Resilient Infrastructure Systems Research Articles

Enhancing System Efficiency through AI, Edge Computing, and Resource Optimization in Modern Infrastructure

This paper explores innovative strategies for enhancing system efficiency in modern infrastructure by integrating artificial intelligence (AI), edge computing, and resource optimization techniques. As the complexity of infrastructure systems increases, traditional methods often fall short in addressing the evolving demands of operational efficiency and reliability. By leveraging AI algorithms for predictive analytics and resource allocation, and utilizing edge computing for real-time data processing, organizations can significantly improve performance and responsiveness. The study examines case studies that highlight successful implementations of these technologies across various sectors, including infrastructure monitoring, and grid maintenance. Insights from this research provide a framework for practitioners to adopt these advanced methodologies, ultimately leading to more resilient and efficient infrastructure systems.

Application of intelligent self-organizing algorithms in UAV cooperative inspection of power distribution networks

In the rapidly evolving technological landscape, the advent of collaborative Unmanned Aerial Vehicle (UAV) inspections represents a revolutionary leap forward in the monitoring and maintenance of power distribution networks. This innovative approach harnesses the synergy of UAVs working together, marking a significant milestone in enhancing the reliability and efficiency of infrastructure management. Despite its promise, current research in this domain frequently grapples with challenges related to efficient coordination, data processing, and adaptive decision-making under complex and dynamic conditions. Intelligent self-organizing algorithms emerge as pivotal in addressing these gaps, offering sophisticated methods to enhance the autonomy, efficiency, and reliability of UAV collaborative inspections. In response to these challenges, we propose the MARL-SOM-GNNs network model, an innovative integration of Multi-Agent Reinforcement Learning, Self-Organizing Maps, and Graph Neural Networks, designed to optimize UAV cooperative behavior, data interpretation, and network analysis. Experimental results demonstrate that our model significantly outperforms existing approaches in terms of inspection accuracy, operational efficiency, and adaptability to environmental changes. The significance of our research lies in its potential to revolutionize the way power distribution networks are inspected and maintained, paving the way for more resilient and intelligent infrastructure systems. By leveraging the capabilities of MARL for dynamic decision-making, SOM for efficient data clustering, and GNNs for intricate network topology understanding, our model not only addresses current shortcomings in UAV collaborative inspection strategies but also sets a new benchmark for future developments in autonomous infrastructure monitoring, highlighting the crucial role of intelligent algorithms in advancing UAV technologies.

Urban Drainage Network Structure Optimization Using Emperor Penguin Optimization Algorithm

Urban drainage systems are an essential component of urban infrastructure that plays a critical role in managing stormwater and preventing flooding in urban areas. With the changing climate and urbanization, the challenges faced by urban drainage systems are becoming increasingly complex. Additionally, aging infrastructure further exacerbates the problem, creating anurban sewage line that must be made more resilient. Redundancy is just a fundamental characteristic of such a robust urban drainage network. Redundancy in urban drainage systems can help to ensure that the system continues to function during extreme weather events or emergencies, reducing the likelihood of flooding and damage. However, the exact locations where redundancy should be increased and its contribution to resilience are not well understood. In recent years, several studies have focused on developing frameworks for optimising urban drainage structures which account pipeline redundancy.One similar research presented a paradigm for constructing the ideal network layout for urban drainage infrastructure, which considers pipeline redundancy under consideration. The original architecture and structure of the urban drainage network was developed using emperor penguin optimizer algorithms and graph theory in the research. Complicated system modelling was done to find extra water pathways or redundancy which might well be implemented to boost resistance. The suggested approach has been utilised to the test region in Dongying City, Shandong Province, China, and its findings revealed even under rainfall above the design specification, the entire overflow capacity of such urban drainage network including pipeline redundancy significantly decreased about 20-30%, compared to the network without pipeline redundancies. The interest in creating optimization algorithms had also increased recently that can be used to design and manage urban infrastructure systems. One such algorithm is the Emperor Penguin Optimization (EPO) algorithm. EPO was a recently developed swarm-based optimization An algorithm which simulates Emperor penguins behaviour in their search for food in Antarctica. The algorithm has shown promising results in solving complex optimization problems in different fields, including engineering, computer science, and management. The EPO algorithm's key features include an emperor search strategy, local search, and randomization, enabling that to efficiently and successfully examine the search process. The algorithm's emperor penguin search strategy enables it to dynamically adjust the search parameters based on the problem's characteristics and progress. The local search feature allows it to escape local optima and explore the search space further. Finally, the randomization feature adds stochasticity to the search process, helping to ensure that the algorithm can avoid getting stuck in a sub-optimal solution. In this article, we aim to explore the potential of EPO as a tool for optimizing Urban drainage solutions which take into account pipeline redundancy. We will start by reviewing the existing Literature upon that optimization in urban drainage facilities, particularly the application of particle swarm optimization, genetic algorithms, and ant colony enhancement. The EPO algorithm will next be described in full including its working principle, key features, and the steps involved in applying it to an optimization problem. Finally, we will present a case study that applies the EPO technique was developed to optimise the network model of such an urban drainage infrastructure taking into account pipeline redundancies, and compare the results with other optimization algorithms. Through this, we aim to demonstrate the potential of EPO as a powerful tool for designing and managing urban infrastructure systems, particularly for enhancing the resilience of urban drainage systems. The study will provide valuable insights into the optimal design of urban drainage technologies which take into account pipeline redundancy, helping policymakers and urban planners make informed decisions about improvingthe adaptability of urban drainage networks. The findings can contribute to the development of sustainable and resilient urban infrastructure systems, which are essential for ensuring the well-being and prosperity of urban residents.

