3 research outputs found

    Principal Component Analysis Method with Space and Time Windows for Damage Detection

    No full text
    Long-term structural health monitoring (SHM) has become an important tool to ensure the safety of infrastructures. However, determining methods to extract valuable information from large amounts of data from SHM systems for effective identification of damage still remains a major challenge. This paper provides a novel effective method for structural damage detection by introduction of space and time windows in the traditional principal component analysis (PCA) technique. Numerical results with a planar beam model demonstrate that, due to the presence of space and time windows, the proposed double-window PCA method (DWPCA) has a higher sensitivity for damage identification than the previous method moving PCA (MPCA), which combines only time windows with PCA. Further studies indicate that the developed approach, as compared to the MPCA method, has a higher resolution in localizing damage by space windows and also in quantitative evaluation of damage severity. Finally, a finite-element model of a practical bridge is used to prove that the proposed DWPCA method has greater sensitivity for damage detection than traditional methods and potential for applications in practical engineering

    Deep learning for structural health monitoring

    Get PDF
    In recent years, Structural Health Monitoring (SHM) has attracted significant attention due to its potential in providing effective maintenance strategy for infrastructure. However, interpreting the SHM data remains a challenging task. Model-based interpretation which utilises a behaviour model in the interpretation, requires experts in both developing the model and understanding the change in the model. On the other hand, data-based/model-free interpretation methods reduce the complexity since no physical model is utilised. However, expertise is required in performing data-based interpretation methods due to the need of feature extraction. This thesis is motivated to develop a deep learning-based data interpretation method that can learn features automatically, thereby minimising the required expertise. In this thesis, a deep learning-based method for estimating load capacity of bridges from bridges’ images is developed. Data labelling is performed using information from National Bridge Inventory (NBI) database. Parametric study is performed to further investigate the method. A deep learning-based method that utilises correlation between two or more sensor measurements is proposed. This method employs raw measurement data from sensors. The proposed method is implemented for estimating structural responses by using measurements from other sensor as the input. The proposed method is compared with other machine learning methods and the method outperforms the other methods. Two damage detection approaches utilising deep learning techniques are discussed: novelty detection and multiclass classification. Both frameworks successfully predict the presence of the damage that could not be detected by a frequency-based method. An approach that combines deep learning with Moving Principal Component Analysis (MPCA) as an existing damage detection method is introduced. Experimental data collected from a laboratory-scale bridge are employed as a case study to validate the method. A series of investigation on parameters used in both MPCA and deep learning architecture are conducted in order to observe the method
    corecore