Journal of Infrastructure Systems

Digital twin (DT) as a virtual replica of a physical asset, can enhance the design, construction, operation, and maintenance of physical infrastructure. This paper aims to review scientific articles using a bibliometric analysis approach to present an overview of research trends and explore the challenges and opportunities related to DT-enabled infrastructures. Bibliographic coupling, co-authorship, citation, and keyword occurrence analysis address the study’s research questions. This review contributes to advancing the knowledge of DT and infrastructure integration while introducing the most recent trends, dominant research topics, the most used technologies with DT-enabled infrastructures, DT application in infrastructure projects, identifying gaps, and defining future research topics. Research on DT and infrastructure integration is primarily conducted in silos, implying that ideas and initiatives should be exchanged between researchers. DT application at the macro level of urban infrastructures and their management and planning will be the direction of future studies.

Distributed solar generation (DSG) has been growing over the previous years because of its numerous advantages of being sustainable, flexible, reliable, and increasingly affordable. DSG is a broad and multidisciplinary research field because it relates to various fields in engineering, social sciences, economics, public policy, and others. Developing a holistic understanding of the state of research related to DSG can be difficult. Motivated to provide that understanding, the goal of this paper is to explore current and emerging multidisciplinary research trends associated with DSG. To achieve that, (1) a large data set of approximately 66,000 publications was collected; (2) the papers were labeled using keywords for topics including “Batteries and Storage,” “Solar,” “Complex Modeling,” “Machine Learning (ML) and Artificial Intelligence (AI),” “Resilience, Vulnerability, and Disasters,” “Policies and Incentives,” “Social Aspects,” “Economics,” “Smart Grid,” “Finance,” “Social Equity,” “Microgrid,” and “Virtual Power Plant”; and (3) the data set was analyzed using scientometric and social network analysis (SNA) in respect to publication counts, citation counts, and interconnectivity between the topics. Notable findings were analyzed to describe current and emerging trends. It was found that social equity has high citation counts contrasted by few publications, indicating a possible strong need for research. There is also rapidly growing research in ML and AI in the context of DSG during recent years. Other research topics, such as smart grids, have been attracting fewer publications. The results also highlight the need for multidisciplinary research connecting the topics. To conclude, future research is suggested to explore research needs in the areas of social aspects, social justice and equity, public policy and incentives, and ML/AI. The findings should benefit researchers and stakeholders with a holistic understanding of multidisciplinary DSG-related research and provide insight for planning new research and funding opportunities.

Abstract

Electric power systems face heightened risks from climate change, on top of existing challenges like aging infrastructure, regulatory shifts, and cybersecurity threats. This paper explores how advanced technologies, including smart grids, artificial intelligence (AI), and machine learning, (ML), enhance the resilience of power systems against climate-driven extreme weather events. Drawing insights from resilience theory, the paper presents a state-of-the-art review of the literature on power system resilience, highlighting the escalating vulnerabilities of energy systems to weather-related disruptions. Although utilities currently use technologies like automated meter reading and advanced metering infrastructure to collect vital grid performance data, the lack of strategic collaboration often impedes effective data governance and sharing, thus undermining efficient responses to climate threats. The paper underscores the significance of distributed energy resources, long-duration energy storage, microgrids, and demand-side management. It further illustrates how AI and ML optimize smart grids to support these strategies. Proactive integration of smart grids with advanced technologies could significantly reduce climate-related costs compared to non-adaptive methods. Such proactive grid resilience strategies not only climate-proof energy infrastructure against climatic changes but also herald a modern, placed-based industrial transformation.

Practical Applications

Climate change exacerbates challenges in our energy systems, from aging infrastructure and a constantly shifting regulatory environment to cybersecurity risks and diversifying energy portfolios. Addressing these issues requires strategic investment in modern infrastructure, particularly smart grids enhanced by advanced technologies like artificial intelligence (AI) and machine learning (ML). These technologies are vital for enhancing power system resilience against climate impacts. Automated systems such as automated meter infrastructure (AMI) and supervisory control and data acquisition (SCADA) provide real-time data crucial for managing extreme weather events. AI and ML contribute to predictive maintenance, preventing failures and blackouts. They also forecast grid loads during severe weather, facilitating proactive power distribution management to prevent blackouts. This comprehensive improvement in situational awareness promotes economic growth in the energy sector and supports sustainable, climate-resilient transformation. AI and ML not only improve energy distribution and efficiency but also promote conservation efforts and ensure reliable energy amidst a changing climate. Collaboration among utility managers, regulators, and governments is key, focusing on data access, verification, and adaptability. Strategies should be tailored to each utility’s unique challenges. Moreover, establishing technical standards is critical for enhancing power grid resilience against climate-induced extreme weather events.

