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

作     者：Niu, Jia an Liang, Bo He, Shiyong Xiao, Jinghang Qin, Can 

作者机构：Chongqing Jiaotong Univ State Key Lab Mt Bridge & Tunnel Engn Chongqing 400074 Peoples R China Chongqing Jiaotong Univ Sch Civil Engn Chongqing 400074 Peoples R China 

出 版 物：《TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY》 (隧道与地下空间技术)

年 卷 期：2022年第128卷

核心收录：

学科分类：08[工学] 0813[工学-建筑学] 0814[工学-土木工程] 

基　　金：National Natural Science Founda- tion of China [51878107, 52108362] Project of Chongqing Talent Team [2019-9-95] China Postdoctoral Science Founda- tion [2020M683254] Research and Innovation Program for Postgraduate in Chongqing Jiaotong University [2022B0004] 

主　　题：Long tunnel Luminance difference phenomenon Lighting environment improvement Multiple influencing factors Intelligent control algorithm 

摘      要：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 sta-tistics, 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.