%0 Journal Article
%T A rock engineering system approach to estimation of blast induced peak particle velocity
%J International Journal of Mining and Geo-Engineering
%I University of Tehran
%Z 2345-6930
%A Adesida, Patrick A.
%D 2023
%\ 03/01/2023
%V 57
%N 1
%P 101-109
%! A rock engineering system approach to estimation of blast induced peak particle velocity
%K Attenuation risk index
%K Blasting
%K Ground vibration
%K Peak particle velocity
%K Rock Engineering System
%R 10.22059/ijmge.2022.343687.594973
%X This paper presents a novel rock engineering system (RES) based method for estimating blast-induced vibration attenuation risk index and predicting peak particle velocity (PPV). The RES approach involves three key steps, which are the identification of influencing parameters, the construction of an interaction matrix and the rating of parameters based on their influence on ground vibration. The selected parameters are the scale distance (SD), the ratio of the scale distance to stemming divided by the burden (SD/TB), the distance of the monitoring station (D), the scale distance divided by the burden (SD/B), the ratio of the scale distance to powder factor (SD/PF) and the ratio of scale distance to spacing divided by the burden (SD/SB). The results indicated that all the six parameters considered have statistically significant influences on the constructed interaction matrix system, with the SD having the highest weighty factor (21.43%) while SD/TB is the lowest (14.29%). The maximum rating of the parameters is 5, 5, 4, 5, 5, 4 for SD, D, SD/B, SD/PF, SD/SB and SD/TB, respectively. The attenuation risk index ranges from 14.29 to 63.43, and the slope of the actual measured PPV against the calculated attenuation risk index is negative. The developed RES-based model demonstrated better performance and a reliable method for ground vibrations prediction with a higher degree of accuracy, considering its higher determination coefficient (R2 = 0.96) and smaller error (RMSE = 1.08, MAD = 0.79, MAPE = 9.95) compared to multiple regression, Langefors & Kihlstrom and Hudaverdi models.
%U https://ijmge.ut.ac.ir/article_89662_89f58ea7fa0a823ce7bfbd18b01fb646.pdf