The performance data of water environment treatment public–private partnership (PPP) projects have the characteristics of multiple data sources, diverse types, and difficulties in integration. Based on existing literature and the characteristics of water environment treatment projects, this study establishes a performance evaluation index system for water environment treatment PPP projects. To address the multitemporal performance data, the data similarity method is employed to integrate multiple time data, while an ordered weighted averaging (OWA) operator is used to integrate multiple spatial location data. For indicator weighting, the OWA operator and overweighted weighting method are applied to allocate weights. Lastly, the Dempster–Shafer (D-S) evidence theory is utilized to evaluate project performance and determine the level of project performance. This model is implemented in the Pingyu County water environment treatment PPP project, yielding an overall performance level of level II (better). Additionally, the model offers distinct performance levels for each indicator, with special purpose vehicle (SPV) company governance ${\mathrm{B}}_1$, water conservancy facilities ${\mathrm{B}}_3$, garden plants ${\mathrm{B}}_6$, and bridges ${\mathrm{B}}_7$ all achieving level III (general). Future managers can concentrate on enhancing these indicators to improve project performance. The incorporation of this approach for integrating multiple spatiotemporal data in performance evaluation enhances the reliability of results and carries substantial practical implications for advancing the sustainable development of water environment governance PPP projects.

The partnership between the public and private sectors has led to a new and innovative way of delivering infrastructure projects that is referred to as public–private partnership (PPP). There are various benefits associated with PPP delivery methods including risk sharing, access to private funding, innovation, and flexibility, among others. Despite the proved benefits, contract conflicts and disputes are very common in PPP projects. While previous research studies examined the risks and the potential causes of conflicts in PPP projects, little-to-no research efforts were directed to study and model the interconnectivities between the different causes of conflicts in PPP agreements. To this end, the aim of this paper is to fill the gap in knowledge by providing a deeper understanding of the causalities or relationships between the different factors that cause disputes in PPP projects in the United States. The authors used a comprehensive analytical approach that involved three primary steps. First, 37 PPP case studies of infrastructure and construction projects were collected and analyzed using manual content analysis. Second, social network analysis was conducted to study the interdependencies between the different causal factors leading to disputes in PPP in general and in relation to Execution, Investment and Operation, and Third-Party Claims, in particular. Third, association rule analysis was conducted to identify key associations between the different causal factors that may trigger the three different types of PPP disputes. The findings showed that the key causes of disputes in PPP projects are related to (1) legal and regulatory, (2) payment and financial, and (3) poor management. While Execution-related disputes were found to be caused by complex interactions of causal factors, dispute causation of Investment and Operation–related and Third-Party Claims–related disputes seemed to be less simplistic. As such, the outcomes of this paper highlighted the important aspects required to avoid dispute occurrence in PPP projects. Ultimately, this paper contributes to the body of knowledge by providing directions for scholars and practitioners toward the aspects and interdependencies that require optimization and/or thorough consideration to avoid dispute occurrence and subsequently ensure successful implementation of PPPs.

The increase in foreign and domestic threats mandates a serious reevaluation of existing security methodologies, standards, and vulnerability assessments. A comprehensive defense strategy with quantitative and qualitative measurements is presented on how the water sector can optimize the application and placement of physical security countermeasures to improve resilience based on known parameters in a cost effective way. This study reviews the history and original intent of these methodologies that were adopted from the atomic and nuclear segments of the energy sector. These methodologies served as a starting point for the risk assessment documents that govern water sector security. The current American National Standards Institute (ANSI) risk models used by the water sector, based on design basis threat (DBT) and risk analysis and management for critical asset protection (RAMCAP), are rooted in the traditional risk formula of threat multiplied by vulnerability multiplied by consequence. This paper concludes that due to the inability to define who the adversary is, along with their objectives, motives, and capabilities, and the lack of statistically valid datasets or available intelligence of malevolent threats, the requirements listed in these methodologies are not achievable and will remain as unknowns in water/wastewater/stormwater systems. Therefore, the risk models used for mitigating adversarial threats have fundamental errors that should be replaced by an alternate risk model capable of measuring what can be known about facility resilience to malevolent attacks. By treating risk as a vector quantity consisting of known parameters, the probability of success of a given threat can be calculated using the mathematical analysis of defense strategy and countermeasures (MADSC) methodology. Once these parameters are established, the MADSC methodology can be used to determine the degree of difficulty in compromising existing countermeasures and provide guidance for physical security improvements and budgeting based on quantitative results.

