The feasibility of a parameter identification method based on symbolic time series analysis (STSA) and the adaptive immune clonal selection algorithm (AICSA) is studied. Data symbolization by using STSA alleviates the effects of harmful noise in raw acceleration data. The effect of the parameters in STSA is theoretically evaluated and numerically verified. AICSA is employed to minimize the error between the state sequence histogram (SSH) that is transformed from raw acceleration data by STSA. The proposed methodology is evaluated by comparing it with AICSA using raw acceleration data. AICSA combining STSA is proved to be a powerful tool for identifying unknown parameters of structural systems even when the data is contaminated with relatively large amounts of noise.

The parameter identification method based on symbolic time series analysis (STSA) and adaptive immune clonal selection algorithm (AICSA) was experimentally verified using a 5-story experimental model structure. In the experimental verification, both single and multiple damage scenarios were studied. A 5-story structure was initially healthy with all original columns intact. The single-damage case, the double-damage or the triple-damage case was simulated by replacing the columns of one, two or three different floors, respectively. The experimental results have shown that the parameter identification method based on STSA and AICSA can successfully identify structure parameters only utilizing measured acceleration information for various damage scenarios under different excitation conditions. The proposed approach was shown promising for application of SHM on buildings.