Securing the IoT-enabled smart healthcare system: A PUF-based resource-efficient authentication mechanism

As the Internet of Things (IoT) continues its rapid expansion, cloud computing has become integral to various smart healthcare applications. However, the proliferation of digital health services raises significant concerns regarding security and data privacy, making the protection of sensitive medical information paramount. To effectively tackle these challenges, it is crucial to establish resilient network infrastructure and data storage systems capable of defending against malicious entities and permitting access exclusively to authorized users. This requires the deployment of a robust authentication mechanism, wherein medical IoT devices, users (such as doctors or nurses), and servers undergo registration with a trusted authority. The process entails users retrieving data from the cloud server, while IoT devices collect patient data. Before granting access to data retrieval or storage, the cloud server verifies the authenticity of both the user and the IoT device, ensuring secure and authorized interactions within the system. With millions of interconnected smart medical IoT devices autonomously gathering and analyzing vital patient data, the importance of robust security measures becomes increasingly evident. Standard security protocols are fundamental in fortifying smart healthcare applications against potential threats. To confront these issues, this paper introduces a secure and resource-efficient cloud-enabled authentication mechanism. Through empirical analysis, it is demonstrated that our authentication mechanism effectively reduces computational and communication overheads, thereby improving overall system efficiency. Furthermore, both informal and formal analyses affirm the mechanism's resilience against potential cyberattacks, highlighting its effectiveness in safeguarding smart healthcare applications.

The contribution of data-driven geotechnical assessments to infrastructure development and national economy

In the evolving landscape of geotechnical engineering, the advent of data-driven assessments heralds a paradigm shift, promising to redefine the contours of infrastructure development. This paper embarks on an intellectual quest to elucidate the evolution, significance, and transformative impact of these assessments, weaving through the complex interplay between technological advancements and traditional methodologies. Anchored in a robust methodological framework, the study employs a thematic analysis of peer-reviewed literature, aiming to distill the essence of data-driven approaches within the realm of geotechnical engineering and their consequential role in sculpting resilient, sustainable infrastructure. The scope of this scholarly endeavor spans an examination of the economic implications, challenges, and future directions of geotechnical assessments, culminating in strategic recommendations poised to navigate the labyrinth of implementation hurdles. Key findings illuminate the enhanced quality, safety, and economic efficiency engendered by these modern methodologies, juxtaposed against the backdrop of traditional practices. The study's conclusions advocate for a harmonious integration of green technologies, stakeholder engagement, and policy reformulation, underscoring the imperative for a concerted effort towards sustainable infrastructure development. In essence, this paper not only achieves its aim of delineating the pivotal role of data-driven geotechnical assessments but also charts a course towards a future where infrastructure development is synonymous with innovation, sustainability, and economic viability. Recommendations call for an increased infusion of green investments and the fostering of collaborative frameworks, heralding a new dawn in the quest for resilient infrastructure systems.

