To ensure bridge safety and functionality under in-service conditions, detecting local abnormalities of a long-span bridge at the early stage is always a desirable but challenging task. Stress influence lines (SIL) or its derivatives are recognized as very promising indices for damage detection. Compared with bridge global responses (such as displacement and acceleration), stress/strain can be more conveniently measured and is often more sensitive to local damages. This paper explores a novel damage localization approach by synthesizing SIL measurements from multiple locations, in which Dempster-Shafer data fusion technique is utilized. Compared with the measurement from a single sensor, more reliable damage-related information with the improved sensitivity and capability in damage localization can be obtained by synthesizing the measured SILs from a number of sensors. The effectiveness of the proposed method is validated through a numerical case study of the Tsing Ma Suspension Bridge. Different hypothetical scenarios, including single-damage case, double-damage, and no-damage cases, are considered in the validation. The comparison with the damage detection results using single-sensor data clearly indicates that the data fusion technique effectively enhance the consistency in the information (e.g., damage-induced structural change) and minimize non-consistent information (e.g. "noise" effect) from multiple sensors installed close to damage. The increasing number of sensors benefits the damage detection results. Excellent damage detection accuracy can be achieved, if different types of bridge components are properly selected for the monitoring. Therefore, it is promising to use the proposed approach in this study in the damage localization of real-world long-span bridges. Parametric studies are conducted to examine the effects of parameter selections and noise levels in this approach