Given the abundance of condition data regularly collected for major roadways, machine learning has the potential to enhance pavement deterioration modeling. This is particularly important for recycling-based rehabilitation techniques, such as full-depth reclamation (FDR), which lack accurate models of deterioration. Previous studies have demonstrated the effectiveness of machine learning (ML) to predict pavement deterioration. However, the increased accuracy of these models often is reported using statistical metrics that pavement managers cannot easily relate to asset management decision-making. This paper quantifies the impacts that increased accuracies in deterioration modeling have on relevant metrics used in the management of pavement assets. The study analyzed the performance of full-depth-reclamation pavements and developed random forest models to estimate roughness, rutting, and fatigue cracking. These random forest models were compared with mechanistic-empirical (M-E) models tuned to the same sites to quantify differences in prediction accuracy, useful life, life-cycle costs, and long-term performance. The tuned random forest deterioration models reduced errors by 90%–97% compared with the tuned M-E models. The results suggest that M-E predicts that FDR reaches the end of service life 8 years sooner than do the random forest predictions. The long-term performance of FDR was found to be 28%–73% higher in a 10-year design life than M-E models predict. This indicates that FDR is significantly more cost-effective than is presumed by M-E predictions, and that improvements in the accuracy of FDR predictions may result in more-informed decision-making.

Abstract

The COVID-19 era has witnessed numerous successful and unsuccessful attempts to adapt or reconfigure physical, virtual, and hybrid aspects of the built environment in order to mitigate the risks of co-occuring (i.e., compound) hazards. But it has also witnessed major challenges to ensuring that the protections these reconfigurations afford are equitably distributed. Additional theoretical and empirical research is needed to inform transitions (via adaptive reconfiguration) toward short-term goals of health and well-being, as well as to guide transformations (via the establishment of stable configuration) toward longer-term goals of equitable societal function. To this end, this paper presents a framework for conceptualizing adaptation of the built environment as a series of state transitions in response to (or in anticipation of) compound hazards. It draws upon cases from recent experience in the areas of food production, shelter, and education to critique, clarify, and explicate this framework. It concludes with implications for further research on the management of transitions in the built environment under a range of hazard scenarios.

Practical Applications

The COVID-19 pandemic provided a global backdrop for the study of the capacities and vulnerabilities of many aspects of societal function, challenging conventions around the design and operation of wide classes of infrastructure to protect populations from pandemics as well as hazards such as hurricanes and earthquakes. The framework offered by this study, and its application through the associated case studies, reveals how observed adaptations of the built environment can elucidate new potentials to mitigate the risks associated with co-occuring (i.e., compound) hazards, as well as areas where our existing conventions are no longer compatible with contemporary uses of the built environment. One such convention challenged by this study is the definition of critical infrastructure in existing regulatory frameworks. The built environment transitions documented by this study suggest that contemporary notions of what infrastructure is critical and what services are essential have outstripped the traditional notions in codes and standards, demanding corresponding realignment of regulatory frameworks to ensure life-safety can still be achieved as usages evolve.

Conventional wastewater treatment plant (CWTP) construction and operation are challenging, and identifying critical factors is a foremost task in its initial stage. This study aims to evaluate the critical factors considering expert judgment-based data so that field professionals can address them on a priority basis in CWTP design, construction, and operation. Accordingly, 29 critical factors were identified from the literature and evaluated by 20 experts in Saudi Arabian CWTP projects. A fuzzy group decision-making approach was applied to assess and prioritize them, considering the design and construction phases of CWTP projects of varying types, sizes, capacities, and contracts. Among 29 factors, 14 were found to be extremely important in CWTP design, whereas eight have similar importance in CWTP construction and operation. Extremely important factors in both phases include design/operation code, institutional setup and personnel capacity, neighborhood community, and environmental rules and regulations. The importance levels of some factors are varied with project variables. Among others, not complying with standard operation procedures is the top-ranked factor for privately funded, design–bid–build contact, and medium-sized projects. The government’s political commitment is significant for the privately funded and cost-plus-profit contract projects. The variance analysis also shows that factors such as the government’s political commitment, lack of proper equipment monitoring, and distance from wastewater sources have higher uncertainties in different project contexts. Thus, managing those factors is critical with the presented fuzzy-based decision analysis considering varying project scenarios. The study further provides guidelines for the sustainable design and construction of CWTP plants by overcoming critical challenges and constraints.

Project bundling is a strategy that combines several infrastructure projects into a single contract to improve the overall performance of projects. While previous research efforts have been conducted on certain aspects of project bundling, no research particularly focused on studying the opportunities and challenges of project bundling and the associated patterns between them. To this end, this paper addresses this knowledge gap. Based on data from 30 case studies that implemented project bundling strategies in the US, various opportunities and challenges were extracted. In addition, spectral clustering was implemented to cluster the identified opportunities and challenges based on the strength of their interconnectivities. Also, association rules mining analysis was conducted to determine key patterns. The results identified a total of 27 opportunities and 27 challenges for project bundling. Furthermore, the most critical associations between the opportunities and challenges were determined within each of the obtained clusters. The outcomes also reflected that while many opportunities and challenges could individually affect the performance of bundled projects, other opportunities and challenges could also result due to a combination of factors that might not be perceived to be critical on the individual level but rather become critical when combined with other factors. This paper adds to the body of knowledge by helping project stakeholders in capitalizing on the opportunities of project bundling while also minimizing the associated challenges.