Frequency and power shaving controller for grid-connected vanadium redox flow batteries for improved energy storage systems

In this research, the performance of vanadium redox flow batteries (VRFBs) in grid-connected energy storage systems centering on frequency and power sharing using voltage source inverters was evaluated. VRFBs are increasingly promising due to their scalability and long lifespan. We explore the impact of voltage source inverters on the frequency of the power supply when there is a change in load and power sharing between the grid and VRFB through MATLAB simulations. The test system demonstrates a storage system at an 11-kV substation and considers a load profile commencing from residential and commercial consumers. The outcome of this study reveals that VRFBs provide the required power to maintain load demand. Due to fast response time, VRFBs also regulate the frequency efficiently during the change in load demand and maintaining current total harmonic distortions (THDs). This research confirms the design and operation of VRFB-based energy storage systems, contributing to resilient grid infrastructure and reliable power distribution systems.

A Pioneering Integration of Structural Health Assessments and Dynamic Analyses: Bridge Pier Responses to the Impact of Floating Objects during Extreme Floods

This study presents a transformative dynamic amplification factor for assessing the resilience of over-river bridges, informed by the real-world conditions of flood events. Through advanced finite element analysis, we unveil how the interplay between mass and velocity of floating objects significantly influences bridge pier responses, challenging conventional assessment methods. Our findings reveal potential inadequacies in current design standards, such as AASHTO and AS5100, and introduces a dynamic multiplier that enhances structural health assessment algorithms. The core contribution of this research is a data-driven analysis approach, which is critical for the proactive maintenance and risk assessment of bridge infrastructures in areas prone to flooding. By redefining the parameters for damage-level identification, our work advocates for a shift towards more resilient infrastructure systems in the face of global climate change.

Empowering Engineers by Leveraging AI in Structural Engineering and Monitoring

Structural engineers play a critical role in providing safe, durable, and sustainable infrastructure to connect our communities and drive our economy. The recent explosion in opportunities to integrate artificial intelligence (AI) into the daily activities of structural engineers offers potential to reduce the workload on the human engineer, giving them more time to focus on creative tasks that require technical expertise and experience. Leveraging AI to support the human engineer has been the focus of several of recent innovative studies conducted by the Intelligent Infrastructure Systems Lab (IISL) at Purdue University. This work has addressed several fundamental challenges related to inefficiencies in the collection, interpretation, analysis, and automated use of large volumes of data, breaking several past barriers to the use of AI methods toward the practice of civil engineering. This paper explores how these innovations are enabling human engineers to harness AI to automate tasks related to extracting information and content from images, providing context to images, and fusing data to support decision- or policy-making. The implications of this integration extend beyond operational efficiencies, offering a pathway to smarter and more resilient infrastructure systems.

Editorial: Special Issue on Recycled Materials for Sustainable Geotechnical Construction

Infrastructure serves a vital role in resolving some of the major challenges faced by societies: supporting economic growth, meeting basic needs of citizens, and facilitating mobility and social interaction. Establishing a long-term development and maintenance plan is a critical priority for many nations. This motivates the design and construction of more resilient and sustainable infrastructure systems that are also able to overcome emerging challenges, particularly given the extreme weather conditions that have recently led to several large-scale failures on these systems. Collectively, these investments require the use of large volumes of earthen materials in construction. This has led to the rapid depletion of natural aggregate materials. Recycled materials have been used in a variety of construction applications, for example, roadways (e.g., embankment, base, subbase, asphalt, and concrete), airfields, and railways, among others. Such materials are attractive to construction and transportation industry stakeholders and taxpayers because they generate millions of dollars in savings while producing environmental benefits. Legislations are being developed/promulgated that remove barriers to large-scale beneficial use of these materials to reduce construction costs and increase sustainability. In addition, the construction industry, including the transportation systems sector, is facing ever-increasing demands to control the cost and environmental impact of waste removal and disposal. The use of recycled materials in construction would reduce energy consumption and emission of greenhouse gases, and recycling provides a means to address these demands. Recycled materials avoid energy and emissions associated with mining and processing construction materials. Energy has already been expended in the first life of recycled material. The use of recycled materials allows for the avoidance of natural materials such as sand and gravel, limestone, and oil. Often, service life can be increased by stabilization using industrial by-products as binders such as self-cementing fly ash, waste gypsum, and other ashes. Waste reuse/recycling practices can be incorporated in new construction projects, renovation/rehabilitation projects, and ongoing operations, and they can be applied to both agency and contractor activities. The evaluation of recycled materials used in construction applications has been the main focus of scientific studies over the last few decades. However, the effectiveness of waste reuse/recycling efforts in such applications still could not be implemented fully because of the lack of comprehensive reviews and evaluations of the limitations and advantages of recycled materials used in construction applications. Therefore, the collection and critical review of such information is necessary. This special issue of ASTM International’s Geotechnical Testing Journal brings together 23 papers that cover the use of a broad range of recycled materials in construction applications including steel slag, rice husk ash, ferrochrome slag, recycled concrete aggregate, recycled asphalt pavement, lignin, recycled binders, recycled plastics, and quarry fines. Moreover, the papers in this issue provide state-of-the-art information on geotechnics of recycled materials, including their material characterization, performance evaluations, and environmental suitability assessment. The field has evolved in terms of new material types and applications that must be evaluated. A variety of laboratory tests are adapted from the standards for natural earthen materials, sometimes with modifications, and there is material for the development of new testing protocols and standards. ASTM standards are particularly needed for sample preparation of recycled materials. There were not many papers on in situ testing, but an increase is expected to be seen in this area in future studies. Thus, the need continues for future investigations to increase our understanding of recycled materials that are a crucial element of sustainable construction. The editors wish to express their appreciation to all those authors who submitted their papers. Particular thanks are extended to those who contributed papers and to the reviewers of the papers and also to the publication staff in producing this special issue.

