Journal of Infrastructure Systems

Continuous, network-wide monitoring of pavement performance will significantly reduce risks and provide an adequate volume of timely data to enable accurate maintenance forecasting. Unfortunately, transportation agencies can afford to monitor less than 4% of the nation’s roads. Even so, agencies monitor their ride quality at most once annually because current methods are expensive and laborious. Distributed mobile sensing with connected vehicles and smartphones could provide a viable solution at much lower cost. However, such approaches lack models that improve with continuous, high-volume data flows. This research characterizes the precision bounds of the road impact factor transform that aggregates voluminous data feeds from geoposition and inertial sensors in vehicles to locate potential road distress symptoms. Six case studies of known bump traversals reveal that vehicle suspension transient motion and sensor latencies are the dominant factors in position estimate errors and uncertainty levels. However, for a typical vehicle mix, the precision improves substantially as the number of traversals approaches 50.

This paper presents the results of a study on the impact of climate change on road infrastructure in South Africa. The approach used built on previous work associated with the UNU-WIDER Development under Climate Change (DUCC) effort, emphasizing understanding of the impact of climate change on roads. This paper illustrates how climate change effects on road infrastructure can be evaluated at a national and provincial level to produce quantitative estimates of climate change from multiple future scenarios. The results of the study indicate that the national-level climate change cost impact in South Africa will vary between US$116.8 million and US$228.7 million annually in the 2050 decade for the median and maximum climate scenarios if a no-adaptation policy action is taken. Conversely, if a proactive adaptation action is taken, these costs can be reduced to US$55.7 million. The savings in costs arising from climate change impacts based on this policy approach is equivalent to building more than 10,000 km of new, secondary paved road by 2050. The analysis focused on the advantage of a proactive policy approach: the upgrade of road infrastructure where climate impacts will exceed current design and maintenance standards. A stressor-response methodology created by the writers is briefly explained, including the application of 54 potential climate futures using general circulation models (GCMs) approved by the Intergovernmental Panel on Climate Change (IPCC). This paper focuses on provincial-level impacts of both a proactive and a reactive approach. Decadal and average annual costs are detailed through 2100.

Nuclear magnetic resonance (NMR) spectroscopy was employed to evaluate the effects of chloride ion diffusion on the chemical environment of hydrated cement paste and concrete. ${}^{27}\mathrm{Al}$ magic angle spinning NMR spectra were obtained at different depths from the surface (surface, 3 mm, and 6 mm) exposed to chloride ion solution after 1 month. Deconvolution of ${}^{27}\mathrm{Al}$ NMR spectra obtained from different depths of concrete exposed to salt solution revealed a clear trend in chloride penetration. Most importantly, the intensities and areas under peaks corresponding to Friedel’s salt (8.5 ppm) and ettringite (13.3 ppm), which reflect the extent of chemical binding of chloride by cement hydrates, dropped with increasing depth. These trends demonstrate the ability of ${}^{27}\mathrm{Al}$ NMR spectroscopy to track chloride ion diffusion into the concrete. Fourier-transform infrared spectroscopy spectroscopy and wet chemistry methods were employed in order to verify the findings of NMR spectroscopy.

Nowadays, broadband plays an important role in our society. Countries around the world are pursuing initiatives to provide high-speed broadband as a universal service. Currently, digital subscriber line technologies (xDSL) dominate the broadband market, covering more than half of the subscriptions in Organisation for Economic Co-operation and Development (OECD) countries. However, these technologies are unlikely to keep up with these ambitious broadband goals; consequently, traditional copper carriers should need to undertake a costly transition in their access infrastructure sooner or later by moving from copper- to fiber-based lines. It is crucial to study, understand, and evaluate different ways of performing this transition to take advantage of the required investment. This paper presents an overall strategy for a copper-fiber switchover, transitioning from the analysis of current copper access, including its limitations, to systematic methods for planning the fiber upgrade and evaluating its economic feasibility. The following questions summarize the investigated key points of focus: Is it necessary to upgrade the copper access infrastructure? Where is it more efficient to initiate the fiber deployment? When is the investment expected to return profits?

