Blast loading prediction in a typical urban environment based on Bayesian deep learning
Explosion events in urban environment, such as terrorist attacks, accidental industrial explosions and missile attacks in war, can be destructive to residents and properties, causing great casualties and structural damages. Rapid and accurate methods for blast loading prediction in urban environment...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
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| Series: | Engineering Applications of Computational Fluid Mechanics |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/19942060.2024.2445765 |
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| Summary: | Explosion events in urban environment, such as terrorist attacks, accidental industrial explosions and missile attacks in war, can be destructive to residents and properties, causing great casualties and structural damages. Rapid and accurate methods for blast loading prediction in urban environment are crucial for risk mitigation and emergency response planning. Commonly used numerical simulation methods can provide accurate blast loading prediction results, but suffer from high computation time and cost. In this paper, a novel and fast method for blast loading prediction in a typical urban environment is proposed. Deep learning-based prediction model is constructed by leveraging numerical simulations of urban explosions, for which a wide range of simulation data with diversified combinations of explosion and environment variables are obtained for assuring generalizability of the prediction model. This blast loading prediction model is further enhanced with Bayesian deep learning, which equips the prediction model with the capability of uncertainty quantification of blast loading prediction without sacrificing its rapidity or accuracy. Numerical study of blast loading prediction in a typical urban environment is presented to demonstrate the proposed method. The results show that the Bayesian deep learning-based prediction model can fulfil the blast loading prediction in this typical urban environment within 2.9 millisecond, and the mean absolute percentage error of blast loading prediction is controlled below 12.9%. Compared with existing data-driven methods, the proposed method can generalize better in this typical urban environment studied in this paper, and improved prediction accuracy and uncertainty quantification capability are acquired, while maintaining comparable prediction rapidity. This research provides a new method and prospect for blast loading prediction in a typical urban environment, enabling accurate and effective safety assurance, risk mitigation for residents and properties under explosion events in urban environment. |
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| ISSN: | 1994-2060 1997-003X |