Abstract

ISO 5006–1 describes specific methods for evaluating visibility on large mobile machinery. The specifications of the ISO 5006–1 have however received criticism for its inability to allow evaluation of differing types of machinery, practical applicability in field settings, and assumptions surrounding eye point spacing. Field practitioners have struggled to determine a method that retains the accuracy of the standard while being feasible for rapid completion in difficult environments. The purpose of this research was to quantify differences in masking widths obtained using variations on the standard method of measurement, as well as anthropometric variables such as operator height and seat location. A computer simulation (Jack 8.0) version of the ISO 5006–1 was completed on an industrial forklift (Toyota 25) and a larger haul truck (TORO 40D). Masking widths and percent visibility were compared to assess measurement variability. Percent visibility estimations using binocular and monocular methods varied <1% across both small and large machinery. This finding is advantageous as it provides support for use of the more pragmatic monocular method in field assessments. Additionally, variation resulting from modelling operators of differing heights was also <1%, indicating that the evaluation method can be applied without necessarily knowing operator anthropometrics. Deviation of seat position from a neutral position also produced estimates of visibility with <1% variation in both small and large machinery. Variation due to operator height and seat positioning varied less among the tested conditions than between measurement methods. Depending on the accuracy required for given applications, the observed variation in measurements may be considered acceptable or unacceptable. The most accurate and reliable measures should be used in controlled conditions, such as those involved in the initial design of machinery. However, for field practitioners the observed variability in measures may be justifiable given field measurement constraints.