Abstract
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.
Highlights
IntroductionStructural health monitoring (SHM) for predicting the onset of damage and deterioration of building structures is receiving more and more attention because of the rising numbers of aged structures and high costs caused by unpredictable hazards
Structural health monitoring (SHM) for predicting the onset of damage and deterioration of building structures is receiving more and more attention because of the rising numbers of aged structures and high costs caused by unpredictable hazards.Some success has been achieved with various heuristic optimization algorithms such as genetic algorithms (GAs), evolution strategy (ES), simulated annealing (SA), particle swarm optimization (PSO), clonal selection algorithm (CSA), and differential evolution (DE)
We studied the feasibility of using the Euclidean distance of a state sequence histogram of symbols as an objective function of adaptive immune clonal selection algorithm (AICSA) for the purpose of identifying structural parameters
Summary
Structural health monitoring (SHM) for predicting the onset of damage and deterioration of building structures is receiving more and more attention because of the rising numbers of aged structures and high costs caused by unpredictable hazards. Some success has been achieved with various heuristic optimization algorithms such as genetic algorithms (GAs), evolution strategy (ES), simulated annealing (SA), particle swarm optimization (PSO), clonal selection algorithm (CSA), and differential evolution (DE). These heuristic stochastic search techniques seem to be a promising alternative to traditional approaches. DE has been used to identify induction motor problems [6] and structural systems [7] These heuristic approaches are very powerful in many applications. Symbolic time series analysis (STSA) for anomaly detection in complex systems [8] has the potential to deal with noise. The results show that with the proper parameters, our methodology is a reliable and effective way of identifying structural parameters
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