REPAIR Approach for Social-based City Reconstruction Planning in case of natural disasters

2025-10-21

AI Summary

Following natural disasters, local governments must coordinate the reconstruction of economic, social, and physical infrastructure under tight budgetary and temporal constraints, while balancing legal compliance, political priorities, and heterogeneous stakeholder interests. This paper introduces REPAIR—a multi-objective decision-support framework integrating deep reinforcement learning (DRL) with stochastic agent-based baselines. REPAIR explicitly models infrastructure interdependencies, urban structural constraints, and multi-stakeholder utility functions to generate Pareto-optimal reconstruction portfolios. Its key innovations include embedding political feasibility into the DRL reward structure and employing multi-agent comparative analysis to jointly optimize social welfare and implementation feasibility. Evaluated on the 2009 L’Aquila earthquake case, REPAIR significantly enhances solution scientific rigor, transparency, and resource-allocation efficiency. The framework establishes a scalable computational paradigm for resilient, evidence-informed post-disaster recovery planning.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Reasoning under Uncertainty: Stochastic Optimization
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Natural disasters always have several effects on human lives. It is challenging for governments to tackle these incidents and to rebuild the economic, social and physical infrastructures and facilities with the available resources (mainly budget and time). Governments always define plans and policies according to the law and political strategies that should maximise social benefits. The severity of damage and the vast resources needed to bring life back to normality make such reconstruction a challenge. This article is the extension of our previously published work by conducting comprehensive comparative analysis by integrating additional deep learning models plus random agent which is used as a baseline. Our prior research introduced a decision support system by using the Deep Reinforcement Learning technique for the planning of post-disaster city reconstruction, maximizing the social benefit of the reconstruction process, considering available resources, meeting the needs of the broad community stakeholders (like citizens' social benefits and politicians' priorities) and keeping in consideration city's structural constraints (like dependencies among roads and buildings). The proposed approach, named post disaster REbuilding plAn ProvIdeR (REPAIR) is generic. It can determine a set of alternative plans for local administrators who select the ideal one to implement, and it can be applied to areas of any extension. We show the application of REPAIR in a real use case, i.e., to the L'Aquila reconstruction process, damaged in 2009 by a major earthquake.

Problem

• Planning city reconstruction after natural disasters with limited resources
• Maximizing social benefits while meeting stakeholder needs and priorities
• Determining optimal reconstruction sequences considering infrastructure dependencies

Innovation

• Deep Reinforcement Learning for post-disaster reconstruction
• Decision support system maximizing social benefits
• Generic approach generating alternative reconstruction plans