Journal of Infrastructure Systems

Road infrastructure has a remarkable economic and social impact on society. This is why road financing has always drawn the attention of policymakers, especially when resources available for government spending become scarce. Nations exhibit differing approaches to dealing with road transportation financing. In the United States, the current system of road funding has been called into question because some regard it as insufficient to meet the amounts now required for road expenditures. By contrast, in most European countries, road charges are very high, but these revenues are not allocated for the funding of roads. This paper analyzes the balance between charging for the use of and expenditure on the road sector in the United States and compares the American policy with those of several European countries (Germany, United Kingdom, France, Spain, and Switzerland). To that end, a methodology is defined to calculate the annual amount of fee charges levied on light and heavy vehicles in the selected countries in order to compare those charges with annual road expenditures. The results show that road charges in America are noticeably lower than those paid in Europe. Additionally, the research concludes that in Europe, road-generated revenues exceed road expenditures in all the countries studied, so road charges actually subsidize other policies. By contrast, in the United States, the public sector subsidizes the road system in order to maintain the current level of expenditure.

Urban drainage systems are prone to symptomatic decay that eventually causes surcharged flows and flooding, with important consequences for the aquatic ecosystems of receiving water bodies in addition to the safety of drinking water and recreational water activities. Rapid urban development and climate change combine with wear and tear along with a lack of network maintenance to accelerate this decay and cause a reduction in the hydraulic system’s capacity. In this context, the need for system rehabilitation becomes more pressing. Cost figures prominently take precedence in the decision-making surrounding the choice of rehabilitation method employed, but models for assessing cost-effectiveness which consider both structural and hydraulic options, in addition to real-world constraints and time-frame conditions, are lacking. This paper proposes an algorithm to maximize the benefits ensuing from the rehabilitation of urban drainage systems. Potential interventions considered in the algorithm include both traditional rehabilitation methods such as the resizing and rebuilding of damaged pipes, in addition to best management practices (BMPs) aimed at reducing runoff rate and volume. A case study from the borough of Verdun is presented, in which the algorithm identifies the best combination of volume retention, pipe rehabilitation, and resizing interventions to optimize the network’s hydraulic performance and minimize operational costs.

In the context of sequential decision making under uncertainty, the Markov decision process (MDP) is a widely used mathematical framework. The MDP-based approaches in the infrastructure management literature can be broadly categorized as either top-down or bottom-up. The former, while efficient in incorporating system-level budget constraints, provide randomized policies, which must be mapped to individual facilities using additional subroutines. Conversely, although state-of-the-art bottom-up approaches provide facility-specific decisions, the disjointed nature of their problem formulation does not account for budget constraints in the future years. In this paper, a simultaneous network-level optimization framework is proposed, which seeks to bridge the gap between the top-down and bottom-up MDP-based approaches in infrastructure management. The salient feature of the approach is that it provides facility-specific policies for the current year of decision making while utilizing the randomized policies to calculate the expected future costs. Finally, the proposed methodology is compared to a state-of-the-art bottom-up methodology using a parametric study involving varying network sizes.

This paper explores the impact of some key infrastructure measures in transportation, telecommunication, and electricity production sectors on labor productivity, using data on two-digit sectors for the Turkish economy for the years 1987–2006. We find both statistical and economic significance for the positive productivity impact of infrastructure on productivity growth, for road, port, and air transport, telecommunications, and electricity production. In the railway sector, only measures of actual freight carried are consistently and significantly associated with productivity growth, whereas other measures of infrastructure are insignificantly or inversely associated with productivity growth. Given that the railway-transport sector is the only infrastructure sector that remains closed to competition and private participation, this raises the issue of the significance of private sector involvement in infrastructure provision.

