Probability-based visual comfort assessment and optimization in national fitness halls under sports behavior uncertainty

Sports behavior uncertainty is a main cause of mismatch between simulated and actual visual comfort in sports spaces, which has not been properly addressed in existing studies. This study proposes a multi-phase and multi-objective optimization approach to improve visual comfort in national fitness halls by better considering sports behavior uncertainty. Specifically, a surrogate-based multi-objective optimization workflow is developed for fast optimization of visual comfort. Then, a probabilistic behavior model is created to predict player's occupancy probability in all possible view scenes during sports, and based on which the probabilistic visual comfort is introduced to assess player's realistic visual comfort taking account of the occupancy probability. The sport of tennis in a parametric national fitness hall is selected as the case study scenario. Results indicated that the proposed response surface model outperformed artificial neural network model in predicting visual comfort, while this approach saved 35.43% of total computational time versus the traditional simulation-based multi-objective optimization approach in this case study; 296 Pareto solutions out of 1758 design alternatives were quickly identified, among which the final optimum solution indicated an approximately 200% better visual comfort against the worst one; Sports behavior uncertainty was found to affect player's visual comfort assessment in tennis sports, presenting variances on different court locations under two court layouts. This study contributes new insights of probabilistic visual comfort assessment under sports scenarios and explores the potential of court location and layout arrangement for multi-purpose usage consideration in national fitness halls.