Among many structural assessment methods, the change of modal characteristics is considered a well‐accepted damage detection method. However, the presence of environmental or operational variations may pollute the baseline and prevent a dependable assessment of the change. In recent years, the use of machine learning algorithms gained interest within structural health community, especially due to their ability and success in the elimination of ambient uncertainty. This paper proposes an end‐to‐end architecture to detect damage reliably by employing machine learning algorithms. The proposed approach streamlines (a) collection of structural response data, (b) modal analysis using system identification, (c) learning model, and (d) novelty detection. The proposed system aims to extract latent features of accessible modal parameters such as natural frequencies and mode shapes measured at undamaged target structure under temperature uncertainty and to reconstruct a new representation of these features that is similar to the original using well‐established machine learning methods for damage detection. The deviation between measured and reconstructed parameters, also known as novelty index, is the essential information for detecting critical changes in the system. The approach is evaluated by analyzing the structural response data obtained from finite element models and experimental structures. For the machine learning component of the approach, both principal component analysis (PCA) and autoencoder (AE) are examined. While mode shapes are known to be a well‐researched damage indicator in the literature, to our best knowledge, this research is the first time that unsupervised machine learning is applied using PCA and AE to utilize mode shapes in addition to natural frequencies for effective damage detection. The detection performance of this pipeline is compared to a similar approach where its learning model does not utilize mode shapes. The results demonstrate that the effectiveness of the damage detection under temperature variability improves significantly when mode shapes are used in the training of learning algorithm. Especially for small damages, the proposed algorithm performs better in discriminating system changes.
Baseline-free damage detection in bridges using acceleration records with the application of Laplacian
Existing damage detection techniques are reliant on monitoring the anomalies in the structure behavior. This requires knowledge of the undamaged baseline structure. This paper introduces a “Baseline-free” damage detection approach that utilizes the acceleration records of the structure to precisely estimate the loci of the damages without the need of using prior data from the structure. The paper investigates the application of Laplacian – second derivative – to the structure measured accelerations in order to localize the damages signature in the measurements. The paper will emphasize on bridges as a case study. The bridge will be dam-aged with different damage levels and locations to investigate the approach fidelity in quantifying the damage severity and position. First, acceleration measurements from the bridge are evaluated for different cases. After-ward, Laplacian is applied to the amplitudes of these measurements to magnify anomalies within them.
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- Award ID(s):
- 1849264
- NSF-PAR ID:
- 10214017
- Date Published:
- Journal Name:
- ASCE International Conference on Transportation & Development” 2020, Seattle, Washington, USA
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract -
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