Journal of Infrastructure Systems

Previous studies of models for evacuation routing usually divide the space into multiple interlinked zones. However, for public buildings that are characterized by a more complicated spatial composition, it may be harder to clearly and objectively define the margins for each zone. This paper proposes an approach that determines the most appropriate guidance locations and assembles them into an efficient guiding network. This allows evacuation routes to be constructed based on the links between starting points, guiding nodes, and terminal points. This approach avoids the difficulty of dividing a space into multiple zones and conforms more to real-life evacuation behavior. This paper also proposes an integrated model that applies simulation to estimate the evacuation time and ant colony optimization algorithms to search for an evacuation routing plan and a near-optimal guiding solution. The feasibility of the model is evaluated through a case of one floor in a hospital building. The results indicate that the model provides valuable suggestions for evacuation planning and offers practical support for the design of an evacuation guidance system.

In this paper, a methodology for quantifying lifetime risk of bridge superstructures is presented. The risk is quantified in terms of the expected direct and indirect losses. A scenario-based approach, which uses the Pontis element condition rating system, is used for identifying the expected losses. The deterioration process of bridge components regarding the transition between the condition states is modeled as a Markov process. In addition, a reliability-based approach is used to compute the probabilities of component and system failure, given the condition states. The methodology is illustrated on an existing bridge in Wisconsin. The expected losses associated with the flexural failure of girders are quantified in time. Furthermore, the time-variant risk-based robustness index is assessed.

Accurate prediction of pavement performance is essential to a pavement infrastructure management system. The prediction process usually consists of classifying sections into families and then developing prediction curves or models for each family. Artificial intelligence, especially machine learning algorithms, provides a medium to investigate techniques that address these management concerns. This paper presents a two-stage model to classify and accurately predict the performance of a pavement infrastructure system. First, sections with similar characteristics are classified into groups using a support vector classifier (SVC). Next, a recurrent neural network (RNN) uses the classification results from the first stage in addition to other performance-related factors to predict performance. A case study using the Minnesota Department of Transportation (MnRoad) test facility database shows that the proposed model is a good classification decision support system, has better prediction results than the single-stage RNN model, and captures all underlying effects of the different variables. The significance and a sensitivity analysis of the model parameters are also presented.

The novel concept of applying decorative concrete overlay (DCO) during the infrastructure design process supports the nationwide initiative for increased implementation of context-sensitive solutions. The DCO is a polymer-acrylic cementitious microtopping, a few millimeters thick, colored to look like stone, brick, or wood, for example, and skimmed or sprayed onto existing or new structural concrete. This research aimed to evaluate the potential of DCO to serve as a long-lasting aesthetic treatment for transportation infrastructure. A brief case study of typical decorative treatments and their associated costs indicated that DCO would be competitive in certain infrastructure applications. The DCO durability characteristics were examined by incorporating environmental conditions such as submersion in saltwater, exposure to ultraviolet light and moisture cycling, and intrusion of deicing salts (salt ponding). Fracture and pull-off adhesion testing were also conducted to evaluate the bonding of DCO to structural concrete. Flexural fracture testing showed that the bond between the DCO and concrete sample was unbroken, and failure was observed in the structural concrete. Adhesion testing indicated that the spray-on application method produces significantly stronger bond strengths than the alternative sponge technique. However, DCO must be sprayed on within a specific thickness range in order to achieve optimum strength. Chloride-ponding test results indicated that an additional benefit of DCO is that it acts as a protective coating against salt penetration. Overall, the research indicated that there may be significant potential for implementing DCO as a lasting and durable decorative contribution to the transportation design process.

Public private partnerships (PPPs) are arrangements under which the private sector supplies infrastructure assets and services that traditionally have been provided by the public sector. Public authorities may enhance the marketability of PPP projects by offering revenue guarantees. However, government revenue guarantees can pose significant fiscal risks for the issuing government, particularly during economic crises. This paper presents a new type of revenue risk hedging contract, the dynamic (flexible) revenue insurance contract, which can be offered as an alternative to the conventional government guarantees. This new contract gives PPP stakeholders other than the government the opportunity to participate in the revenue risk coverage. Potential revenue risk insurers include international financial institutions, export credit agencies, and private insurance companies. The key feature of these new contracts is that they facilitate the pooling of project revenue insurers by accommodating insurer financial and risk preferences. These contracts are modeled as multiple exercise options and priced by using two different Monte Carlo methods, the multiple exercise boundary method and the multiple least-squares method. Because of its inherent flexibility, the dynamic revenue insurance contract offers risk coverage similar to the conventional revenue guarantee at much lower cost to the government. A numerical example on a build operate transfer toll road project shows the substantial cost reduction sustained by the government.

