repair project. Field and laboratory testing of existing bridge materials is described. Prerepair tests included ultrasonic pulse velocity and rebound hammer on beam concrete, compressive strength tests on deck concrete cores, carbonation tests for both beam and deck concrete, scanning electron microscope–energy dispersive X-ray spectroscopy (SEM-EDX) analyses for beam and deck concrete, and tension tests of the extracted reinforcing steel. Structural analysis was on the basis of AASHTO specifications. Finite-element (FE) modeling was performed to determine existing capacity, and the FE model was calibrated by testing of the bridge by using applied truck-loads. The FRP design was based on strengthening the bridge to sustain an HS-20 AASHTO truck loading. The FRP-repair system was designed on the basis of current American Concrete Institute design guidelines. Repair work and post construction load testing were completed. Supporting full-scale lab studies were conducted to evaluate the most effective concrete substrate repair method and FRP strengthening scheme for laboratory damaged concrete beams by accelerated corrosion, to assure better long-term performance under static and fatigue loads. Results from the rehabilitated bridge and supporting testing were used to develop draft PennDOT design standards and construction specifications and to apply lessons learned to the design and constructability of nearly 1,000 concrete T-beam bridges in Pennsylvania.

This paper presents reliability-based analyses for the calibration of resistance factor for axially loaded drilled shafts. A total of 16 cases of drilled-shaft load tests were collected from the Louisiana Department of Transportation and Development (LADOTD) archives. Only 11 out of the 16 cases met the Federal Highway Administration (FHwA) failure criterion. Because of the limited number of available drilled-shaft cases in Louisiana, additional 15 drilled-shaft cases were collected from a neighboring state, Mississippi, which has subsurface soil conditions similar to Louisiana soils. A database of 26 drilled shafts representing the typical design practice in Louisiana was created for a statistical reliability analysis. Predictions of load-settlement behavior of drilled shafts from soil borings were determined using the FHwA design method through the SHAFT computer program. Measured drilled-shaft axial nominal resistance was determined from either the Osterberg cell (O-cell) test or the conventional top-down static load test. Statistical analyses were performed to compare the predicted ultimate drilled-shaft nominal axial resistance and the measured nominal resistance. Results show that the selected design method underestimates the measured drilled-shaft resistance by an average of 17%. The Monte Carlo-simulation method was selected to perform the LRFD calibration under strength I limit state. Total resistance factors for different reliability indexes ($\beta$) were determined and compared with those available in literature. The LRFD calibration showed that the FHwA design method has a resistance factor ($\varphi$) of 0.60 at a target reliability index (${\beta }_T$) of 3.0.

Environmental or air-quality effects of Intelligent Transportation Systems (ITS) are very difficult to measure. Some researchers have attempted to quantify the effects of individual ITS application on emissions; yet, the effects of ITS as a whole on ambient air-quality have not been investigated. This paper shows how to model the relationship between ITS and ambient airquality. The multiple artificial neural networks (ANN) training with the data yielded a model for predicting the ${\mathrm{NO}}_2$ concentrations. In addition, the ANN made the measurement of the effect of ITS on ${\mathrm{NO}}_2$ concentrations in ambient air possible. Data pertaining to 59 U.S. cities (urbanized area) were used for this work. Input variables used were related to transportation, local characteristics, and ITS applications. Output variable was the annual average concentration of ${\mathrm{NO}}_2$ in ambient air. The $K$-fold cross-validation technique was used to train the ANN. There was an unusual finding: in contrast to the common assumptions, ${\mathrm{NO}}_2$ concentration increased with ITS intensity and that may be suggestive of causing conformity problems and may jeopardize the ITS project and the transportation program.

