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The purpose of this paper is to explore the pathway toward operationalizing resilience in management of transportation infrastructure.

The research approach includes a comprehensive survey of the State Transportation Agencies (STA) in the USA. The information collected from the survey is analyzed using statistical analysis to explore the determinants of operationalizing resilience in transportation infrastructure management.

The results reveal that the current practices of STA need improvement in terms of pre-disaster vulnerability and exposure analysis as well as pre-disaster retrofit and betterment efforts. A pathway toward this end is identified with the major components being: funding availability, integration of efforts across different units, use of risk and vulnerability assessment approaches, and use of resilience indices.

The pathway, along with the other findings, enhances the understanding of the status quo, drivers, and barriers toward operationalizing resilience in transportation infrastructure management. Such an understanding is critical for infrastructure agencies to better adapt and enhance the resilience of their assets in response to various stressors such as the impacts of climate change as well as natural disasters.

The study presented in this paper is the first of its kind to identify the pathway toward operationalizing resilience in transportation infrastructure management.

The purpose of this paper is to present a methodology for estimating the “price,” or the not-to-loss value, of individual highway assets, which reflects not only the assets’ capital value but also economic productivity, by adopting a productivity-based asset valuation framework. The price tags can be used in prioritizing highway assets in support of transportation asset management processes.

The methodology adopts the utility theory to consider multiple performance measures reflecting the economic productivity generated by the assets, as well as their capital value. Key performance measures are first selected, and their values are retrieved from highway asset management databases. Next, the utility functions representing decision makers’ preferences convert the performance measures into utility values, which adjust the replacement cost (RC) of each highway asset to estimate price tags. To demonstrate its applicability, case studies were conducted for the highway networks of Texas and Washington State in the USA.

The methodology yielded price tags that better reflect the importance of highways’ roles in the economy in comparison to methods where only RCs are used. Furthermore, it was proven to be flexible enough to accommodate local conditions such as varying data availability.

The research provides a practical and reasonable way to prioritize critical highway assets in purport of maintenance and rehabilitation resource allocations, based on their economic productivity as well as physical condition and historical cost information, enhancing the overall efficiency and effectiveness of highway asset management.

Transportation infrastructure asset management has long been an active but challenging problem for agencies, which urges to maintain a good state of their assets but faces budgetary limitations. Managing a network of transportation infrastructure assets, especially when the number is large, is a multifaceted challenge. This paper aims to develop a life-cycle cost analysis (LCCA) based transportation infrastructure asset management analytical framework to study the impacts of a few key parameters/factors on deterioration and life-cycle cost. Using the bridge as an example infrastructure type, the framework incorporates an optimization model for optimizing maintenance, repair, rehabilitation (MR&R) and replacement decisions in a finite planning horizon.

The analytical framework is further developed through a series of model variations, scenario and sensitivity analysis, simulation processes and numerical experiments to show the impacts of various parameters/factors and draw managerial insights. One notable analysis is to explicitly model the epistemic uncertainties of infrastructure deterioration models, which have been overlooked in previous research. The proposed methodology can be adapted to different types of assets for solving general asset management and capital planning problems.

The experiments and case studies revealed several findings. First, the authors showed the importance of the deterioration model parameter (i.e. Markov transition probability). Inaccurate information of p will lead to suboptimal solutions and results in excessive total cost. Second, both agency cost and user cost of a single facility will have significant impacts on the system cost and correlation between them also influences the system cost. Third, the optimal budget can be found and the system cost is tolerant to budge variations within a certain range. Four, the model minimizes the total cost by optimizing the allocation of funds to bridges weighing the trade-off between user and agency costs.

On the path forward to develop the next generation of bridge management systems methodologies, the authors make an exploration of incorporating the epistemic uncertainties of the stochastic deterioration models into bridge MR&R capital planning and decision-making. The authors propose an optimization approach that does not only incorporate the inherent stochasticity of bridge deterioration but also considers the epistemic uncertainties and variances of the model parameters of Markovian transition probabilities due to data errors or modeling processes.

