Arboriculture & Urban Forestry 46(6): November 2020 depending on the risk target. For people and vehicles, the method more strongly weighted likelihood (49%) over consequences (25%), but when assessing struc- tures it reversed the weightings. The distribution curve generated from the Monte Carlo simulation showed the logarithmic curve expected from the mathematics (Figure 22); the mean is 0.126, which equated to a risk value of 1:8, which was very high given the level of acceptable risk is deemed to be 1:10,000. QTRA W Version (Ellison 2005a, 2005b) QTRA W was a simple tool for calculating quick in-field QTRA risk levels that could generate differ- ent RoH values to the full QTRA assessment method. The most striking variation was that an increase of 1 rank changed the output by 852% to 900%, whereas a reduction in 1 rank only changed the output by 72% to 81%. The QTRA W produced a low R2 regression was applied (R2 value when = 0.14), hence correlation coefficients were used; as shown in Figure 23, the various inputs differed in their influence on the output values, with “Target” influencing 45% of variation, “Probability of Failure” influencing 31%, and “Size of Part” influencing 21%. Figure 23. QTRA W @Risk ranked correlation sensitivity. 421 As with the full QTRA version, the likelihood and consequences categories changed depending on the risk target. For people and vehicles, the method more strongly weighted likelihood (49%) over conse- quences (25%); whereas when assessing for struc- tures, the method reversed the weightings. The distribution curve generated from the Monte Carlo simulation showed an extreme logarithmic curve (Figure 24); with output values based in the 10-9 scale . This extreme value occurred because the wheel generated values from 1:1 to 3 × 10-12 using two 5-scaled ranges and one 6-scaled range; and these were expressed over a 150-point range. Due to scal- ing and limited categories, the QTRA W produced different outputs to the full version of QTRA, and users should be aware of the differences between the two approaches. Threats (Forbes-Laird 2006) Threats was a qualitative method with 3 assessment categories, “Likelihood of Failure,” “Target Score,” and “Impact Score.” Threats used differing ranks and scale ranges for each input, hence the significantly differing effects of the various inputs. The 1 rank change showed the large variation within each input category and between the 3 inputs, with a 1 rank increase in the “Likelihood of Failure” category mod- ifying the output value by 525%. The @Risk Monte Carlo simulation and subsequent regression produced an R2 of 0.53, hence correlations coefficients are reported. Figure 25 confirmed that the “Likelihood of Failure” input category had the greatest influence on the outputs but was the input with the greatest uncer- tainty. The “Likelihood of Failure” category accounted for some 51% of the variation, whilst “Target Score” and “Impact Score” accounted for 20% and 3%, respectively. Combining the various inputs into consequences Figure 24. QTRA W @Risk Monte Carlo distribution. and likelihood indicated a significant favouring of inputs that affect likelihood over consequences, due to the strong influence of the “Likelihood of Failure” category, with likelihood accounting for some 72% of the variation in the method and consequences a mere 3%. The Monte Carlo distribution showed that the method produced many more values at the lower end of the risk scale (Figure 26). However, whilst the Threats risk output scale ranges from 0 to 20,000, the maximum risk level was reached at an output of 4000. Based on this simulation, Threats would produce ©2020 International Society of Arboriculture (billions). The Monte Carlo mean value was 2.5 × 10-9
November 2020
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