Journal of Infrastructure Systems

Public-private partnerships (PPPs) are critical for delivering infrastructure assets worldwide. They encompass a variety of public and private organizations, and therefore, participants’ behavior can significantly affect the life-cycle performance of projects. However, the extant literature shows a lack of attention to the process efficiency of PPPs from behavioral and economic perspectives. Thus, we developed a managerial framework in this study, which is pivotal to understanding and managing the relationships between participants’ behaviors and project process efficiency in PPPs. Using social network analysis, a total of two case studies of transport PPPs were undertaken to examine and refine the developed paradigm. This study expands the knowledge of the organizational management of PPPs and will be useful for enhancing project delivery through an improved management of participating organizations’ behaviors.

A simplified exhaustive search approach is proposed to estimate the nonhomogeneous transition probabilities for a particular infrastructure element. The yearly nonhomogeneous transition probabilities associated with discrete-time Markovian chains can be estimated for a given analysis period mainly using observed performance ratings. The proposed approach is applicable to Markov chains comprised of only two state transitions, namely remaining in the same current state or transiting to the next worse one. The exhaustive search aims at finding two optimal deterioration exponents that would yield the optimal initial and terminal transition probabilities subject to a minimal difference between the predicted and observed performance ratings for each transition. Therefore, the exhaustive optimization is mainly carried out with respect to two parameters only. A limited number of annual infrastructure performance ratings spanned over an analysis period is required to estimate the corresponding initial and terminal transition probabilities. In contrast, the intermediate transition probabilities for each transition can be estimated using either linear or quadratic approximation. The sample results presented for both hypothetical and actual performance data indicated the simplicity and efficiency of the proposed approach in yielding reliable optimal solutions. In particular, the results indicated that there is more than one compatible solution, and that a Markov chain with a smaller size is required when the deterioration rates are higher considering only two state transitions.

Heating, ventilation, and air conditioning components are among the significant component groups in building services. Around 50% of a buildings’ energy consumption is related to HVAC systems. Published research has indicated there is a strong relationship between the deterioration rate and energy consumption of HVAC systems. High energy consumption due to aging can significantly impact the total life cycle operating cost of HVAC systems. The deterioration process depends on various factors such as exposure condition, utilization, types of unit, maintenance regime, and others. A comprehensive literature review indicated that existing models do not consider the aforementioned parameters in predicting the degradation and, therefore, the accuracy of the current models may be low. Existing modeling practices do not consider runtime as an influencing parameter on deterioration forecasting for HVAC components. The primary knowledge contribution of this article is addressing the aforementioned limitations in existing modeling practices. Deterioration prediction models based on the Markov process are developed to identify the effect of different runtime cluster groups on the degradation of selected HVAC component groups in 2019 condition inspection data of buildings in Melbourne, Australia. In the first part of the article, a comprehensive literature review is carried out to identify the knowledge gap. Data were analyzed for 20 critical HVAC components with three main cluster groups based on runtime in the second part. Deterioration prediction models are derived based on the Markov-chain Monte Carlo (MCMC) method and the nonlinear optimization method. In the end, a detailed analysis of the results is carried out with a three-way comparison in order to demonstrate the effect of runtime on deterioration forecasting for HVAC components.

