An innovative artificial immune system (AIS) is proposed herein for structural health monitoring (SHM) to ensure the structural integrity and functionality. While satisfactory results were obtained by previous AIS schemes, their performance is strongly structural-parameter-value (SPV) dependent and deviations of SPVs in testing from training due to modeling errors and measurement noises significantly deteriorates the AIS' performance. This thesis presents a less SPV-dependent AIS with a three-phase architecture, including damage-existence-detection, damage-location-determination, and damage-severity-estimation, using specially designed feature vectors (FVs) based on structural modal parameters. The maximum-relative-modal-parameter-change is used to detect the damage's existence and estimate its severity, and the pattern in normalized-modal-parameter-change is used to determinate the damage's location. Comparisons between the proposed FVs and their existing counterparts were conducted for 2/3/4-degree-of-freedom structures to illustrate the superior performance and less SPV-dependence of the proposed method, particularly in determining damage location. The proposed AIS was tested on a 4-degree-of-freedom model using 440 randomly generated damage conditions with a different SPV set per condition. A success rate of 95.23% in the determination of damage's existence and its location was obtained. The trained AIS for the 4-degree-of-freedom model was further evaluated by a four-story and two-bay by two-bay prototype structure used in the benchmark problem proposed by the IASC-ASCE Structural Health Monitoring Task Group. Results have shown great potentials of the proposed approach in its real-world applications.