This paper discusses a multifaceted approach to developing specific and general climate resilience in a state transportation system that focuses on organizations and physical infrastructure. The paper focuses on resilience building to the dynamically evolving climate-related threats and extreme events in a transportation agency. This paper aims to enable agencies to understand better how their systems are exposed to different hazards and provide the information necessary for prioritizing their assets and systems for resilience improvement.

This paper leverages long-term climate hazard databases, spatial and statistical analyses and nonprobabilistic approaches for specific and general climate resilience improvement. Spatial and temporal variability assessments were conducted on granular historical records of exposure obtained from Spatial Hazards Events and Losses Database for the United States data set to identify emerging hot spots of exposure. These were then assessed in combination with various asset specific vulnerability parameters, presented with examples of pavements and bridges. Specific metrics were obtained for the various aspects of vulnerability in the context of a given asset to estimate the overall vulnerability. A criticality-vulnerability matrix was then developed to provide a prioritization model for transportation systems.

This paper provides insights into the evolving nature of exposure, vulnerability and risk assessments and an approach to systematically account for climate change and the uncertainties associated with it in resilience planning. The Multi-Hazards Exposure, Vulnerability and Risk Assessment tool presented in this paper conducts climate hazard exposure, vulnerability and risk analysis on pavements, bridges and culverts and can be applied by any transportation agency.

This study does not address operational aspects of the transportation system nor include future climate scenario data, but uses the historical records available at hand for resilience planning. With better climate projection data available in the future, the approach should be enhanced by leveraging scenario-based planning.

This paper is of potential value to practitioners and researchers interested in developing resilience building capabilities to manage the effects of climate-related hazards and extreme events as well as unknown threats on infrastructure and organizational performance.

This paper bridges an important gap in infrastructure resilience approaches by systematically accounting for the dynamic nature of climate change and the system level context of vulnerability beyond the physical condition of assets.

The increased need for, and maintenance of, infrastructure creates challenges for all agencies that manage infrastructure assets. To assist with these challenges, agencies implement asset management systems. The purpose of this paper is to investigate and compare the importance of barriers faced by agencies establishing transportation asset management systems in the USA and Libya to contrast a case of a developed and developing country.

A literature review identified 28 potential barriers for implementing an asset management system. Practitioners who participate in decision-making processes in each country were asked to rate the importance of each barrier in an online survey questionnaire. Descriptive statistics, Kendall Concordance W., and Mann-Whitney are used to analyze the collected data.

Through an analysis of 61 completed questionnaires, 14 barriers were identified as important by both the US and Libyan practitioners. A total of 11 additional barriers, primarily in the areas of political and regulatory obstacles, were determined to be important only for Libya. These 11 barriers provide reasonable insights into asset management systems’ barriers for developing countries.

The list of barriers identified from this research will assist decision makers to address and overcome these barriers when implementing asset management systems in their specific organizational and country conditions.

The research identified standard barriers to implementing asset management systems and identified barriers that were specific to the country context, such as political and regulatory barriers in Libya. When viewed with the asset management literature, the results show broad applicability of some asset management barriers and the need to contextualize to country context (e.g. developing countries) for other barriers.

Resource allocation is essential to infrastructure management. The purpose of this study is to develop a methodological framework for resource allocation that takes interdependencies among infrastructure systems into consideration to minimize the overall impact of infrastructure network disruptions due to extreme events.

Taking advantage of agent-based modeling techniques, the proposed methodology estimates the interdependent effects of a given infrastructure failure which are then used to optimize resource allocation such that the network-level resilience is maximized.

The findings of the study show that allocating resources with the proposed methodology, where optimal infrastructure reinforcement interventions are implemented, can improve the resilience of infrastructure networks with respect to both direct and interdependent risks of extreme events. These findings are also verified by the results of two case studies.

As the two case studies have shown, the proposed methodological framework can be applied to the resource allocation process in asset management practices.

The proposed methodology improves the resilience of the infrastructure network, which can alleviate the social and economic impact of extreme events on communities.

Capitalizing on the combination of agent-based modeling and simulation-based optimization techniques, this study fulfills a critical gap in infrastructure asset management by incorporating infrastructure interdependence and resilience concepts into the resource allocation process.