Barge transport operations on the Mackenzie River, a major transportation corridor in the Northwest Territories, are impacted by multiple sources of uncertainties. In particular, the impacts of climate change on this important corridor have led to summer shipping seasons that are growing more volatile in terms of length and quality. This change can lead to a growing reliance on costly airlifts for delivering essential freight that cannot be delivered by barge during seasons that end early due to low water. The Government of Northwest Territories has been planning the construction of the Mackenzie Valley Highway (MVH) for decades to provide cheaper, more reliable transportation for communities. However, the costs of constructing the MVH are prohibitive, and traditional benefit-cost analyses are unable to consider flexible investment actions in response to uncertainties. Therefore, we apply a real options modeling framework to determine if and when to construct the different segments of the MVH, considering climate change and freight demand uncertainties. We first model climate and freight demand uncertainties as geometric Brownian motion processes. Next, a benefit-cost model is developed. Finally, we use the least-squares Monte Carlo method to solve for extended project values and optimal investment times for each segment. The results indicate that Segment 2 has the largest value with an optimal seven-year delay in investment time, followed by Segment 1, Segment 3, and Segment 4 in the last year of the planning period (or possibly beyond). Freight demand volatility appears to have the greatest impact on project values and investment years. The results show that, although the benefits of construction may not outweigh the costs now, they may at some future date; in between, decision makers have opportunities to change their minds as conditions change. This is particularly important in northern Canada, where highly costly infrastructure investment decisions are subject to massive uncertainties. Overall, we see such an approach as a tool to communicate the value of uncertainty in infrastructure benefit-cost analyses and as one tool in a larger decision-support toolbox that is required for major transportation investments in northern Canada.

Large-scale infrastructure operates in a complex and uncertain environment. When an unexpected safety accident occurs, efficient emergency decision-making plays a crucial role in safe operation. Case-based reasoning (CBR) provides an effective tool in emergency response decision-making. However, case retrieval efficiency and missing values for case attributes present significant challenges to CBR. Firstly, in this study, a unified framework representation method is constructed for accident cases of large-scale infrastructure. Secondly, an inductive indexing approach is used to preclassify cases according to key attributes of the cases in order to improve retrieval efficiency. Thirdly, this study proposes a two-layer integrated structure and attributes similarity algorithm based on the K-nearest neighbors (KNN) method in a bid to overcome the missing attribute values of the cases. Fourthly, the emergency decision-making system is developed for the large-scale infrastructure. Finally, a case study of the South-to-North Water Diversion Project in China is undertaken to verify the proposed methods and computing system. This study provides a valuable decision-making approach for operational safety-related emergency management of large-scale infrastructure.

This paper presents a new integrated approach to address household perceptions of the quality of tap water and the influence of such perceptions on households’ decisions to use bottled water and the related consumption patterns. It is hypothesized that these perceptions influence households’ bottled water–related decisions. To the authors’ knowledge, this is the first time this approach has been adopted by including the output of the perception of the tap water model (probabilities of satisfaction levels) as an explanatory variable in the household consumption of bottled water model. This paper also evaluated people’s perceptions of tap water quality by conducting physiochemical tests on samples of tap water collected from households. A correlated random parameters ordered probit model was estimated to provide insights into factors shaping the perceptions of respondents toward the quality of tap water. A zero-inflated ordered probit model with correlated error terms that explicitly accounts for households’ quality of tap water perceptions was estimated in order to gain deeper insights into the factors influencing the decision to use bottled water and the levels of consumption. The results showed, among other things, that households’ perceptions of the quality of both tap and bottled water are important predictors of the amount of bottled water consumed by a household as its primary source of drinking water. Our results show the need to account for such perceptions when formulating a demand model for bottled water consumption. The majority (75.72%) of people believe that tap water is of unacceptable quality, whereas the physiochemical tests actually show the opposite.

The goal of this paper is to define a quantitative measurement system capable of capturing multiple dimensions of a community impacted by disaster. The multiple dimensions of a community are defined here as the seven community capitals inherent to any community, namely, financial, political, social, human, cultural, natural, and built capitals. A two-pronged approach is proposed, where one prong relates organizations to community capitals using a novel scoring system aligned with the definition of each community capital, and the other prong relates building-damage consequences to the community capitals, including number of damaged buildings for built capital, household dislocation for social capital, morbidity rates for human capital, accessibility changes for political capital, and repair costs for financial capital. The framework is exemplified on a virtual community, Centerville, under an earthquake scenario. The example demonstrates that the proposed approach for quantifying capitals provides useful measures of disaster impacts and can readily inform risk-based decision making.