Electrical properties of ultra-high-performance concrete with various reinforcing fibers

Understanding the electrical properties of ultra-high-performance concrete (UHPC) is of paramount importance in the realm of smart concrete as it unlocks the potential for creating advanced, intelligent, and resilient infrastructure systems. This study focused on systematically assessing the electrical behaviors of UHPC with two commonly-used reinforcing conductive fillers, i.e. carbon fibers (CFs) and steel fibers (SFs). The effects of fiber type, fiber length, fiber content, and curing age on the alternating current (AC) resistivities and AC electrochemical impedance spectroscopy (AC-EIS) spectra of fiber-reinforced UHPC were investigated, and the equivalent circuit models of fiber-reinforced UHPC were established. Experimental results showed that the AC resistivities of UHPC with CFs and with SFs both exhibited a faster growth rate during the 14–28 d of curing, but subsequently decelerated after the completion of hydration, and stabilized at 90–120 d. Compared with the control sample, the addition of both CFs and SFs resulted in a reduction of the electrical resistivity of UHPC, with a more pronounced decrease observed with higher fiber content. In particular, the addition of SFs demonstrated a more significant reduction in UHPC’s AC resistivity in relative to CFs, with the addition of 4 vol.% copper-plated end-hook SFs remarkably lowering the resistivity by up to 87.5%. Furthermore, the introduction of different types of fibers caused remarkably different AC-EIS topologies of UHPC. The proposed equivalent circuit models reveal that compared to the control sample, the introduction of fibers can provide the fiber-fiber conductive paths and fiber-wrapped hydration products (Q F R F) within UHPC matrix. The role of UHPC matrix (Q 1 (R 1 W 1) in the conductive path of SFs-reinforced UHPC is weakened compared to that of CFs-reinforced UHPC as reflected by the differences in the impedance values of Nyquist plots.

Designing resilient and economically viable water distribution systems: a multi-dimensional approach

Enhancing the resilience of critical infrastructure systems requires substantial investment and entails trade-offs between environmental and economic benefits. To this aim, we propose a methodological framework that combines resilience and economic analyses and assesses the economic viability of alternative resilience designs for a Water Distribution System (WDS) and its interdependent power and transportation systems. Flow-based network models simulate the interdependent infrastructure systems and Global Resilience Analysis (GRA) quantifies three resilience metrics under various disruption scenarios. The economic analysis monetizes the three metrics and compares two resilience strategies involving the installation of remotely controlled shutoff valves. Using the Micropolis synthetic interdependent water-transportation network as an example, we demonstrate how our framework can guide infrastructure stakeholders and utility operators in measuring the value of resilience investments. Overall, our approach highlights the importance of economic analysis in designing resilient infrastructure systems.

Blast response of cantilever retaining wall: Modes of wall movement

For the past few decades, there has been an increase in awareness regarding the safety of highway bridges from blast loads. Since abutments/Retaining walls (RWs) are portions of bridges, investigating abutments/RW or RW behaviour under blast loads is important. As there have not been any studies investigating the dynamic response of retaining walls due to blast loading, an experimental study was conducted to examine the influence of blast loads on the dynamic behaviour of reinforced concrete retaining wall (RCRW) with sand as a backfill material. A shock tube was used to generate blast loads on the soil-RW model. The influence of the relative density, backfill saturation, blast load intensity, and traffic load equivalent surcharge on the blast behaviour of RCRW with sand backfill was studied. The results showed that the modes of wall movement were affected by the backfill relative density, blast load intensities, and degree of saturation. Under the same load conditions, an increase in the wall movement was noticed in loose backfill, and a translation response mode was evident in this condition. A relationship between wall relative movements and mobilized earth pressure coefficients was determined. The mobilized passive resistance of the RW backfill induced by blast load was used to determine the force–displacement relationship. Acceleration time histories for RW/backfill were found for all conditions. The findings of this research will help to properly evaluate and design bridges’ abutment and to develop resilient transportation infrastructure systems.

