Journal
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
Volume 18, Issue 5-6, Pages 1355-1371Publisher
SAGE PUBLICATIONS LTD
DOI: 10.1177/1475921718797949
Keywords
Concrete arch dam; health diagnosis; deformation monitoring; data mining; spatio-temporal clustering
Funding
- National Key Research and Development Project [2018YFC0407104]
- National Natural Science Foundation of China [51609074, 51739003]
- Jiangsu Basic Research Program [BK 20160872]
- Central University Basic Research Project [2017B11114, 2018B25514]
- Plan Project of Science and Technology of Zhejiang Province [2017F30011, 2018F10021]
- Plan Project of Water Conservancy Science and Technology of Zhejiang Province [RB1604]
- Key R&D Program of Guangxi [AB17195074]
- Priority Academic Program Development of Jiangsu Higher Education Institutions [YS11001]
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The timely analysis of deformation monitoring data and reasonable diagnosis of the structural health are key tasks in dam health monitoring studies. This article presents a spatio-temporal clustering and health diagnosis method for super-high concrete arch dams that uses deformation monitoring data obtained from plumb meters. The spatio-temporal expression of the deformation monitoring data is proposed first by upgrading a punctuated time series to a curved panel time series, including cross-sectional, dam axial, and temporal changing directions. Second, a comprehensive similarity indicator on three aspects, namely, the absolute distance, incremental distance, and growth rate distance, is constructed after a deep discussion on deformation similarity characteristics both temporally and spatially. Next, the temporal clustering method is proposed by keeping the key features, namely, extreme points and turning points, while eliminating extraneous details, namely, noise points. Finally, the optimal spatio-temporal clustering of dam deformation is achieved by designing a multi-scale fuzzy C-means method of data mining and its iterative algorithm. The proposed method is applied to the Jinping-I hydraulic structure, which is the highest concrete arch dam in the world. The clustering results is quite sensitive in different weight coefficients of the comprehensive similarity indicator and clustering numbers of fuzzy C-means method. The dam deformation behaviors on high-water-level, water-falling, and low-water-level periods are analyzed and diagnosed. The advanced version of proposed methods is verified by comparative analysis on dam health diagnosis results obtained from ordinary deformation distribution figures and the spatio-temporal clustering figures. The proposed method will facilitate the recognition of abnormal deformation areas and associated safety diagnoses.
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