Commercial construction operations are heavy users of nonroad equipment and diesel engines. These engines are large contributors of carbon dioxide (${\mathrm{CO}}_2$) and other engine-based pollutants and consume large quantities of diesel fuel. This paper presents a case study to identify construction activities that cause the most emissions and fuel use on a construction project and to determine when and where these occur. The paper links emissions and fuel use of construction activities with a project schedule to establish a temporal relationship. RSMeans was used to obtain equipment productivity. Equipment emission rates and fuel use rates were obtained from the EPA’s NONROAD Model. A sensitivity analysis was conducted to investigate the effect of changes in scheduling, equipment size, and EPA engine tier on emissions and fuel use. Site construction activities proved to be the heaviest polluters and caused a significant spike in emissions early in the project. Further study is recommended to validate the trends found and to determine the broader effects of this study. However, the methodology presented can be used by construction professionals to estimate fuel use and emissions for various infrastructure building projects.

Plug-in electric vehicles (PEVs) are becoming increasingly popular. In the next several years, all major vehicle manufacturers will introduce PEVs, either as battery electric vehicles (BEVs), such as the Nissan Leaf, or plug-in hybrid electric vehicles (PHEVs) with a dual powertrain, such as the General Motors Volt and the Toyota Prius. This new generation of vehicles relies on electricity as a power source and a battery as an energy storage medium. Naturally, for PEVs, refueling occurs when the battery connects to an electrical outlet linked to a distribution network. The primary objective of this paper is to study optimal locations for building facilities (i.e., smart garages) that will maximize integrated benefits from the transportation system (i.e., parking fee) and the electric power system [i.e., revenue from vehicle-to-building (V2B) services]. A deterministic smart garage development problem (SGDP) is formulated and modeled as a bilevel program and solved by using a genetic algorithm. The results of a simple numerical example show the sensitivity of the model results with respect to parameters, including PEV penetration rate and battery capacity.

This paper presents an energy efficiency metric for the raw water extraction process at urban water systems. Raw water pumping is the second largest energy consumer at water treatment plants after finished water pumping. United States water utilities are using more benchmarking metrics to assess performance and to compare their energy efficiency with other utilities. This trend has become especially prevalent among water and wastewater utilities. This paper proposes a thermodynamic score to provide complementary information of a utility’s energy efficiency. The thermodynamic score arises from estimating the minimum energy required by the system in contrast to the benchmarking method in which the basis is an empirical approach. The thermodynamic score proved to be an effective additional tool for measuring energy efficiency in most cases may have significant limitations in others. However, the energy analysis developed for the thermodynamic score has further applications on proactive asset management of water utilities.

Wastewater utilities are aiming to implement asset management strategies to minimize costly emergency repairs, to justify expenditures, and to optimize future renewal actions. Consequently, development of deterioration models that explain the behavior of wastewater lines and provide predictions regarding potential future condition levels is gaining importance. In this paper, deterioration models are generated to estimate the probability of failure values for sewer sections. A set of variables was obtained by examining the inventory and inspection databases of a sewer network. Three statistical methods (ordinal regression, multinomial logistic regression, and binary logistic regression) were employed in successive steps. Proportionality of odds assumption was tested for ordinal regression models, and suitability of this particular method was discussed. Estimated condition ratings were compared with observed data, and the binary logistic regression model was found to be more suitable for predicting probability of failure than the multinomial logistic regression model. The models presented in this paper are expected to assist wastewater utilities in developing section-level risk assessment models to identify pipe sections that require immediate attention and close monitoring.

The Afghanistan National Development Strategy identified billions of dollars of needs for transportation, water, energy, telecommunications, and other necessary infrastructure development for the rebuilding of Afghanistan. With economic sustainability as a primary aim, the coordination and prioritization of investments has been a challenge in part because of Afghanistan’s volatile security situation along with the intricacies of the negotiating and coordinating efforts of numerous stakeholders. An understanding of the contributions of infrastructure systems and associated projects to the national development strategy is needed. This paper formulates a scenario-informed multicriteria approach to prioritize major project investments for infrastructure development subject to deep, nonprobabilistic uncertainties. The methods are inclusive of stakeholder values and accounts for deep uncertainties in governance, security, economy, environment, workforce, and other topics. The methods are applied in Afghanistan’s Nangarhar province to assist in the selection among twenty-seven candidate infrastructure projects that are vulnerable to potential refugee immigration among other emergent conditions. The paper describes the relationships of selected projects to strategic goals while facilitating collaboration among government and nongovernment investors, donors, technologists, and other stakeholders.