Journal of Infrastructure Systems

Forum papers are thought-provoking opinion pieces or essays founded in fact, sometimes containing speculation, on a civil engineering topic of general interest and relevance to the readership of the journal. The views expressed in this Forum article do not necessarily reflect the views of ASCE or the Editorial Board of the journal.

This paper introduces the new concept of restoration interdependencies that exist among infrastructures during their restoration efforts after an extreme event. Restoration interdependencies occur whenever a restoration task in one infrastructure is impacted by a restoration task, or lack thereof, in another infrastructure. This work identifies examples of observed restoration interdependencies during the restoration efforts after Hurricane Sandy as reported by major newspapers in the affected areas. A classification scheme for the observed restoration interdependencies is provided that includes five distinct classes: traditional precedence, effectiveness precedence, options precedence, time-sensitive options, and competition for resources. This work provides an overview of these different classes by providing the frequency they were observed, the infrastructures involved with the restoration interdependency, and a discussion of their potential impact on interdependent infrastructure restoration. The analysis is important because it provides a new understanding of how the restoration efforts of infrastructures are linked across systems and motivates the need for potential information-sharing in interdependent infrastructure restoration.

This study proposes a model and a solution method to optimize protection planning for transportation-infrastructure systems such as highway networks, which are subject to disasters given a limited budget. When links of a highway network fail because of disasters, the connectivity of the highway network can be damaged and travel demand cannot be served. In this study, a situation is considered when links of a highway network can be protected against disasters to maintain its connectivity. Given that damage to a highway network caused by a disaster is probabilistic, a two-stage stochastic programming (SP) model is developed to identify the optimal protection plan that maximizes connectivity reliability for highway networks. The measure of connectivity reliability is defined as the expected number of origin–destination pair groups that can be served under all scenarios of link failure caused by a disaster. The main contribution of the model formulation is that the travel cost of alternative paths and (normal or postdisaster) travel demand can be simultaneously considered in the optimization. As the probability of failures caused by a disaster can be extremely low, the required number of scenario samplings to obtain accurate estimates for connectivity reliability could be large, which causes computational difficulty for solving the SP problem. Therefore, a solution method is developed to practically solve the SP model. The proposed methodology is demonstrated with a highway bridge system that is subject to earthquakes. Results show that the proposed model has potential for improving the connectivity reliability of highway networks that are subject to disasters.

The new draft AASHTO standard practice PP70-10 and PP69-10 are promising to change the landscape of rut measurement. PP70-10 specifies the technical requirements for transverse profiling and PP69-10 defines multiple parameters such as percent deformation and rut cross-sectional area to characterize a rut. Per PP70-10, an inspection device should cover at least 4.0 m (13 ft) transverse width in order to cover the full lane under vehicle wandering. However, for many existing transverse profilers, it is challenging to identify the lane markings from the raw images in a consistent manner. This deficiency would prevent these systems from accurately calculating PP69-10-based rut attributes. In this study, with the field data collected in Arkansas national highway systems (NHS) using an automated system called PaveVision3D Ultra, the impact of lane offsets on PP69-10-based rut attributes is investigated. The rut attributes calculated with programs embedded in the system, where accurate lane locations are semiautomated extracted from both 2D and 3D images, are set up as base values. It is found that lane offsets greater than 50 mm would cause significant errors in rut measurement. To reduce the negative effect of inaccurate lane identification on rut characterization, a methodical framework is proposed in this research. A case study is performed to demonstrate that machine learning approaches could satisfactorily aid in increasing the accuracy of rut characterization even when the locations of actual lane markings are unknown. This research would improve the reliability and accuracy of the new AASHTO rut measurement.

The objective of this paper is to model interconnectivities among systems and subsystems of transport and critical infrastructures vulnerable to climatic events that impose the highest impacts on people and regional economies from chronic to episodic events. To achieve this objective, the study first lays out a high-level conceptual map of the interacting complex systems, then depicts some of the interconnections between and within these complex systems as related to climatic events. By outlining these interconnectivities at a high-level, analytical approach, the strategic risk register system (SRRS) is introduced to demonstrate the feasibility of capturing these interactions and links analytically, and ultimately to model risks and threats imposed on transport and civil and public systems due to climatic events. A hypothetical episodic event is initiated to examine the credibility of the methodology and the applicability of the proposed technique to model the infrastructures’ connectivity and risks imposed on the infrastructures. The outcomes demonstrate that as the level of connectivity decreases, the potency value increases.

The function of a spillway gate system is to control the flow of water through an opening for purposes such as flow regulation, flood control, emergency water release during load rejection, and dewatering for maintenance operations, or to remediate or respond to structural deficiencies. A growing problem for dam owners is the aging of such structures. As spillway systems age, more rigorous maintenance, inspection/testing, and repair/replacement of parts are required to ensure their long-term safe operation. Executing these measures may be very costly to dam owners and, if not conducted appropriately, may not have the desired effect on the reliability of the system. The first objective of this paper is to investigate the effect of inspection, testing, repair, and replacement strategies on the availability of spillway gate components. The second objective is to model the availability of the entire spillway gate system under flood and load rejection scenarios to determine the ability of the system as a whole to operate safely under emergency conditions. These two objectives are then used with cost optimization to develop recommendations on the type and frequency of inspections and tests for different types of spillway gate systems. A software application was developed to achieve the objectives by modeling the spillway gate components and system, determining component/system unavailability based on type and frequency of inspections/tests, and obtaining the optimum inspection/testing strategy.