This study has attempted to address a challenging problem in winter road maintenance, namely road surface condition (RSC) forecasting. A novel conceptual framework for short-term road surface condition forecasting is proposed. This framework is designed to consider all important conditional factors, including weather, traffic, and maintenance operations. Salt applications are modeled by considering a history instead of one single-time interval of salting operations. In this way, the variation of snow/ice melting speed caused by both residual salt amounts and salt-contaminant mixing state is effectively incorporated in the forecasting model, enabling accurate short-term forecasting for contaminant layers. This approach practically circumvents a major limitation of previous studies, making the postsalting RSC forecasting more reliable and accurate. Under this model framework, several advanced time series modeling methodologies are introduced into the analysis in order to capture the highly complex interactions between RSC measures and conditional factors. Those methodologies, especially the univariate and multivariate integrated autoregressive moving average (ARIMA) methods, are for the first time applied to the winter RSC evolution process. The forecasting errors of surface temperature and contaminant layer depths are all found to be small. The calibrated models are simple in structure, easy to interpret, and mostly consistent with physical knowledge. Compared to existing models, the proposed models provide extra flexibility for refactory, tuning, and deployment.

This paper presents a system of systems (SoS) Biofuel model considering the interdependency among the systems involved in biofuel development, including biofuel refinery location, transportation infrastructure, agricultural production and markets, environment, and social communities. The model provides the optimal infrastructure development and land-use allocation for biofuel production in a region considering socio-economic and water quality and quantity effects. The optimal development plan quantifies economic and hydrologic outputs and specifies biofuel refinery locations and capacities, refinery operations, land allocation between biofuel and food crops, optimal shipments of products and feedstock, and transportation infrastructure. The model is formulated as a mixed integer linear program (MILP) and is solved by an algorithm developed specifically to cope with the large size of the optimization problem. In addition to the development of the SoS-Biofuel model, this paper demonstrates the functionality of the model and its ability to analyze the effects of interdependency among subsystems by applying it to a watershed in Illinois. The SoS-Biofuel model is used to investigate the effects of different biofuel polices on infrastructure needs and related environmental consequences, highlighting the interdependencies inherent in the optimal development of the entire system.

The deterioration of a pavement surface can be described in terms of the presence and severity of distinct distresses, like potholes, cracking, and rutting. Each deterioration process is ordinarily described by a set of pavement indicators (e.g., number of potholes, percentage of cracks, international roughness index) that are measured during monitoring and inspection activities. Manifestly, there exist statistical correlations among the deterioration processes. For instance, cracks appearing on a road section may contribute to an increase in pothole occurrence, and vice versa. In order to mathematically formulate the statistical interdependency among deterioration processes, a Poisson hidden Markov model is proposed in this paper. The model describes the complex process of pavement deterioration, which includes the frequent occurrence of local damage (e.g., potholes) as well as the degradation of other pavement indicators (e.g., cracks, roughness). To model the concurrent frequency of local damage, a stochastic Poisson process is used. At the same time, a Markov chain model is used to depict the degradation of other pavement indicators. A numerical estimation approach using Bayesian statistics with a Markov chain Monte Carlo simulation is developed to derive the values of the model’s parameters based on historical information. The applicability of the model was demonstrated through an empirical example using data from a Japanese highway road link.

A study on using an unsupervised learning technique, called a self-organizing map (SOM) or Kohonen map, for the detection of road cracks from pavement images is described in this paper. The main focus is on highly textured road images that make the crack detection very difficult. Road images are split into smaller rectangular cells, and a representative data set is generated for each cell by analyzing image texture and color properties. Texture and color properties are combined with a Kohonen map to distinguish crack areas from the background. Using this technique, cracks are detected to a precision of 77%. The algorithm also resulted in a recall of 73% despite the background having very strong visual texture. The technique applied here shows a great deal of promise despite the images being captured in an uncontrolled environment devoid of state-of-the-art image-acquisition setups. The results are also benchmarked against an advanced algorithm reported in a recent research paper. The benchmarking shows that the proposed algorithm performs better in terms of reducing the false positives in crack detection.

