A remote sensing imagery-based model for assessment of building damage induced by large-equivalent explosions

LI Heng; MA Guorui; LIU Yudi; ZHANG Haiming

doi:10.11883/bzycj-2023-0331

Volume 44 Issue 3

Mar. 2024

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Article Navigation > Explosion And Shock Waves > 2024 > 44(3): 031407

LI Heng, MA Guorui, LIU Yudi, ZHANG Haiming. A remote sensing imagery-based model for assessment of building damage induced by large-equivalent explosions[J]. Explosion And Shock Waves, 2024, 44(3): 031407. doi: 10.11883/bzycj-2023-0331

Citation:

LI Heng, MA Guorui, LIU Yudi, ZHANG Haiming. A remote sensing imagery-based model for assessment of building damage induced by large-equivalent explosions[J]. Explosion And Shock Waves, 2024, 44(3): 031407. doi: 10.11883/bzycj-2023-0331

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A remote sensing imagery-based model for assessment of building damage induced by large-equivalent explosions

doi: 10.11883/bzycj-2023-0331

State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, Wuhan 430079, Hubei, China

Received Date: 2023-09-15
Rev Recd Date: 2024-02-01

Available Online: 2024-02-04

Publish Date: 2024-03-14

Abstract

Abstract

To address challenges in the field of large-scale explosive building damage assessment, where the explosion process is too complex for high-precision numerical simulation, and relying solely on change detection from remote sensing imagery cannot capture detailed internal information and lacks the capability of predicting in advance, this paper establishes a building damage assessment model for large-scale explosive events by coupling empirical mechanics models with remote sensing image interpretation and big data analysis. The study initially constructs a damage dataset based on specific historical cases of large-scale explosions. This involves extracting building damage information (including building types and damage levels) from remote sensing imagery and supplementing damage details with additional big data sources such as collected online images, videos, and news reports to enhance the precision of the sampled data. Geographic information systems spatial analysis is employed to digitize the damage information, obtaining data on building types, damage levels, and the distance from the target building to the explosion center, forming the damage dataset. Subsequently, the empirical model parameters are refined based on the training samples from the damage dataset, creating damage assessment models applicable to different building types for large-scale explosive events. The performance of the model is then tested using validation samples from the damage dataset. Experimental results demonstrate a model fitting goodness of over 96%, accuracy on validation samples exceeding 84%, and an overall error within an acceptable range. The model, under certain accuracy requirements, can provide guidance for site selection of storage locations for chemicals and hazardous materials, emergency evacuation of people in the event of a risk of large-scale explosions, critical equipment evacuation during an emergency, resource dispatching for rescue and relief after an accident, and building damage assessment.
- large equivalent explosion,
- remote sensing imagery,
- damage assessment,
- big data analysis,
- building

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References

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