A crack monitoring method based on a smart film crack sensor was proposed by the authors to monitor different characteristics of cracks such as initiation time, length, propagation, shape, and location. However, in engineering practice, data changes frequently after running for about half a year; it does not appear in the earlier period. Aiming at the problem, this paper focuses on electrical analysis in smart film crack monitoring. It is found that coupling capacitance increases with dielectric constant of enameled wire of the sensor, thus leading to the increase of coupling voltage and affecting the judgment. Therefore, a model is formulated to infer the relationship among the frequency of detecting signal, parasitic capacitance, and coupling interference voltage. On this basis, a smart film crack sensor is redesigned and the anti-interference circuit is added to hardware; the low-frequency detecting signal and the custom judgment mechanism are adopted to lower coupling voltage in the software. In consideration of improving the reliability of data transmission, a check mechanism is employed. Meanwhile, the improved sensor is testified to by experiments and applied to Chongqing Lijiatuo Yangtze River Bridge.

This study compared output from mobile inventory data collection vehicles to manually collected data techniques with a focus on two-way communications primarily through the submission of a sample data set to be analyzed prior to the submission of a full data set. The interim submittal and feedback to the vendors based on that resulted in a marked improvement in data quality for 5 of the 28 assets studied. After feedback, it is apparent that highway data collection vendors can accurately locate the vast majority of assets, with the primary exception being those that are occluded by vehicles or surrounding landscaping, such as those assets in the median. Along with the locations of assets, vendors showed promise at collecting many of the feature descriptions such as asset type and condition. Many of the elements (location, type, etc.) for a particular asset type that created collection difficulty were only problematic for a particular vendor, which suggests that further improvements may be achieved through additional communication and more explicit definitions and examples by the contracting agency. Using measurement tolerances, the research team determined that measurements of height, grade, and azimuth were generally accurately obtained; however, measurements parallel to the direction of travel, such as offset and width, posed problems with accurate measurements. Last, the accuracy of data location was acceptable for finding assets in the field; however, many specific point features such as drop inlets or attenuators were not geo-located, but instead located from the vehicles position in the roadway. This appears to be standard practice for some vendors; therefore, if a specific location outside the roadway is desired, it should be clearly indicated in the instructions. Although mobile data collection is sufficient for most efforts, there is still room for improvement if more detailed location is necessary.

Networked infrastructures serve as essential backbones of society. In particular, road transportation networks have a principal role in people’s everyday lives because they facilitate physical connectivity. External factors, such as disasters and failures, may degrade the performance of road networks. For example, severe flooding may disrupt a large area of a network, leading to cancellations and delay of several trips over the network. In addition, the collapse of a bridge could render certain nodes and links ineffective, thereby affecting traffic flow conditions. The authors formalized and developed a computational framework to assess vulnerability of road networks due to critical artificial or natural disruptions. The framework is built upon recent developments in interdisciplinary domains, such as network science, computational science, and transportation engineering. Using the proposed framework for the Greater Philadelphia road network, the network was divided into several high-critical, medium-critical, and low-critical clusters. The clustering results varied based on the time and severity of disruption. For instance, during the p.m. peak, disruption of roads within the city of Philadelphia was more critical than other roads of the region. However, during the a.m. peak, the roads surrounding the city of Philadelphia were more critical. The framework was validated using real-world observations and simulated traffic results.

Utility management systems (UMSs) have become more common for government agencies, and their use has recently been advocated by AASHTO on a statewide basis. By identifying current utility locations and correlating utility-related information between ongoing and upcoming projects, UMSs can reduce the occurrence of unexpected delays and cost overruns resulting from unforeseen utility conflicts found during construction. This paper presents an analysis of a two-part survey of local agency professionals in the transportation industry, regarding perceptions of UMS cost and time benefits and implications associated with implementation. The paper evaluates two hypotheses regarding local agencies’ perception on two key areas: (1) impact of UMS cost and time implementation requirements on implementation decisions; and (2) difference in cost and time benefits and implications for different types of local agencies. The study finds that despite its obvious potential benefits, UMSs are not being widely implemented at the local level because the cost to adopt them is perceived to be high. Additionally, this study found opportunities for improvement that may considerably advance UMS practices.

Rehabilitation programs are essential for efficiently managing large networks of infrastructure assets and sustaining their safety and operability. While numerous studies in the literature have focused on various aspects of infrastructure rehabilitation, limited efforts have investigated the overall dynamics of the process. The research presented in this paper, therefore, takes a holistic view to investigate the dynamics that affect rehabilitation decisions and the long-term performance of an infrastructure network. First, the interactions among the main parameters related to asset deterioration, rehabilitation actions, and cost accumulation have been analyzed using causal loop diagrams (CLDs). Afterward, a system dynamics (SD) model has been developed based on the CLDs and the underlying mathematical relations among the various parameters. The SD model was then tested on a network of 1,000 assets over a 50-year plan, considering a range of rehabilitation policies regarding budgets, possible rehabilitation actions, and fund allocation options. The model proved to be a practical and effective tool for quick assessment of the long-term impact of rehabilitation policies on infrastructure performance and costs.