How can a digital twin sustainably enhance integration and service quality for road authorities?

Towards the vision of cooperative, connected and automated mobility (CCAM) smart road services enabling communication between participants at the information level seem to be necessary. This paper aims at providing a framework for the integration of key concepts like operational design domains (ODD) and infrastructure-based support for automated driving (ISAD) into an umbrella digital twin as developed in the project DIGEST. A prototype concept for advanced forms of information management for a homogenous traffic situation representation is proposed. The concept of ODD Aware Traffic Management provides the ground for the digital twin, which shall support all vehicles in their decision-making. The intermediate project results contribute to the conference theme ‘Innovative Infrastructure for Europe 2030’ with the use of intelligent, resilient and cooperative infrastructure systems.

A Novel Methodology to Assess Seismic Resilience (SR) of Interconnected Infrastructures

Modern and smart cities are significantly vulnerable to natural hazard, and their functionality is based on resilient infrastructure systems. In particular, seismic resilience may be considered the ability to deliver services during and after hazard events. Therefore, it is fundamental to identify the most critical components within a system, especially when multiple infrastructure systems are interdependent. The paper aims to propose a novel methodology that consider interconnected infrastructures to assess seismic resilience that may be defined as a function that depends on time, and the different components are considered the functional dimensions. The proposed methodology may be applied for several typologies of infrastructures, specifically looking at the seismic resilience analyses related to transportation systems. A case study has been considered in order to apply the proposed formulation and to demonstrate the importance of considering interdependency in the assessment of the seismic resilience. Many stakeholders (infrastructure owners, public administrations, decision makers) may be interested in applying the methodology that could be used to study several applications.

Condition-based monitoring as a robust strategy towards sustainable and resilient multi-energy infrastructure systems

ABSTRACT A resilient energy infrastructure system is exceptionally imperative to ensure uninterrupted energy supply to support the nation's economic growth. The resilience capability in energy infrastructures can be realized through effective planning decisions and maintenance strategies by implementing the condition-based monitoring (CBM) approach. CBM minimizes the unplanned downtime of a system by monitoring the system's health status in real-time and predicting upcoming failures. Thus, the planned maintenance can be performed before failures occur. With advancements in data analytics, conventional CBM methods have been enhanced with modern artificial intelligence algorithms to improve the prediction accuracy. This paper comprehensively evaluates the importance of CBM as a robust strategy to enhance energy infrastructure resilience. The vulnerabilities of energy infrastructure and current advancements in data-driven CBM methods are detailed. Furthermore, this survey equip energy infrastructure stakeholders and practitioners with CBM knowledge in managing unforeseen disaster risks, such as power failures due to adverse weather conditions.

A Native American Perspective on Sustainable and Resilient Infrastructure in Southern California

Although the needs for sustainable urban infrastructure systems are increasingly being recognized and addressed, the same is not true for Native American communities, where existence, condition, and access to a collection of infrastructure systems is often lagging behind urban and non-native communities. To date, there has been a quite limited number of studies exploring Native American perspectives on infrastructure challenges and how to build sustainable and resilient infrastructure systems in their communities. Thus, the purpose of this study is to identify the challenges and opportunities for building sustainable infrastructure systems for Native American communities in Southern California and investigate how those communities experience, understand, and apply engineering practice in the context of their community and culture. Drawing from six group interviews, our study highlights challenges and opportunities for building sustainable and resilient infrastructure systems on tribal reservations in San Diego County, California, from the perspectives offered by Native Americans themselves. We focus on infrastructure systems fundamental to well-being: built environment, water/wastewater, telecommunications, transportation, energy, human capital, and education. Our research participants emphasized the importance of their cultures, sovereignty, and care for the welfare of their communities in innovating sustainable and resilient infrastructure. To accomplish this, a key priority should be to train native engineers, who are best positioned to understand the infrastructure needs and opportunities in their own communities.

