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Bullet ricochet mark plan-view morphology in concrete: an experimental assessment of five bullet types and two distances using machine learning

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Eren, Metin I 
Romans, Jay 
Walker, Robert S 
Buchanan, Briggs 
Key, Alastair 


: Bullet ricochets are common occurrences during shooting incidents and can provide a wealth of information useful for shooting incident reconstruction. However, there have only been a small number of studies that have systematically investigated bullet ricochet impact site morphology. Here, this study reports on an experiment that examined the plan-view morphology of 297 ricochet impact sites in concrete that were produced by five different bullet types shot from two distances. This study used a random forest machine learning algorithm to classify bullet types with morphological dimensions of the ricochet mark (impact) with length and perimeter-to-area ratio emerging as the top predictor variables. The 0.22 LR leaves the most distinctive impact mark on the concrete, and overall, the classification accuracy using leave-one-out cross-validation is 62%, considerably higher than a random classification accuracy of 20%. Adding in distance to the model as a predictor increases the classification accuracy to 66%. These initial results are promising, in that they suggest that an unknown bullet type can potentially be determined, or at least probabilistically assessed, from the morphology of the ricochet impact site alone. However, the substantial amount of overlap this study documented among distinct bullet types’ ricochet mark morphologies under highly controlled conditions and with machine learning suggests that the human identification of ricochet marks in real-world shooting incident reconstructions may be on occasion, or perhaps regularly, in error. Key points: Bullet ricochet impact sites can help with shooting incident reconstruction. A random forest machine learning algorithm classified bullet type from ricochet morphology. Results suggest that unknown bullets can potentially be determined from ricochet impact site morphology. Human identification of bullet types from ricochet sites may be erroneous.


Acknowledgements: This research was in part possible thanks to a Kent State University Summer Research Appointment and Faculty Professional Improvement Leave awarded to Metin I. Eren during the summer and fall of 2023, respectively. We are grateful to the Riverside Range for providing space for this experiment to be conducted. We are also appreciative to John Serdinak, Greg Fuller, and Lisa Knallay at Old Castle APG, Great Lakes Division for describing the cement block preparation process and ingredients.


morphometrics, bullet ricochet, machine learning, concrete, shooting incident reconstruction

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Forensic Sciences Research

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Oxford University Press