The rapid increase in both financial demands and the need for large and comprehensive highway infrastructure systems necessitates public authorities to muster support from the private sector. Public-private partnerships (PPP) have been increasingly adopted and cited as one of the most effective project delivery systems, which bring balance to risks and rewards between involved project participants. However, a number of PPP infrastructure projects around the world have been reported to have operating deficits and difficulties in debt-servicing. One of the major reasons for these difficulties is the unrealistic and inaccurate prediction of project investment performance during the initial stages of project viability and profitability analysis. Therefore, the research reported in this paper presents a formulation of a comprehensive framework that evaluates financial viability of PPP toll road projects. The framework utilizes a modified user-equilibrium traffic-assignment algorithm to estimate toll road traffic volume considering critical demand-influencing factors, which include travel time, pavement serviceability, and out-of-pocket user trip expenses. The proposed framework also considers the effects of the rehabilitation maintenance activities on the equilibrium flow pattern and project financial viability. This makes possible an estimation of the maintenance costs and frequency as well as an evaluation of the effects of rehabilitation programs on the traffic volume. The research reported in this paper also demonstrates the difference in traffic-flow patterns with and without interactive changes in pavement performance and traffic volume to demonstrate the need for incorporation of pavement performance and network levels of service into PPP highway project evaluation.

A reliable deterioration model is essential in bridge asset management. Most deterioration modeling requires a large amount of well-distributed condition rating data along with all bridge ages to calculate the probability of condition rating deterioration. This means that the model can only function properly when a full set of data is available. To overcome this shortcoming, an improved artificial intelligence (AI)-based model is presented in this study to effectively predict long-term deterioration of bridge elements. The model has four major components: (1) categorizing bridge element condition ratings; (2) using the neural network-based backward prediction model (BPM) to generate unavailable historical condition ratings for applicable bridge elements; (3) training by an Elman neural network (ENN) for identifying historical deterioration patterns; and (4) using the ENN to predict long-term performance. The model has been tested using bridge inspection records that demonstrate satisfactory results. This study primarily focuses on the establishment of a new methodology to address the research problems identified. A series of case studies, hence, need to follow to ensure the method is appropriately developed and validated.

National infrastructure systems (energy, transport, digital communications, water, and waste) provide essential services to society. Although for the most part these systems developed in a piecemeal way, they are now an integrated and highly interdependent “system of systems.” However, understanding the long-term performance trajectory of national infrastructure has proved to be very difficult because of the complexity of these systems (in physical and institutional terms) and because there is little tradition of thinking cross-sectorally about infrastructure system performance. Here, a methodology is proposed for analyzing national multisectoral infrastructure systems performance in the context of uncertain futures, incorporating interdependencies in demand across sectors. Three contrasting strategies are considered for infrastructure provision (capacity intensive, capacity constrained, and decentralized) and multiattribute performance metrics are analyzed in the context of low, medium, and high demographic and economic growth scenarios. The approach is illustrated using Great Britain and provides the basis for the development and testing of long-term strategies for national infrastructure provision. It is especially applicable to mature industrial economics with a large stock of existing infrastructure and challenges of future infrastructure provision.

Researchers have studied the vulnerability of roadway systems to disasters, such as terrorist attacks or natural disasters. However, the literature has not explicitly addressed other factors, such as infrastructure condition, that can significantly affect the vulnerability of roadway systems. In this study, the authors developed an algorithmic framework to address how the condition of a roadway network affects its vulnerability to disruptions. The vulnerability of the network was computed with respect to two measures: network efficiency and vehicle miles of travel. The results show that the average condition of the roadways in the network, the difference between the conditions of the roads, the uncertainties associated with road disruption probabilities, and link topological positions affect the roadway vulnerability.

Due to the repeated passage of trains, differential settlements are observed in the vertical direction of the railway tracks. These settlements depend on the platform quality, on the train speed, on the dynamic load, and on the track structural behavior. In recent years, several authors have defined laws and models for the prediction of settlement. The majority of those settlement models only consider the vertical degradation of the track, disregarding this phenomena in the lateral direction, since the vertical track geometry deteriorates faster than the horizontal one. In these models also the dynamic nature of the vehicle load is not considered accordingly. This paper presents a vehicle-track dynamic-interaction model for predicting the evolution of the vertical track profile and assessing the track response along the degradation process. The model is one-dimensional (1D) and it estimates the track degradation from a settlement law that uses deformation under instantaneous wheel passage as input. Although track degradation is a three-dimensional (3D) process, a 1D model is considered because the vertical component of the track settlement is the largest one. In order to predict the evolution of the track settlement due to the passage of the dynamic loads on the track, an iterative procedure is proposed. One cycle of this methodology is composed by three phases: numerical calculation of the train-track dynamic-interaction loads along the track, calculation of the track settlement, and definition of the track profile after settlement. Then, a new cycle begins. The proposed methodology is applied to a real track profile, measured in a Portuguese railway line, in order to understand the influence of the dynamic nature of the load on the track, the influence of the settlement rate and the influence of the type of vehicle on the evolution of the vertical track profile. The dynamic response of the track over time due to the evolution of the real vertical profile is evaluated through a vehicle-track dynamic-interaction approach. The attained results reveal the importance not only of the track irregularities, but also of the vehicle speed and characteristics on the evolution of the track profile. The numerical simulations presented in this paper evidence the potential of the proposed methodology, which can be used as a tool to forecast track settlement and to estimate the dynamic response of the track along the degradation process.

