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UBC Theses and Dissertations

Unexpected events while manually falling trees DeMille, Gregory John


To date, the forest industry has relied on incident data in the form of fatality and serious injury statistics to improve safety in manual tree falling. These data are limited in their ability to improve falling safety. Thus, it is necessary to record another class of incident data to help gain insight into the difficulty (or danger) of falling trees. This study used a system of conditions (management requiring conditions) that are reported by the faller before a tree was felled and an assessment by the faller of whether the tree deviated from the intended plan (unexpected event) to determine if the frequency of unexpected events was correlated with site specific factors or the frequency of management requiring conditions encountered. In total, 1292 falling observations were collected during 86 one-hour observation periods. In this study it was found that there were significant differences between fallers in the frequency of management requiring conditions reported; however, other than the presence of an adjacent standing tree with falling cuts present (cut-up tree), the management requiring conditions were not good predictors of whether an unexpected event would occur. The likelihood of an unexpected event occurring was found to be correlated with terrain type, ground slope, stump diameter, and the presence of a cut-up tree. Overall, 6.9 % of the falling observations had an unexpected event occur. Of particular note, 1.2% of the observations had an unexpected event occur with a severity code 2 or 3, which indicates it was more than normal variation in falling. Given the uncertainty that a faller is exposed to when cutting into a tree, a major focus on faller safety must consider how to help the faller to be mentally and physically ready to adapt to changing conditions while working on a tree. The results indicating that all fallers who participated in this study had unexpected events, and that management requiring conditions were not good predictors of unexpected events, demonstrates that data collected at the harvest planning phase (often years before falling) will not be very effective at predicting unexpected events during falling.

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