A Mamba-Based Multimodal Network for Multiscale Blast-Induced Rapid Structural Damage Assessment
Summary: arXiv:2604.11709v1 Announce Type: new
Abstract: 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
Introduction
The need for effective structural damage assessment in the aftermath of disasters has never been more urgent. As natural and man-made disasters increase in frequency and intensity, the demand for rapid and accurate assessment methods is critical for effective response and recovery efforts. Traditional methods, while reliable, are often hampered by various constraints, including accessibility to damaged sites, safety concerns for inspection teams, and the time-consuming nature of manual assessments.
The Role of Machine Learning in Structural Damage Assessment
Machine learning techniques, particularly those utilizing remote sensing technologies, have emerged as valuable tools for addressing these challenges. These innovative approaches allow for the rapid acquisition and analysis of data, enabling responders to assess damage without the need for physical presence at the site. Among these techniques, Mamba-based networks have shown promise, achieving impressive results in terms of speed and accuracy.
Challenges in Current Approaches
Despite their advantages, existing machine learning methods face significant hurdles. Key challenges include:
- Dependence on large datasets for training, which may not always be available, particularly in unique or unprecedented disaster scenarios.
- Failure to effectively incorporate the physical characteristics and dynamics of blast loading, which are crucial for accurate damage assessment.
- Limited real-world applicability due to the complexity of integrating various data sources.
Proposed Solution: Mamba-Based Multimodal Network
To address these challenges, we introduce a novel Mamba-based multimodal network designed for rapid structural damage assessment. This approach integrates:
- Multi-scale blast-loading information to enhance the accuracy of damage predictions.
- Optical remote sensing images, allowing for a comprehensive analysis of the affected areas.
Our method has been rigorously evaluated using data from the 2020 Beirut explosion, demonstrating significant improvements over existing state-of-the-art approaches. By combining physical insights with advanced machine learning techniques, we believe this network can revolutionize the way structural damage is assessed in the wake of explosions and other disasters.
Conclusion
The ongoing evolution of machine learning technologies holds great promise for enhancing disaster response efforts. The introduction of our Mamba-based multimodal network marks a significant step forward in the field of structural damage assessment, providing a scalable and effective solution to the pressing challenges faced by emergency responders. As the field continues to advance, further research and development will be essential in refining these methods and expanding their applicability in real-world scenarios.