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An Intelligent Warning Method for Diagnosing Underwater Structural Damage

by 1, 1,* and 2,*
1
College of Civil Engineering, Northeast Forestry University, Harbin 150040, China
2
College of Science, Northeast Forestry University, Harbin 150040, China
*
Authors to whom correspondence should be addressed.
Algorithms 2019, 12(9), 183; https://doi.org/10.3390/a12090183
Received: 16 July 2019 / Revised: 26 August 2019 / Accepted: 26 August 2019 / Published: 30 August 2019
(This article belongs to the Special Issue Algorithms for Fault Detection and Diagnosis)
A number of intelligent warning techniques have been implemented for detecting underwater infrastructure diagnosis to partially replace human-conducted on-site inspections. However, the extensively varying real-world situation (e.g., the adverse environmental conditions, the limited sample space, and the complex defect types) can lead to challenges to the wide adoption of intelligent warning techniques. To overcome these challenges, this paper proposed an intelligent algorithm combing gray level co-occurrence matrix (GLCM) with self-organization map (SOM) for accurate diagnosis of the underwater structural damage. In order to optimize the generative criterion for GLCM construction, a triangle algorithm was proposed based on orthogonal experiments. The constructed GLCM were utilized to evaluate the texture features of the regions of interest (ROI) of micro-injury images of underwater structures and extracted damage image texture characteristic parameters. The digital feature screening (DFS) method was used to obtain the most relevant features as the input for the SOM network. According to the unique topology information of the SOM network, the classification result, recognition efficiency, parameters, such as the network layer number, hidden layer node, and learning step, were optimized. The robustness and adaptability of the proposed approach were tested on underwater structure images through the DFS method. The results showed that the proposed method revealed quite better performances and can diagnose structure damage in underwater realistic situations. View Full-Text
Keywords: structural health monitoring; digital image processing; damage; gray level co-occurrence matrix; self-organization map structural health monitoring; digital image processing; damage; gray level co-occurrence matrix; self-organization map
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Li, K.; Wang, J.; Qi, D. An Intelligent Warning Method for Diagnosing Underwater Structural Damage. Algorithms 2019, 12, 183.

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