This article presents an optimization method for solving a material flow traffic assignment problem (TAP) in a large-scale construction project considering a hierarchical structure with fuzzy random variables. A multiobjective bilevel decision-making model is established in which the transportation time and cost in each arc are considered as fuzzy random variables. The construction contractor, the leader in the hierarchy, aims to minimize both total direct and transportation time costs. The transportation agency, next in the hierarchy, assesses the target to minimize total transportation cost. To deal with the uncertainties, the expected value operator and chance constraint method are used to transform the uncertain model into a calculable one. Furthermore, a multiobjective bilevel particle swarm optimization algorithm with a fuzzy random simulation-based constraint checking procedure is applied to solve the model. Finally, the Shuibuya Hydropower Project is used as a practical example to demonstrate the practicality and efficiency of the proposed model. Results and a sensitivity analysis are presented to highlight the performance of the optimization method.

A large proportion of U.S. civil infrastructure will need to be refurbished or replaced in the coming decades. How to do so while considering various aspects of sustainability is a major challenge for engineers, utilities, and government agencies. Additionally, infrastructure decisions have a long time horizon and, as such, the implications of these decisions need to be considered over the life cycle of each project. This study describes a triple-bottom-line analysis that was performed in cooperation with a water utility in the Northeast to quantify the life-cycle economic, environmental, and social impacts of refurbishing or replacing a large water-storage tank. A hybrid environmental life-cycle assessment (LCA) was conducted and included both on-site use of resources and emissions, as well as those that occur during production and transport of the various materials used in site preparation, construction, and maintenance. The proposed new tank would achieve better water quality and a different overall usable capacity, introducing additional complexity into the comparison. The analysis considered capital and maintenance costs, life-cycle greenhouse gas emissions, water consumption, human health impacts, and ecotoxicity, as well as local societal impacts such as congestion costs, noise, and the effect of aesthetic changes on property values. The results show that refurbishing the existing tank is preferable to new tank construction across all environmental impact categories, both in absolute terms and when normalized by the amount of high-quality water storage provided. Economic and societal cost analyses also preferred tank refurbishment, with significant variations depending on maintenance schedule and assumed tank lifetimes. This analysis provides detailed, quantitative data to support project decision making at the municipal level and demonstrates generally the application of multicriteria sustainability assessment to civil infrastructure planning.

The development of natural gas resources in the Marcellus Shale formation has progressed rapidly in the last several years, particularly in the Commonwealth of Pennsylvania. These activities require many heavy truck trips for equipment and materials, which can damage state and local roads that were not designed for high volumes of heavy truck traffic. For state transportation agencies, one measure of costs of shale gas development is the potential degradation of roadways resulting from shale gas development. This technical note, provides a first-order an estimate of roadway consumptive use costs of additional heavy truck traffic on Pennsylvania state-maintained roadways from Marcellus Shale natural gas development in 201, estimated at 1 about $13,000–$23,000 per well for all state roadway types, or $5,000–$10,000 per well if state roads with the lowest traffic volumes are excluded. This initial estimate of costs, is based on data on the distribution of well activity and roadway type in Pennsylvania, estimates for the number of heavy truck trips to construct and operate a single well, the corresponding equivalent single-axle loadings, and estimates of roadway life and reconstruction costs by roadway maintenance class in Pennsylvania.