Research and development for energy systems (e.g., smart and secure microgrids for industrial and military installations and solar cogeneration technologies) must account for deep uncertainties and emergent conditions including economic, regulatory, technological, mission, demographic, and environmental or ecological, among others. Deep uncertainties involve structural and qualitative perspectives or trends that change the game. Recent literature has identified emergent conditions and deep uncertainties that are most influential to decision criteria priorities among existing energy assets and systems. This paper introduces a practical method to help prioritize strategic energy research and development investments and minimize opportunity loss or program risk for investments with a long time horizon. There are a number of interesting observations that can be perceived from this method including robustness, scenario influence, risk, and opportunity. The method is recommended to help achieve consensus of program managers, installation commanders, energy managers, technology vendors, urban planners, and customers and tenants. A quantitative demonstration is provided that addresses five scenarios of deep uncertainty, ten performance criteria, and six investment portfolios.

Research has separately existed for life-cycle costs and environmental assessment of photovoltaic systems. This study provides a framework for the identification of the optimal set of residential photovoltaic system options in terms of costs and selected air emissions by assessing the life cycle of the product from manufacturing to disposal. Analyzed air emissions include carbon, nitrogen oxides, sulfur oxides, carbon monoxide, and particulate matter. The study focuses on general residential installations within the San Francisco Bay Area of California. Pareto frontiers are determined for costs and emissions, and the best options for suppliers are identified in each case, including sensitivity analysis of the installation planning horizon. Choosing multicrystalline silicon modules over thin film modules has the greatest impact on cost reduction. The electricity mix with which the modules are produced has the greatest impact on the emissions.

Many departments of transportation (DOTs) rely on pavement management systems (PMSs) to plan maintenance operations by identifying schedules that maximize overall network condition subject to cost constraints. PMSs do not typically incorporate environmental considerations, despite being ideally situated in the decision-making process to balance performance and environmental goals. This work proposes an expanded PMS framework to incorporate greenhouse gas (GHG) emissions by utilizing a multiobjective genetic algorithm (GA) validated against past DOT interstate maintenance practices in Virginia. In practice, the DOT relies heavily on corrective maintenance, a treatment of intermediate intensity, resulting in suboptimal use of financial resources and emissions of GHG. The model suggests that alternative maintenance plans exist that yield higher network pavement performance with both lower costs and GHG emissions over the same time period. For the validation area considered, an optimized management plan could achieve the same network pavement performance with 60% of the cost and 50% of the GHG emissions. The framework, as presented, uses current DOT deterioration models and cost estimates to demonstrate the feasibility of its direct integration with existing PMSs.

Over the past few decades, the design capacity and service condition of many bridges, which are vital components of the surface transportation infrastructure reliability and functionality, have been challenged by rapid traffic growth and scarce maintenance funds. Numerous incidents of bridge collapse call for an urgent need to develop a systematic method of assessing the failure risks and identifying the initiating events that can lead to a bridge collapse. This paper presents a process of bridge failure risk analysis through fault-tree modeling for a common type of bridge known as segmental concrete box girder bridge. The process was applied to a segmental box girder bridge in South Carolina and was found to effectively identify causal factors of bridge failure and estimate overall failure risk. However, to comprehensively assess the probability of bridge failure, fault-tree analysis can best be used in combination with current risk assessment methods, such as visual inspections and structural health monitoring sensors.

The impact of the prevailing capital markets on public-private partnerships and their future use in the medium term in Australia are examined. Alternative finance mechanisms that can be used by Australian state governments to procure infrastructure in the future are also discussed.

Traffic loading for pavement deterioration should be modeled as a dynamic indicator based on trip distribution derived from spatial economics. The estimation of modal distribution of trips and land development has been the main focus of integrated land use and transport models. However, no connection with transportation asset management has been established. This paper proposes the use of spatial economic simulation to forecast freight-traffic distribution to improve pavement-deterioration modeling. A case study of trade flows between Canada’s Atlantic Provinces and Québec is used to show the pitfall of current management models in estimating rates of deterioration, underfunding maintenance, and rehabilitation strategies. It was found that a total cost of $25 million could maintain adequate levels of condition under the current performance modeling; however, such a budget is inadequate when performance is based on forecasted truck traffic. It was also found that aggregation of pavements in a few homogeneous groups resulted in the inability to prioritize investments considering the economic relevance of the road in the region. This study suggests the use of individual deterioration models for strategic roads.