The occurrence of unpredictable disruptions in infrastructure systems has urged the development of a variety of recovery strategies. Each recovery strategy follows different recovery preferences or criteria, such as the criticality of damaged components, the distances between nodes, or the probability of restoration. However, no single recovery strategy exists that is deemed to effectively recover all types of failures. Most recovery strategies are either cost-effective but time-consuming or time-efficient but expensive. To consider this discrepancy and simultaneously ensure long-term recovery sustainability, a hybrid recovery strategy with switching points is developed in this paper. Given a set of recovery strategies, the most effective combination of strategies will be selected based on the progression of the recovery stage and the overall cost. The procedure for finding the switching point between different recovery strategies within the feasible region is also explained. A case study of an extensive infrastructure networked system is presented to demonstrate the feasibility and effectiveness of the proposed hybrid recovery strategy. The proposed approach can serve as a useful decision-making tool during recovery planning in the presence of limited time, budget, and resources while aiming to achieve maximum system resilience.

Road infrastructure is engineered to perform through constant exposure to climatological stressors. Due to climate change in particular, expected rises in the average ambient and extreme temperatures, pavement performance might deviate from what was originally predicted at the design stage. This poses serious risks to the long-term performance of this infrastructure because the capacity of a transportation network to provide an acceptable level of service over the period that it was intended for is also seriously affected. However, identifying where potential risks are greatest and where more involved planning and prioritizing is needed is hampered by the lack of vulnerability assessment methods that appropriately integrate failure pathways and engineering decisions. In this study, a new vulnerability index is proposed—extent of pavement grade reliability loss (${\mathit{E}}_{\mathrm{PGRL}}$), which has the capacity to capture both flexible pavement performance and transportation serviceability. The index is based on two assumptions. First, flexible pavement performance is directly related to the inherent rheological properties of the asphalt binder, which can be estimated from the performance binder grade requirements for a locale. Second, the level of service of a road network can be represented by its connectivity, expressed here by a network centrality measure, the betweenness score. Two developing countries, Colombia and India, were selected to analyze the capabilities of the ${\mathit{E}}_{\mathrm{PGRL}}$. The results obtained indicate the ${\mathit{E}}_{\mathrm{PGRL}}$ captures the available performance capability of pavement infrastructure together with the importance of transportation network elements. Hence, it can be used as a tool to evaluate and quantify the vulnerability of transportation infrastructure to future climate change.

Underfunding and the continuous deterioration of pavement assets have been among the major challenges for transportation agencies in the US. These challenges motivate the use of different optimization techniques such as integer programming to perform maintenance and rehabilitation planning at the network level. However, one of integer programming’s limitations is the massive computational power requirement associated with the large number of solutions searched by the algorithm to get the optimal solution, limiting the size of the network it can optimize. In this paper, the full mathematical derivation of the binary integer programming problem is shown in terms of budget constraints only and in terms of both budget and percentage of poor assets (PPA) constraints. An alternative approach including the PPA constraint is therefore suggested along with a new practical way of reducing the search space based on the statistical bootstrap approach. The proposed approach’s adequacy is validated through a comparison of different optimization approaches applied to a small asphalt concrete (AC) pavement network. The suggested approach shows promising results because it significantly reduced the time required to allocate budget funds over the test network of 400 asphalt concrete (AC) segments.

Building information modeling (BIM) and public–private partnerships (PPPs) have been well-received for their technological and organizational innovations in construction projects. However, due to the complexity nature and project changes of BIM-enabled PPP procurement, an effective and flexible contract remains an unresolved agenda. Hence, this research aims to determine the relationship between contractual flexibility and BIM-enabled PPP project performance, particularly for its project performance during the construction phase. Data were collected through questionnaire surveys. A total of 177 valid responded questionnaires were collected from PPP practitioners under their BIM-enabled PPP projects and analyzed using a structural equation modeling (SEM) method. The results reveal both content flexibility and executing flexibility have a significant positive impact on the performance of BIM-enabled PPP projects during the construction phase, while the effect of executing flexibility is greater than the content flexibility. This research has uncovered a positive effect of contract content flexibility on contract execution flexibility for the first time, which sheds light on appropriate contract framing for governing stakeholders’ relationships in BIM-enabled PPP projects, which is an area that has not been explored in the previous literature. More importantly, it proves the important role of contract flexibility for BIM-enabled PPP projects and helps to effectively promote and use BIM and PPPs in the construction industry. A conceptual framework has also been developed to provide practical references and strategies for future implementation and contract design in BIM-enabled PPP projects, adding new knowledge of construction management both theoretically and practically.

