arXiv:2409.03167v1 Announce Type: new
Abstract: 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.
Efficient Infrastructure Management Using Data-driven Approaches: An Analysis
The management of infrastructure systems is an essential aspect of ensuring economic stability, sustainability, and public safety. However, this complex task presents various challenges, including the vast scale of systems, stochastic deterioration of components, partial observability, and resource constraints. To address these challenges, data-driven approaches, such as reinforcement learning (RL), have emerged as promising avenues for optimizing infrastructure management policies.
However, the application of RL to infrastructure management has been limited due to the lack of suitable simulation environments. Without realistic and comprehensive simulation environments, it is challenging to model and analyze infrastructure management problems effectively. This limitation hinders the development of efficient management policies.
That is where InfraLib comes in. InfraLib is a groundbreaking framework introduced by the authors of this article. It offers a comprehensive and hierarchical approach to model infrastructure systems and their deterioration realistically. By incorporating practical functionalities, such as modeling component unavailability, cyclical budgets, and catastrophic failures, InfraLib enhances the ability to simulate the complexities of real-world infrastructure systems.
One notable aspect of InfraLib is its multi-disciplinary nature. Efficient infrastructure management requires insights from various fields, including engineering, operations research, and computer science. By combining concepts from these disciplines, InfraLib provides a holistic approach to infrastructure management that goes beyond a single domain’s expertise.
Another key feature of InfraLib is its support for expert data collection, simulation-driven analysis, and visualization. These tools not only enable researchers to evaluate the performance of different management policies but also facilitate decision-making by providing intuitive visualizations of the system’s behavior. This aspect of InfraLib makes it a valuable asset for policymakers and infrastructure managers.
The article demonstrates InfraLib’s capabilities through case studies on a real-world road network and a synthetic benchmark with 100,000 components. By showcasing its ability to handle both actual and large-scale infrastructure systems, the authors reinforce InfraLib’s potential to revolutionize infrastructure management practices.
In conclusion, efficient infrastructure management is essential for economic stability, sustainability, and public safety. Data-driven approaches, such as InfraLib, offer an innovative solution to optimize management policies in the face of numerous challenges. By providing a comprehensive framework, InfraLib bridges the gap between different disciplines and empowers researchers and decision-makers to tackle infrastructure management with a holistic perspective.