Artificial intelligence-informed planning for the rapid response of hazard-impacted road networks

Post-hazard rapid response has emerged as a promising pathway towards resilient critical infrastructure systems (CISs). Nevertheless, it is challenging to scheme the optimal plan for those rapid responses, given the enormous search space and the hardship of assessment on the spatiotemporal status of CISs. We now present a new approach to post-shock rapid responses of road networks (RNs), based upon lookahead searches supported by machine learning. Following this approach, we examined the resilience-oriented rapid response of a real-world RN across Luchon, France, under destructive earthquake scenarios. Our results show that the introduction of one-step lookahead searches can effectively offset the lack of adaptivity due to the deficient heuristic of rapid responses. Furthermore, the performance of rapid responses following such a strategy is far surpassed, when a series of deep neural networks trained based solely on machine learning, without human interventions, are employed to replace the heuristic and guide the searches.

Current State and Future Direction for Building Resilient Water Resources and Infrastructure Systems

Planning and developing resilient socio-technical and natural systems to cope with and respond to unprecedented changes has been one of the top goals of government bodies, researchers, and practitioners worldwide. This study aims to review how resilience is defined and evaluated in water resources and infrastructure systems (hereafter water systems) and propose a framework to analyze and incorporate resilience in the system. Two questions guide the review: How is resilience defined in water systems compared to other disciplines? What are commonly used resilience measures and methods applicable to water systems? Based on the review, a resilience analysis framework has been proposed. The framework uses a system of systems approach and applies hierarchical holographic modeling to address the complexity of interdependent systems. The resilience of the systems was analyzed using three questions: resilience of what, resilience to what, and resilience for whom. Two resilience measures selected for the analysis are robustness and rapidity. The framework also includes methods for uncertainty analysis, options for resilience strategies, and multi-criteria decision analysis methods to select optimal resilience options. The review is not exhaustive due to the broader topic but aims to present necessary background information to support the proposed framework.

Quantifying disaster resilience of a community with interdependent civil infrastructure systems

Disaster resilient civil infrastructure systems are essential for disaster resilient communities. Measuring the resilience of these systems is the first step towards their improvement. This, however, is not easy: civil infrastructure systems are highly complex, operate in different ways, and are affected differently in different disasters. Adding to the complexity are the dynamic interdependencies among components of such systems, that change as the community is recovering. The Re-CoDeS framework for quantifying disaster resilience measures the lack of resilience of a system (e.g., a community) as the amount of the system’s unmet demand for a considered resource or service over the resilience assessment interval. This paper presents a novel demand/supply-based method to consider dynamic component interdependencies by extending the Re-CoDeS framework: whenever the demand of a component is not met by the currently available supply capacity of the system, that component ceases to operate and its supply capacity decreases. The proposed iRe-CoDeS framework is demonstrated on a virtual community served by three interdependent civil infrastructure systems producing five types of resources and services. Step-by-step instructions for defining a community model within the iRe-CoDeS framework are presented to facilitate the use of the proposed framework.

Sustainability transitions of urban food-energy-water-waste infrastructure: A living laboratory approach for circular economy

Urban areas often face versatile stressors (e.g., food security, congestion, energy shortage, water pollution, water scarcity, waste management, and storm and flooding), requiring better resilient and sustainable infrastructure systems. A system dynamics model (SDM), explored for the urban region of Orlando, Florida, acts as a multi-agent model for portraying material and energy flows across the food, energy, water, and waste (FEWW) sectors to account for urban sustainability transitions. The interlinkages between the FEWW sectors in the SDM are formulated with multiple layers of dependencies and interconnections of the available resources and their external climatic, environmental, and socioeconomic drivers through four case studies (scenarios). The vital components in the integrated FEWW infrastructure system include urban agriculture associated with the East End Market Urban Farm; energy from the fuel-diverse Curtis H. Stanton Energy Center; reclaimed wastewater treated by the Eastern Water Reclamation Facility, the Water Conserv II Water Reclamation Facility, and stormwater reuse; and solid waste management and biogas generation from the Orange County Landfill. The four scenarios evaluated climate change impacts, policy instruments, and land use teleconnection for waste management in the FEWW nexus, demonstrating regional synergies among these components. The use of multicriteria decision-making coupled with cost-benefit-risk tradeoff analysis supported the selection of case 4 as the most appropriate option as it provided greater renewable energy production and stormwater reuse. The SDM graphic user interface aids in the visualization of the dynamics of the FEWW nexus framework, demonstrating the specific role of renewable energy harvesting for sustainably transitioning Orlando into a circular economy.