With the increasing number of aging bridges that need maintenance and rehabilitation (M&R), it is important to plan and prioritize M&R projects proactively. This process requires mathematical models capable of predicting bridge condition over multiyear planning horizons taking into account the long-term performance effects of M&R treatments. This paper describes the development, validation, and application of Markov chain–based bridge deterioration prediction models. Bridge condition is measured in terms of the National Bridge Inventory (NBI) deck rating, superstructure rating, substructure rating, and culvert rating. The models consider explanatory variables that affect bridge deterioration, including climate/environment, traffic loading, material type, and M&R type. Because data on M&R work history are often disconnected from bridge condition data, the study used a novel approach that allows for inferring past M&R type and timing based on changes in bridge condition ratings. The models were developed using condition data for 43,320 bridges across Texas extending from 2001 to 2017. The developed prediction models can be used as a tool to support bridge asset management planning in Texas. The modeling approach, however, can be used by transportation agencies across the world to develop bridge deterioration prediction models using their local empirical data.

Planning transport infrastructure development involves high levels of uncertainty due to socioeconomic, environmental, and technological changes. Methodologies currently used in transport planning often have minimal consideration for adaptiveness, leading to costly redesigns or cancellation of entire projects. Presented herein is the investigation of the applicability of dynamic adaptive policy pathways, which is a methodology predominantly used in the field of flood-risk planning, to long-term transport infrastructure planning. Specifically, the paper investigates whether this methodology could facilitate ongoing adaptation to variations in service demand and capacity. It demonstrates this by examining future demand and capacity of road and rail travel between Manchester, United Kingdom, and London using publicly available data and information sources. The study shows that dynamic adaptive policy pathways is useful for identifying periods of time of significant capacity vulnerability for the examined transport network in the coming decade. The method is demonstrated to be valuable for identifying the points in time when policy-makers will have to make decisions and for assessing the impact of transport mode switching. This can have implications of cost-saving and improved service delivery.

Increased prevalence, lower cost, and reduced environmental impacts have granted natural gas status as a bridge fuel over the past decade in place of coal-fired heat and power generation across the United States. As a result, some coal facilities are retrofitting their systems to combust natural gas for heating and power. However, data and economic criteria surrounding these decisions are not widely available. Wright-Patterson Air Force Base (WPAFB) invested approximately $25 million, from 2014 to 2016, to retrofit two coal-fired steam-heat-generating plants with natural gas equipment. Economic analyses used to inform these long-term infrastructure decisions rarely consider the uncertainty of climate change. As the uncertainty of climate change projects is large, there is a distinct need to incorporate these forecasting models in large-scale infrastructure investments to facilitate robust decision-making. In this case study, we evaluate the uncertainty surrounding infrastructure decision-making through a Monte Carlo simulation. The case study evaluates the sensitivity of cost savings and payback period for the fuel switch relative to climate predictions. In this analysis, we characterize uncertainty of the retrofit as a function of its payback period length. The payback period is expected to increase by nearly 6 years ($\sim 20\%$) when factoring climate change and uncertainty into the economic analysis. This case study showcases the uncertainty surrounding large infrastructure decisions with climate change implications.

This technical note proposes the adoption of biomimicry at the system level as an innovative method to help solve the complex managerial challenges for civil infrastructure resilience. Although biomimicry has been adopted at the function and process levels to develop new materials and design for civil infrastructures, the full potential of biomimicry in creating system-level solutions has not been explored fully. This technical note discusses the rationale and provides examples of adopting system-level biomimicry as a promising way of finding novel solutions to facilitate resilience in interconnected infrastructure systems against disasters and disruptions.