As the risk of climate change continues, the need for transportation adaptation planning increases. Decisions regarding development (or redevelopment) of transportation systems, specifically transit facilities, have an essential role in the future of public mobility. Therefore, formalizing the project prioritization process using geographic information systems (GIS), sustainability metrics, and decision analysis techniques is needed. This research aims to identify sustainability metrics that address environmental, economic, and societal factors of transportation projects (rail or bus networks) using an integrative GIS and AHP (analytic hierarchy process) approach. Sustainability metrics are identified as factors based on their relevance to transit systems and their opportunity for spatial application, including climate-related factors such as sea level rise. Then, AHP is applied through a pairwise comparison survey in order to determine the importance of each factor to project prioritization. The survey results are used to develop the project and location prioritization index (PLPI). A case study application based on Philadelphia County’s commuter rail network is used to demonstrate the applicability of the index to a real world network.

Unmanned aerial vehicles (UAVs) allow remote imaging which can be useful in infrastructure condition evaluation. Furthermore, emerging noncontact sensing techniques such as digital imaging correlation (DIC) and other photogrammetric and visual approaches, including simultaneous localization and mapping (SLAM), can compute three-dimensional (3D) coordinates and perform deformation measurements as in the case of DIC/photogrammetry. A quantitative assessment of ways remote sensing in conjunction with UAVs could be implemented in practical applications is critically needed to leverage such capabilities in structural health monitoring (SHM). A comparative investigation of the remote sensing capabilities of a commercially availabl’e UAV, as well as both an optical metrology system known by the acronym TRITOP and the X-Box Kinect, is presented in this paper. The evidence provided demonstrates that red-green-blue cameras on UAVs could detect, from varying distances, cracks of sizes comparable to those currently sought in visual inspections. In addition, mechanical tests were performed on representative bridge structural components to attempt, for the first time to the writers’ best knowledge, deformation measurements using an aerial vehicle; displacements and corresponding accuracies were quantified in static and flying conditions. Finally, an outdoor feasibility test with the UAV was accomplished on a pedestrian bridge to test the marker identification algorithm.

Uncertainty about the scale of future demand presents a challenge for infrastructure design. The attraction of flexible strategies, incorporating upgrade options that can be exercised when and if required, has been recognized for a long time. The techniques of real options allowed the benefits of flexible strategies to be quantified for the first time. A new and simplified methodology for real options analysis has been developed and implemented in software. It performs evaluation in both cost and environmental impact terms. A case study of a real highway in Spain was evaluated using the new methodology and software. Seven alternative strategies for the roadway (subgrade, base course, and surface course) were evaluated, six with upgrade options that are exercised if the traffic demand crosses specified thresholds. The rate of traffic growth, and also the discount rate for financial evaluation, were treated as uncertain variables. The highway was designed when the Spanish economy was growing strongly and a high roadway specification performed best. Before the highway was built the economy crashed and construction was canceled. Recalculation for the posteconomic shock conditions with lower traffic growth and a higher interest rate favored a lower highway specification with upgrade options. Sensitivity analysis shows how the specification choice varies with the values taken by the uncertain variables. A novel specification was proposed to maximize flexibility when there is high uncertainty about traffic growth, and evaluation showed that it performed better in many conditions. The methodology of evaluating flexible strategies could be used in many applications, including infrastructure for climate change adaptation.

This paper examines the critical factors affecting the efficient use of public investments in infrastructure to support trade and economic development. Investments in infrastructure have increasingly been used to improve the infrastructure quality, thus stimulating trade and economic development in developing countries. Nevertheless, a number of infrastructure challenges faced by developing countries remains. This raises the question about the efficiency in the use of public investments in infrastructure. Based on the case of Vietnam, this paper identifies a number of deficiencies in the planning and policymaking process as well as in the implementation of infrastructure plans. This paper aims to show policymakers that the critical challenge faced by developing countries in infrastructure construction is thus not about the funding but should be more about effective planning and implementation for long-term benefits.