Abstract
The damage identification of concrete dams provides a crucial alternative to assess their current conditions, and can provide continual safety evaluation throughout their whole lifespan. A novel data mining approach for damage identification which named the multi-resolution singular value decomposition-based permutation entropy (MSVD-PE) combined the eXtreme Gradient Boosting (XGBoost) is proposed to identify the different structural conditions of concrete dams. In this method, the MSVD-PE algorithm is first used to extract the damage feature from the de-noised vibration signals. Secondly, the low-rank constraint modified Laplacian Score algorithm is developed to refine the damage feature. Finally, the obtained feature is fed into the XGBoost model to accomplish the damage pattern identification. The proposed method is experimentally demonstrated to be able to recognize the different damage categories in concrete dams. Hence, the method proposed in this paper can provide reference for the safety assessment of the concrete dam in the operation and maintenance stage in the domain of Structural health monitoring.
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Acknowledgements
The authors would like to acknowledge the financial support provided by the National Key R&D Program of China the National Natural Science Foundation of China. The experiments have been conducted at Hohai University which is acknowledged.
Funding
This work was supported by the National Key R&D Program of China (2018YFC1508603, 2016YFC0401601) and the National Natural Science Foundation of China (Grant Nos. 51579086, 51739003).
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Li, H., Bao, T., Cao, E. et al. A Novel Damage Sensitive Feature Extraction Method of the Concrete Dam. Iran J Sci Technol Trans Civ Eng 46, 2173–2186 (2022). https://doi.org/10.1007/s40996-021-00709-5
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DOI: https://doi.org/10.1007/s40996-021-00709-5