Most civil engineering infrastructures, especially bridges worldwide, are approaching the end of their designed lifespan. They are continuously deteriorating due largely to material aging, excessive loads and changing environments. Therefore, it is crucial to evaluate the performance of existing structures to prevent catastrophic events. Structural Health Monitoring (SHM) has the potential to provide a proper assessment of structural performance and to further reduce cost through early damage detection and thus replacement avoidance. SHM integrates technologies to monitor structural behaviour and with current advances in sensor technology and measurement systems, the number of bridges that are continuously monitored is increasing. The bottleneck in SHM is data interpretation and this task is even more challenging in the presence of environmental variations. The main stream of data interpretation in SHM involves physics-based modelling and validation. However, building a model can be expensive, time consuming and difficult due to the structural complexity and uncertain environments. This research focuses on model-free methodologies for continuous monitoring of civil structures under environmental variations. The work involves important aspects of SHM such as damage detection, measurement system configuration and environmental variations. For damage detection, a novel model-free methodology that combines Moving Principal Component Analysis (MPCA) and regression analyses is proposed. Such approach aims to exploit the advantages of both MPCA and regression-analysis methods. The methodology has been tested on numerical and experimental studies including a full-scale bridge application. Results of a comparative study with other model-free methodologies demonstrate the superior performance of the proposed methodology in terms of damage detectability and time to detection. This research work also compares the performance of model-free methodologies for predicting natural frequencies of a continuously monitored bridge under environmental variations. Relative importance of environmental factors and traffic loading is also evaluated. Results of the case study reveal that traffic loading and temperature variations are the most influential parameters. A structural identification strategy that takes advantage of thermal variations is developed. The strategy utilizes thermal variations as load cases to evaluate structural performance. Results exhibit the promising potential of the strategy for enhancing structural identification tasks. As for measurement system configuration, a systematic approach that involves multi-objective optimization and Multi Criteria Decision Making (MCDM) methodologies is proposed. The approach is able to accommodate model-free methodologies for damage detection and provide support for selecting the best compromise configuration. It is also applicable for situations where several model-free methods are used for data interpretation.