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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.11709 |
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| _version_ | 1866911588922949632 |
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| author | Ma, Wanli Selvakumaran, Sivasakthy Farrimond, Dain G. Dennis, Adam A. Rigby, Samuel E. |
| author_facet | Ma, Wanli Selvakumaran, Sivasakthy Farrimond, Dain G. Dennis, Adam A. Rigby, Samuel E. |
| contents | Accurate and rapid structural damage assessment (SDA) is crucial for post-disaster management, helping responders prioritise resources, plan rescues, and support recovery. Traditional field inspections, though precise, are limited by accessibility, safety risks, and time constraints, especially after large explosions. Machine learning with remote sensing has emerged as a scalable solution for rapid SDA, with Mamba-based networks achieving state-of-the-art performance. However, these methods often require extensive training and large datasets, limiting real-world applicability. Moreover, they fail to incorporate key physical characteristics of blast loading for SDA. To overcome these challenges, we propose a Mamba-based multimodal network for rapid SDA that integrates multi-scale blast-loading information with optical remote sensing images. Evaluated on the 2020 Beirut explosion, our method significantly improves performance over state-of-the-art approaches. Code is available at: https://github.com/IMPACTSquad/Blast-Mamba |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_11709 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Mamba-Based Multimodal Network for Multiscale Blast-Induced Rapid Structural Damage Assessment Ma, Wanli Selvakumaran, Sivasakthy Farrimond, Dain G. Dennis, Adam A. Rigby, Samuel E. Artificial Intelligence Accurate and rapid structural damage assessment (SDA) is crucial for post-disaster management, helping responders prioritise resources, plan rescues, and support recovery. Traditional field inspections, though precise, are limited by accessibility, safety risks, and time constraints, especially after large explosions. Machine learning with remote sensing has emerged as a scalable solution for rapid SDA, with Mamba-based networks achieving state-of-the-art performance. However, these methods often require extensive training and large datasets, limiting real-world applicability. Moreover, they fail to incorporate key physical characteristics of blast loading for SDA. To overcome these challenges, we propose a Mamba-based multimodal network for rapid SDA that integrates multi-scale blast-loading information with optical remote sensing images. Evaluated on the 2020 Beirut explosion, our method significantly improves performance over state-of-the-art approaches. Code is available at: https://github.com/IMPACTSquad/Blast-Mamba |
| title | A Mamba-Based Multimodal Network for Multiscale Blast-Induced Rapid Structural Damage Assessment |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2604.11709 |