Efficient management of infrastructure systems is crucial for economic stability, sustainability, and public safety. However, infrastructure management is challenging due to the vast scale of systems, stochastic deterioration of components, partial observability, and resource constraints. While data-driven approaches like reinforcement learning (RL) offer a promising avenue for optimizing management policies, their application to infrastructure has been limited by the lack of suitable simulation environments. We introduce InfraLib, a comprehensive framework for modeling and analyzing infrastructure management problems. InfraLib employs a hierarchical, stochastic approach to realistically model infrastructure systems and their deterioration. It supports practical functionality such as modeling component unavailability, cyclical budgets, and catastrophic failures. To facilitate research, InfraLib provides tools for expert data collection, simulation-driven analysis, and visualization. We demonstrate InfraLib's capabilities through case studies on a real-world road network and a synthetic benchmark with 100,000 components.

Infrastructure systems play a critical role in providing essential products and services for the functioning of modern society; however, they are vulnerable to disasters and their service disruptions can cause severe societal impacts. To protect infrastructure from disasters and reduce potential impacts, great achievements have been made in modeling interdependent infrastructure systems in past decades. In recent years, scholars have gradually shifted their research focus to understanding and modeling societal impacts of disruptions considering the fact that infrastructure systems are critical because of their role in societal functioning, especially under situations of modern societies. Exploring how infrastructure disruptions impair society to enhance resilient city has become a key field of study. By comprehensively reviewing relevant studies, this paper demonstrated the definition and types of societal impact of infrastructure disruptions, and summarized the modeling approaches into four types: extended infrastructure modeling approaches, empirical approaches, agent-based approaches, and big data-driven approaches. For each approach, this paper organized relevant literature in terms of modeling ideas, advantages, and disadvantages. Furthermore, the four approaches were compared according to several criteria, including the input data, types of societal impact, and application scope. Finally, this paper illustrated the challenges and future research directions in the field.

COVID-19 has transformed how we travel, work and live. As we emerge from the pandemic, our transport, energy and internet patterns will again undergo a seismic shift, and so will the infrastructure systems that underlie them: our roads, railways, water supply, electrical grids and telecommunications. To plan and optimise these systems, operators need to forecast future usage. Forecasting energy demand and renewable energy generation, for instance, can help operators to avoid unnecessary use of fossil fuels. Artificial intelligence (AI), and more specifically machine learning (ML), can play a crucial role in making these forecasts, helping to guide the evolution of our infrastructure systems.

Natural disasters cause considerable economic damage, loss of life, and network disruptions each year. As emergency response and infrastructure systems are interdependent and interconnected, quick assessment and repair in the event of disruption is critical. School of Computational Science and Engineering (CSE) Associate Professor B. Aditya Prakash is leading a collaborative effort with researchers from Georgia Institute of Technology, University of Oklahoma, University of Iowa, and University of Virginia to determine the state of an infrastructure network during such a disruption. Prakash's group has also been collaborating closely with the Oak Ridge National Laboratory on such problems in critical infrastructure networks. However, according to Prakash, quickly determining which infrastructure components are damaged in the event of a disaster is not easily done after a disruption.

Can human ingenuity assisted by new and emerging technologies overpower Covid-19? Will faster processing of more--and more relevant--data, analyzed with the right models, yield better insights into mitigating the spread of future pandemics, designing effective treatments, and developing successful vaccines? A number of promising initiatives were announced in recent weeks aiming to enlist data, AI algorithms, supercomputers, and human expertise in the fight with our global predicament. Supercomputers and quantum computers crunching lots of data are at the core of recent initiatives to ... [ ] fight the Coronavirus The Digital Transformation Institute, a new research consortium established by C3.ai, Microsoft, a number of leading universities, and the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign (UIUC), announced its first call for proposals for "AI techniques to mitigate pandemics." In addition to a total of $5.8 million in cash awards, recipients will be provided by Microsoft and C3.ai with significant cloud computing, supercomputing, data access, and AI software resources and technical support.

Assessment of the impact of natural disasters on infrastructure systems is of importance toward four main objectives: (1) Planning for actions that eliminate or reduce the long-term risk to human life and infrastructure systems (e.g.[2]); (2) Disaster preparation or adjustment, which aims to reduce the risk of damages and injuries while enabling the capability to cope with the temporary disruption of the infrastructure systems (e.g.[3]); (3) Development of effective emergency response strategies (e.g.[4]); and (4) Post-disaster recovery planning (e.g.[5]). These four are, respectively, known as the mitigation, preparedness, response, and recovery practices. A variety of analytical [6], simulation [7-11], and optimization [12] approaches are proposed in the literature for hazard reliability analysis of infrastructure systems. A comprehensive literature review on transportation infrastructure system performance in disasters is provided in [13]. Simulation-based reliability assessment of large infrastructure systems are often computationally intractable or expensive due to the large number of network components, complex network topology, statistical dependence between component failures, and uncertainties in the hazard models. This will impose limitations on design optimization or sensitivity analysis of these systems. Alternatively, a more efficient response assessment for large infrastructure systems can be made possible by using approximate surrogates [14]. Surrogates are fast models that approximately describe the relationship between the system inputs and outputs and serve as a substitute for more expensive simulation tools. If the response evaluated by the reference expensive model is denoted by f (x), a surrgate seeks to provide a global approximate function f (x).

This paper considers the restoration of multiple interdependent infrastructures after a man-made or natural disaster. Modern infrastructures feature complex cyclic interdependencies and require a holistic restoration process. This paper presents the first scalable approach for the last-mile restoration of the joint electrical power and gas infrastructures. It builds on an earlier three-stage decomposition for restoring the power network that decouples the restoration ordering and the routing aspects. The key contributions of the paper are (1) mixed-integer programming models for finding a minimal restoration set and a restoration ordering and (2) a randomized adaptive decomposition to obtain high-quality solutions within the required time constraints. The approach is validated on a large selection of benchmarks based on the United States infrastructures and state-of-the-art weather and fragility simulation tools. The results show significant improvements over current field practices.