Natural hazards cause enormous damage to infrastructure systems that societies depend on every day. When these systems fail, the consequences can ripple through economies and communities. Researchers are increasingly turning to artificial intelligence (AI) to help governments and emergency managers restore critical infrastructure faster and more effectively.
A new review published in Civil Engineering Sciences examines how machine learning (ML) methods are being applied to support infrastructure recovery following natural hazards. The study was conducted by researchers from University College London and Tsinghua University and systematically analyzed 57 academic studies to understand the current state of the field and its future potential.
Infrastructure systems, such as power grids, water supply networks, telecommunications, and transportation, form the backbone of modern society. When disasters strike, damage to these systems can disrupt essential services for weeks or months. According to the researchers, faster and more efficient recovery is therefore a key component of infrastructure resilience.
The review found that ML techniques are increasingly being used in three main ways.
First, ML can characterize recovery processes by analyzing data sources such as satellite imagery or social media posts to understand how infrastructure systems recover over time after a disaster.
Second, ML models can predict recovery outcomes, such as estimating how long it will take to repair damaged infrastructure or forecasting how traffic flows will change after hurricanes or earthquakes.
Third, and most importantly, ML can optimize recovery strategies. Reinforcement learning algorithms – one of the most widely used approaches identified in the review – can simulate different repair strategies and identify the most effective sequence of actions for restoring infrastructure systems.
The researchers found that reinforcement learning accounted for nearly half of the studies reviewed, reflecting its strong potential for complex decision-making problems. Power systems and transportation networks were the most commonly studied infrastructure sectors.
"Recovery planning often involves multiple interacting systems and stakeholders," said lead author Zaishang Li. "Machine learning can help decision-makers evaluate many possible strategies and identify solutions that restore services more quickly and efficiently."
Despite promising progress, the study also highlights several challenges that limit the widespread use of machine learning in disaster recovery.
One major issue is data scarcity. High-quality datasets describing real infrastructure damage and recovery processes are rare, partly because disasters are infrequent and partly because infrastructure data are often sensitive or difficult to collect. As a result, many existing studies rely on simulated data rather than real-world observations.
Another challenge is ensuring that ML models can generalize across different infrastructure systems and disaster scenarios. Infrastructure networks vary widely in design, and recovery decisions also depend on human factors such as resource availability and policy priorities.
To address these challenges, the researchers recommend developing shared open datasets, combining multiple data sources, and applying explainable AI techniques to better understand the factors that drive recovery outcomes.
Looking ahead, the team believes that advanced approaches such as multi-agent reinforcement learning could enable coordinated recovery strategies across interdependent infrastructure systems, for example, restoring electricity, water, and transportation networks simultaneously.
"Ultimately, our goal is to support smarter and more resilient infrastructure systems," Li said. "By integrating machine learning with disaster recovery planning, cities may be able to restore essential services faster and reduce the societal impacts of future natural hazards."
Other contributors include Dina D'Ayala from University College London, and Nan Li, Jiaxu Huang, and Mukun Liu from Tsinghua University. Dina D'Ayala is also affiliated with the UNESCO Chair in Disaster Risk Reduction and Resilience Engineering.
This research was supported by the National Natural Science Foundation of China (grant nos. 72304162, 72242107, 12411530115, and W2521182), the Beijing Natural Science Foundation (grant no. JQ25028), and UK Research and Innovation (UKRI) under the UK government's Horizon Europe funding guarantee (grant no. EP/Z001978/1).
Civil Engineering Sciences
Literature review
Not applicable
Machine Learning for Infrastructure Recovery from Natural Hazards: A Review
19-Feb-2026
N.L. is the Associate Editor and D.D. is an Editorial Board member of this journal, but they were not involved in the peer review or decision of this article. The authors declare that they have no competing interests.