Work

Formulating infrastructure restoration as a geospatial multi-project scheduling problem using agent-based simulation

Year: 2025

Type: article

Abstract: Urban flooding increasingly disrupts transportation networks, requiring efficient coordination of repair operations. This paper addresses how to optimize post-flood road restoration scheduling as a scattered repetitive project in a sophisticated, real-time decision-making environment. The proposed framework integrates agent-based modeling with deep learning, where autonomous repair crews dynamically prioritize tasks based on real-time accessibility predictions from a neural network proxy model. The Beijing case study demonstrated that the accessibility-driven strategy significantly improved recovery of network functionality compared to nearest-first and random approaches, particularly during critical early restoration phases. This improvement matters for emergency managers and infrastructure operators who must rapidly restore community access to vital facilities such as hospitals after disasters. Future research can extend this framework to other hazards and infrastructure systems, incorporating advanced uncertainty quantification, climate-informed risk assessments, and adaptive decision-making mechanisms for enhanced disaster planning. • Post-flood restoration is formulated as geospatial multi-project scheduling prioritizing accessibility. • Agent-based modeling with deep learning enables rapid, scalable restoration of large-scale urban networks. • Interpretable Gantt charts and crew trajectories provide practical decision support tools for emergency managers. • Comparative analysis of restoration strategies offer actionable insights for emergency response.

Source: Automation in Construction

DOI: 10.1016/j.autcon.2025.106595

Authors: Sihan Cao, Wenying Ji, Dongping Fang, Zaishang Li

Institutions: Tsinghua University, George Mason University, University College London

Language: English

Topic: Infrastructure Resilience and Vulnerability Analysis

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: hybrid

Estimated APC paid: $5,490