A probability-based risk assessment of secondary fragments ejected from the reinforced concrete wall under close-in explosions

Improvised explosive device (IED) poses a significant threat due to its simplicity of fabrication and deployment. For reinforced concrete (RC) walls, the close-in IED explosions could cause severe structural damage, and the resultant high-velocity secondary fragments endanger people and facilities in the surrounding area. Existing safety standards regarding safety distance are not applicable for close-in IED explosions. This study proposes a probability-based risk assessment method to estimate human casualty risks from secondary fragment ejection caused by close-in IED explosions. This method leverages data from a machine-learning-based Fragment Graph Network (FGN) developed in the authors’ previous research, simulating secondary fragments more efficiently than traditional methods. By analysing fragment distribution data and applying logistic regression analysis, safety distances to avoid human casualties corresponding to various safety probability thresholds are determined. Consequently, the proposed systematic risk assessment method for secondary fragments enables precise determination of safety distances to mitigate potential injuries in close-in IED blast scenarios. Empirical formulae are developed for fast estimation of safety distances required for different blast scenarios and wall configurations.