Durham School of Architectural Engineering and Construction


Date of this Version



A DISSERTATION Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Doctor of Philosophy, Major: Engineering (Architectural Engineering), Under the Supervision of Professor Clarence E. Waters. Lincoln, Nebraska: December, 2015

Copyright (c) 2015 Yulia Tyukhova


The overarching goal of this research was to examine humans’ subjective and physiological responses to small, high luminance light sources in outdoor nighttime environments. Currently, discomfort glare is rarely calculated in lighting practice (Remaking Cities Institute (RCI) 2011), partly, because it is not known which metric predicts glare most accurately in the given application.

This dissertation describes a parametric experiment evaluating the effects of three glare source luminances (20,000; 205,000; 750,000 cd/m2), two source positions (0°, 10°), two source sizes (10-5, 10-4 sr), and three background luminances (0.03; 0.3; 1 cd/m2) on the subjective measure of perceived glare (a seven-point rating scale) and two objective measures (relative pupil size (RPS) and electromyographic (EMG) recordings of the muscles around the eyes). Subjective responses and predictions by four metrics (the outdoor sports and area lighting metric (CIE 112-1994), the motor vehicle lighting metric (Schmidt-Clausen and Bindels 1974), a combination of two metrics by Bullough et al. (2008, 2011), and the Unified Glare Rating (UGR) small source extension (CIE 146, 147-2002)) were correlated to determine which metric predicts discomfort glare best in the tested ranges. Fifty-six participants were tested at Musco Sports Lighting in an apparatus constructed specifically for this experiment and fully controlled through custom software.

Repeated-measures Analysis of Variance was applied to subjective and RPS data; one of the results showed that when background luminance decreases, the RPS increases (F = 390.94, df = 2, p < 0.0001). The EMG data were not analyzed due to problems with data acquisition that resulted in partial data incompleteness, however, insights gained are discussed. The correlation analysis showed that the UGR small source extension correlated best with subjective responses (r = 0.879, p < 0.0001).

Advisor: Clarence E. Waters