In recent years, many resource allocation models have been developed to protect critical infrastructure by maximizing system resiliency or minimizing its vulnerability to disasters or disruptions. However, these are often computationally intensive and require simplifying assumptions and approximations. In this study, we develop a robust and representative, yet tractable, model for optimizing maintenance planning of generic network-structured systems (transportation, water, power, communication). The proposed modeling framework examines models that consider both linear and nonlinear objective functions and enhances their structure through suitable manipulations. Moreover, the designed models inherently capture the network topography and the stochastic nature of disruptions and can be applied to network-structured systems where performance is assessed based on network flow efficiency and mobility. The developed models are applied to the Istanbul highway system in order to assess their relative computational effectiveness and robustness using several test cases that consider single- and multiple-treatment types, and the problems are solved on the NEOS server using different available software. The results demonstrate that our models are capable of obtaining optimal solutions within a very short time. Furthermore, the linear model is shown to yield a good approximation to the nonlinear model (it determined solutions within 0.3% of optimality, on average). Managerial insights are provided in regard to the optimal policies obtained, which generally appear to favor selecting fewer links and applying a higher quality treatment to them.

Resource allocation across multiple assets in transportation systems has gained significant attention over the past few years. In this context, a framework was proposed to help decision makers estimate the optimal resource allocation across multiple assets considering functional, structural, and environmental performance indicators. Carbon dioxide equivalent emissions were considered in the calculations, along with condition measures to evaluate the optimal budget allotment scenario that will lead to structural and functional integrity and environmental sustainability. The application of the framework was demonstrated in a case study with pavement and bridge assets. Results showed that the framework could be used as a supporting tool for decision makers and transportation agencies to estimate the optimal budget to invest on each asset. Also, the impact of different parameters on resource allocation policy was evaluated with a sensitivity analysis. Findings showed that the initial condition of assets and the priority (weights) assigned to each indicator (functional, structural, and environmental) could change the optimal resource allocation scenarios. The relative size of the assets was also found to have had a notable impact on the optimal budget share. Finally, the need for agencies to consider their own data inputs is discussed and recommendations for future research are given.

Small Island Developing States (SIDS) are acknowledged as particularly vulnerable to extreme climate events; however, the realities for transport infrastructure and bridges are still poorly studied. Assessing bridges in this context can be challenging due to data scarcity, a lack of local standards, and uncertainty due to climate change. While bridges are designed to connect transport networks, they also carry energy, water, and communication networks, making them critical cascading failure points worthy of special attention in terms of risk assessment and resilience measures. We explore what resilience actually means for the design and management of SIDS bridge infrastructure by applying a post disaster forensics and systems approach that is not reliant on complex methods or large amounts of data. To demonstrate the practicality of our approach, we apply it to the island of Dominica, which is regularly impacted by both tropical storms and hurricanes. Our results document the extreme conditions for infrastructure and nearby settlements and the complex interrelated physical processes that occur during these events. We reflect on the implications for design approaches for bridges under these conditions and detail specific recommendations on how the resilience of existing and new bridges can be enhanced through practical measures that are achievable, even within the constraints experienced by those managing bridge infrastructure in SIDS contexts. This work adds to the growing number of studies exploring forensic disaster investigation and systems thinking, but is the first to explore bridge resilience in SIDS.

In the last few years, research studies have assessed the vulnerability of roadways to disasters and extreme events. However, in many of these studies the impact of the endogenous characteristics of the roadway network (such as the condition of the roads, and existing travel on the network) on roadway system vulnerability has not been adequately investigated. This paper investigates how condition of the roads, travel demand, travel patterns, and the position of links within the network affect the dynamics of vulnerably of the roadway systems. The method is applied to the Istanbul highway system and provides a probabilistic assessment of the vulnerability of the network using travel time and efficiency. The findings show that travel time vulnerability depends on the travel pattern in the network and increases with travel demand. The results also show that efficiency vulnerability and travel time vulnerability increase nonlinearly with network deterioration. When the network deterioration is not severe (disruption probabilities are low) the network efficiency vulnerability and travel time vulnerability depend on the average disruption probability of the roads within the network. With further deterioration (higher disruption probability values), both efficiency vulnerability and travel time vulnerability depend not only on the average disruption probability of the network but also on disruption probability of each link.