SUMMARY This new method of identifying structural parameters, called ‘symbolization‐based differential evolution strategy’ (SDES), merges the advantages of symbolic time series analysis and differential evolution (DE). Data symbolization in SDES alleviates the effects of harmful noise. SDES was numerically compared with particle swarm optimization and DE on raw acceleration data. These simulations revealed that SDES provided better estimates of structural parameters when the data were contaminated by noise. We applied SDES to experimental data to assess its feasibility in realistic problems. SDES performed much better than particle swarm optimization and DE on raw acceleration data. The simulations and experiments show that SDES is a powerful tool for identifying unknown parameters of structural systems even when the data are contaminated with relatively large amounts of noise. Copyright © 2012 John Wiley & Sons, Ltd.

A symbolization-based negative selection (SNS) algorithm that combines the advantages of symbolic time series analysis (STSA) and negative selection (NS) is proposed for detecting the abnormal states of building structures. In SNS, no prior knowledge of a structure's abnormal state is needed. Only the response of the structure in a current state is used as input data. In addition, this approach can work even with one sensor, so it is highly practical and flexible. A state sequence histogram (SSH) transformed from raw acceleration data by using STSA can capture the main features of structure dynamics and alleviate the effects of harmful noise. SSHs of the normal and abnormal states of a structure are defined as self and non-self elements, respectively. A new detector generation strategy and matching mechanism is proposed that makes the procedure more effective, along with guidelines for appropriate parameters in SNS. Numerical simulations and experimental verifications for different abnormal state cases were conducted to demonstrate the feasibility of the proposed method.

This hybrid methodology for structural health monitoring (SHM) is based on immune algorithms (IAs) and symbolic time series analysis (STSA). Real-valued negative selection (RNS) is used to detect damage detection and adaptive immune clonal selection algorithm (AICSA) is used to localize and quantify the damage. Data symbolization by using STSA alleviates the effects of harmful noise in raw acceleration data. This paper explains the mathematical basis of STSA and the procedure of the hybrid methodology. It also describes the results of an simulation experiment on a five-story shear frame structure that indicated the hybrid strategy can efficiently and precisely detect, localize and quantify damage to civil engineering structures in the presence of measurement noise.

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Cloud model is a new mathematical representation of linguistic concepts, which shows potentials for uncertainty mediating between the concept of a fuzzy set and that of a probability distribution. This paper utilizes cloud model theory as an uncertainty analyzing tool for noise-polluted signals, which formulates membership degree functions of residual errors that quantify the difference between the prediction from simulated model and the actual measured time history at each time interval. With membership degree functions a multi-objective optimization strategy is proposed, which minimizes multiple error terms simultaneously. Its non-domination-based convergence provides a stronger constraint that enables robust identification of damages with lower damage negative false. Simulation results of a structural system under noise polluted signals are presented to demonstrate the effectiveness of the proposed method.