Abstract
The effect of varying temperatures is one of the most important challenges of vibration-based damage identification due to its bigger effects on the structural response than the damage itself. This study presents a methodology incorporating the autoregressive (AR) time series model with two-step artificial neural networks (ANNs) to identify damage under temperature variations. AR coefficients, which are extracted by fitting the AR models to acceleration responses, are however sensitive to temperature changes, resulting in false diagnoses. Thus, two-step ANN models with the inputs of difference in AR coefficients are utilized to compensate the detrimental temperature variations. Finite element (FE) models of a steel-braced frame structure, simulating several damage scenarios with different damage locations and severities at fluctuating temperatures, are used to verify the effectiveness and reliability of this approach. Numerical results indicate that the proposed approach could successfully recognize, locate, and quantify damage by using output-only vibration and temperature data regardless of varying temperatures and noise perturbations.
Highlights
Structural health monitoring (SHM) has become a very important research area for evaluating the performance of critical civil infrastructure systems [1]
Owing to the existence of unavoidable cases, the changes caused by temperature fluctuations can even mask those induced by damage. erefore, if temperature variations are not taken into account for damage identification based on time series models, false diagnosis will occur
After fitting the AR time series models to the response data, the difference between the first threeorder AR coefficients from damaged and undamaged structures is extracted as damage features, which are fed into backpropagation neural network (BPNN) ensemble with the corresponding temperature values (T) to identify the locations and severities of damage
Summary
Structural health monitoring (SHM) has become a very important research area for evaluating the performance of critical civil infrastructure systems [1]. E changes of modal parameters or other damage-sensitive features based on the dynamic characteristics of civil structures can be used to indicate the existence, location, and severity of damage. To extract damagesensitive features from response data, time series analysis techniques have been widely utilized [1]. AR time series model-based approaches, in which damage indices such as Mahalanobis distance, residual error, and features are extracted from AR coefficients, have been used for damage diagnosis [7, 8]. In recent years, combining the AR time series model and ANN methods for structural damage identification has garnered significant interests [7,8,9]. A damage identification approach, based on time series analysis in conjunction with two-step ANN models using acceleration responses under temperature variations, is explored. Numerical experiments of a four-story, steel-braced frame structure with different damage scenarios and temperatures are conducted to investigate the effectiveness and robustness of the proposed approach
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