# An Integrated Two-Stage Deep-Learning Tool for Rapid Post-Hurricane Damage Identification and Repair Scheduling

arXiv: https://arxiv.org/abs/2606.29117 · HTML: https://mlsanity.com/paper/2606.29117/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 15 references that are in the map.
- First posted on arXiv 28 Jun 2026.

## Builds on (most cited first, 5 of 5)

- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Deep Sets](https://mlsanity.com/paper/1703.06114/) (2017)
- [Learning Optimal Crew Dispatch for Grid Restoration Following an Earthquake](https://mlsanity.com/paper/2509.04308/) (2025)
- [Event-Driven Deep RL Dispatcher for Post-Storm Distribution System Restoration](https://mlsanity.com/paper/2601.10044/) (2026)
- [Repair and Resource Scheduling in Unbalanced Distribution Systems using Neighborhood Search](https://mlsanity.com/paper/1808.10548/) (2018)

## Built on by (most cited first, 2 of 2)

- [A Graph Neural Network--Guided Genetic Algorithm for Physical Internet Supply Chain Optimization under Cost Uncertainty](https://mlsanity.com/paper/2608.10245/) (2026)
- [Dynamic Multi-Depot Vehicle Routing with Online Requests: Event-Driven Transformer--DRL and Rolling-Horizon Benchmarking](https://mlsanity.com/paper/2608.13799/) (2026)

## Abstract (arXiv)

Post-hurricane damage assessment and repair scheduling can require computationally intensive simulation and optimization. This paper presents an integrated two-stage deep-learning tool for rapid damaged-line identification and repair-schedule computation. An available offline synthetic dataset for the IEEE 9500-node test feeder contains 1,700 hurricane scenarios with exposure features, grid metadata, fragility parameters, OpenDSS outputs, damaged-line labels, and Adaptive Large Neighborhood Search reference schedules. Stage 1 benchmarks MLP, ResMLP, and GraphSAGE, while Stage 2 compares MLP, DeepSets, and Set Transformer. The selected ResMLP-Set Transformer pipeline propagates Stage 1 errors into Stage 2 and achieves a damaged-job F1-score of 0.920, pairwise order agreement of 0.854, and start- and end-time mean absolute errors of 4.349 min and 4.486 min, respectively. The tool provides rapid initial repair-log decision support for new hurricane cases.