Long tunnel lighting environment improvement method based on multiple-parameter intelligent control: Considering dynamic changes in luminance difference. (October 2022)

• Record Type: Journal Article
• Authors: Niu, Jia'an; Liang, Bo; He, Shiyong; Xiao, Jinghang; Qin, Can
• Is Part Of: Tunnelling and underground space technology. Volume 128(2022)
• Publication Date: 2022-10
• Subjects: Long tunnel -- Luminance difference phenomenon -- Lighting environment improvement -- Multiple influencing factors -- Intelligent control algorithm; Tunneling -- Periodicals; Underground construction -- Periodicals; Tunnels -- Periodicals; Underground areas -- Periodicals; 624.193
• DOI: 10.1016/j.tust.2022.104637
• Languages: English
• ISSNs: 0886-7798

Abstract

Graphical abstract: Highlights: The luminance difference of long tunnels in operation is mainly affected by LSCP. The relationship between tunnel visibility and road surface luminance is quantified. The established control model optimizes the actual road surface luminance effect. The proposed method achieves energy savings while ensuring the safety of long tunnels. Abstract: The difference between luminance in design versus road surface in the lighting environment of long tunnels often results in the road surface luminance not reaching safety standards. To solve this problem, this study proposes a method to improve the lighting environment of long tunnels. First, based on field tests and mathematical statistics, the main influencing factors of the luminance difference phenomenon are found to be (in descending order) the visibility level, traffic flow, interior zone luminance, and vehicle speed. Second, the relationship between tunnel visibility and road surface luminance is quantified, and the effect of luminance difference on road surface luminance is further investigated. Considering the characteristics of luminance difference, a dynamic optimization model of lighting luminance is established. Based on the typical lighting scenes of long highway tunnels, the optimization model is then integrated with two intelligent control algorithms to establish and optimize the lighting control model. Finally, based on a combination of the dynamic optimization model and intelligent control model, a method is introduced to improve the lighting environment of long tunnels, and its effectiveness is verified. The research results show that in different lighting scenes involving time periods and weather conditions, the proposed method can dynamically adjust the lighting luminance in a long tunnel to ensure that the average road surface luminance meets the safety luminance. It is shown that the safety luminance qualified rate increases by 39.58 %, 26.04 %, and 5.21 % on sunny, cloudy, and rainy days, respectively, and energy usage can be reduced by 21.59 %, 32.13 %, and 33.03 %, respectively.