Cross-impact analysis (CIA) is a technique that is designed to predict future events by capturing the interactions among variables. It is an appropriate tool to deal with the selection of a project delivery method. Project delivery selection involves the assessment of tradeoffs between numerous risks and uncertainties, complex relationships among variables, and multiple decision alternatives. In fact, the number of variables involved in project delivery decisions creates CIA models that are extremely complex, and few researchers have attempted to apply them. This paper presents a hybrid CIA approach to project delivery decisions in highway design and construction. It provides for the evaluation of project cost with projects in three fundamental delivery methods: design-bid-build (DBB), design-build (DB), and construction manager/general contractor (CMGC). The paper discusses in detail how the cross-impact concepts support the selection of an appropriate delivery method in highway projects. The hybrid CIA approach integrates the results from the factor analysis of 31 delivery risk factors, which were evaluated by 137 practitioners, to determine the interaction between variables in the cross-impact matrix. These data allowed the researchers to reduce the number of required judgments in the CIA model approximately from more than 3,000 to fewer than 300. A case project from the Florida Department of Transportation demonstrates the approach. The hybrid CIA approach provides a defensible and repeatable process for highway agencies to quantitatively select an appropriate delivery method for their projects. More fundamentally, the findings from this paper contribute to theory by providing a new method to apply the CIA technique. It is expected that researchers can use this hybrid CIA approach for other areas in construction engineering and management research.

The environmental impacts and economic costs associated with passenger transportation are the result of complex interactions between people, infrastructure, urban form, and underlying activities. When it comes to roadway infrastructure, the ongoing resource commitments (which can be measured as embedded impacts) enables vehicle travel, which is a dominant source of air emissions in regional inventories. The relationship between infrastructure and the environmental impacts it enables are not often considered dynamic. Furthermore, the environmental effects of roadway infrastructure are typically assessed at a fine geospatial and temporal scale (i.e., a short distance of roadway over a short period of time) and there is generally poor knowledge of how the growth of a roadway network over time creates a need for long-term maintenance commitments that create environmental impacts and lock-in vehicle travel. A framework and operational lifecycle assessment (LCA) tool [City Road Network (CiRN) LCA] are developed to assess the extent to which roadway commitments result in ongoing and increasing environmental and economic impacts. Known for its extensive road network and automobile reliance, Los Angeles County is used as a case study to explore the relationship between historic infrastructure deployment decisions and the emergent behavior of vehicle travel. The results show that every kilogram of greenhouse gas (GHG) emissions resulting from construction and maintenance has led to 27 kg of GHG emissions in fuel combustion. Similarly, every public dollar invested into the network has created $21–$46 in private user spending. As states and regions grapple with financing the upkeep of aging infrastructure, a solid understanding of the relationship between upfront infrastructure capital costs, long-term maintenance costs, and associated long-term environmental effects are critical. In Los Angeles, the infrastructure that exists was largely deployed by 1987. Since then, maintenance costs are estimated to have exceeded city budgets despite minimal growth in infrastructure. The research demonstrates how infrastructure matures (i.e., its stages of growth toward completion), it becomes locked-in, leading to transitions from a capital financing focus to foci on securing rehabilitation and maintenance costs, and the share of environmental impacts changing from being somewhat balanced between embedded infrastructure construction impacts and vehicle use to today where vehicle use creates impacts several orders of magnitude greater than those associated with rehabilitation.

In August 2012, Hurricane Isaac made landfall twice in Louisiana. Cumulatively, over 1 million customers lost electricity as a result of the hurricane, some for more than 10 days. As a disaster, Hurricane Isaac is relatively unique because of the opportunity to largely isolate impacts and decisions directly associated with the electricity outage and restoration from cascading impacts related to direct flood or wind damage. Louisiana emergency managers, business representatives, and public officials were interviewed to develop and analyze a case study of the outage and restoration event. Interviewees were asked about (1) the significant impacts from the outage, (2) what (if any) lasting effects would result from the outage, (3) the relative performance of power restoration, (4) the effectiveness of communication between stakeholders, and (5) factors influencing the public and political perception of restoration performance. Other collected data included content from news media, government documents, press releases, situation reports, and publicly available quantitative data. Results suggest that there were few long-term impacts from the outage, and restoration performance was not unusually slow. Even so, the electricity service provider was the subject of vocal criticism. The findings of the case study highlight the importance of all aspects of communication and awareness raising before, during, and after power outages, as well as further research to facilitate this. The study reinforces the need to improve power continuity using a wide range of technical, political, and organizational strategies. Research and policy development is particularly needed for improving continuity of transportation, health care facilities, and gasoline provision. Understanding of the sociotechnical role of electricity outages within broader disaster contexts can only be improved through further interdisciplinary research.