In this study, a new approach based on the FMEA method under uncertainty and the opinions of a three-person team of fire experts was used to assess the risk of ground vibration caused by fire. For this purpose, first, 14 event states were considered according to the effects of vibration on the crusher, thickener, industrial site, concentrator, key house, gas station, refinery, power plant, and explosives warehouse in the Songun copper mine. Using statistical analysis, a relationship was presented to calculate the minimum vulnerable distance caused by ground vibration. In the FMEA approach, by developing and applying qualitative classification patterns and their fuzzy equivalents, the fuzzy numbers of 14 event states and the fuzzy weights of the relative importance of risk factors were determined based on the opinions of the expert team. The fuzzy risk priority number for 14 states was calculated as a geometric mean by considering the effect of the fuzzy weights of the risk factors. The results showed that state C-4, which represented the effect of ground vibration caused by fire at point 4 on the industrial site, had the highest risk. Two cases F-9 (gas station) and G-10 (refinery) had the same risk and were close to the risk of the industrial site. The rock crusher (A-1) and the explosives warehouse (I-13) both had medium to low risk and the other parts had negligible risk. In this study, a new approach based on the FMEA method under uncertainty and the opinions of a three-person team of explosives experts was used to assess the risk of ground vibration caused by explosives. For this purpose, first, 14 event cases were considered according to the effects of vibration on the crusher, thickener, industrial site, concentrator, key house, gas station, refinery, power plant and explosives warehouse in the Songun copper mine. Using statistical analysis, a relationship was presented to calculate the minimum vulnerable distance caused by ground vibration. In the FMEA approach, by developing and applying qualitative classification patterns and their fuzzy equivalents, the fuzzy numbers of the 14 event cases and the fuzzy weights of the relative importance of the risk factors were determined based on the opinions of the expert team. The fuzzy risk priority number for 14 cases was calculated as a geometric mean by considering the effect of the fuzzy weights of the risk factors. The results showed that case C-4, which represented the effect of ground shaking caused by the fire at point 4 on the industrial site, had the highest risk. Two cases F-9 (gas station) and G-10 (refinery) had the same risk and were close to the risk of the industrial site. The stone crusher (A-1) and the explosives warehouse (I-13) both had medium to low risk and the other sections had negligible risk.