Numerous long-span cable-supported bridges have been built throughout the world in recent years. These bridges begin to deteriorate once built and continuously accumulate damage during their long service life. The growing popularity of comprehensive structural health monitoring systems (SHMSs) in recently built long-span bridges has started a new trend of integrating SHMS and damage detection technology for real-time condition assessment of these bridges. This paper explores a novel damage detection technique based on stress influence lines (SILs) of bridge components and validates the efficacy of the technique through a case study of the Tsing Masuspension bridge. A mathematical regularization method is first introduced to identify SILs based on the in situ measurement of train information and train-induced stress responses in local bridge components. Good agreement between the identified and baseline SILs demonstrates the effectiveness of the proposed identification method. Damage indexes based on SILs are subsequently proposed and applied to hypothetical damage scenarios in which one or two critical bridge components are subjected to severe damage. The comparison suggests that the first-order difference of SIL change is an accurate indicator of the damage location. Results of this study indicate that the proposed SIL-based method offers a promising real-time technique for damage localization in long-span cable-supported bridges equipped with comprehensive SHMSs.Department of Civil and Environmental Engineerin