# An Intelligent Warning Method for Diagnosing Underwater Structural Damage
###### Abstract  
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. structural health monitoring; digital image processing; damage; gray level co-occurrence matrix; self-organization map +
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by detecting the future damage possibility of a multi-layer reinforced concrete building structure [11]. [PERSON] et al. proposed a new approach to damage detection of a concrete column structure subjected to blast loads using embedded piezoceramic smart aggregates [12]. According to research by [PERSON] et al., almost all types of structural damage can be effectively identified by image processing [13; 14; 15]. [PERSON] et al. proposed a new retrieval method for crack defect performance, which located the crack point by the most advanced crack detection technology and used image refinement technology to identify the skeleton structure of the point [16]. [PERSON] et al. applied ultrasound imaging to assess the extent of damage during concrete freeze-thaw cycles and extracted NAAP (damage index) parameters as an evaluation criterion [17]. [PERSON] et al. presented a new method for automatic detection of the flaking area of reinforced concrete columns and measured its characteristics in image data [18]. Based on the data provided by self-powered wireless sensors, [PERSON] et al. proposed a steel beam tensional vibration fatigue cracking detection method based on artificial intelligence [19]. Although these methods have achieved superior results through the combination of image properties and neural network models for damage identification, it is difficult to realize accurate damage diagnosis for underwater real-world structures [20; 21; 22; 23]. Taking image-labeled data from underwater real-world situations is restricted and the identification accuracy is influenced by environmental conditions and the defect type. The gray level co-occurrence matrix is a typical algorithm in statistical analysis and is widely used in various fields because of its good discrimination ability [24; 25]. As an important pattern recognition method, self-organization map neural network (SOM) has been widely used in automatic identification of signals, texts, images, and other fields due to its illuminating memory, learning, and generalization [26; 27; 28].