# Research on an Improved SOM Model for Damage Identification of Concrete Structures
## 3 Improved SOM Damage Identification Method
### SOM Improvement Method  
In order to reduce the interference of complex factors such as environment and humans, a method based on machine vision is proposed to obtain input samples. The damage signal is collected based on the vision sensor, and the initial sample is extracted by the feature extraction algorithm. In order to reduce the requirement for the number of input samples, the input samples that can effectively characterize the damage characteristics are automatically screened based on statistical theory. The improved SOM model and its input sample selection process is shown in Figure 3.  
An input sample for constructing a damage identification network model based on 3D laser scanning technology is proposed, as shown in Figure 3. First, a 3D image of the specimen under the loading system is acquired by adding 1D transmission equipment to a 2D laser sensor. Then, the initial samples are extracted by constructing the gray level co-occurrence matrix (GLCM) of structural damage. Finally, in order to further improve the effectiveness of the damaged samples, based on the digital feature screening (DFS), the feature parameters are selected as the input samples of the network model.  
Analysis of topology map  
In order to accurately identify the damage category information contained in the topology map image, according to the characteristics of the topology map image, a network model optimization method, the topology grayscale (TOP-G) algorithm, is proposed. Figure 4 shows the flow of the TOP-G algorithm.  
The first step is to determine the grayscale of the topology map:  
First, determine the number \(L\) of connection polygons between neurons. The gray level of the topology map is determined according to the number of \(L\), and the gray value range of the pixels in the topology map should be \([0,L]\). Thus, it is judged that the gray level of the image is \(g=L=2^{n}\), and it is deduced that \(n=log_{2}L\), \(g=2^{[n]}\), where \([n]\) represents the value of \(n\) is the smallest integer that exceeds the value of \(n\). Then the obtained \(g=2^{[n]}\) is the gray level of the topology map.  
Figure 3: Improved method for selecting input samples.  
* The second step is to grayscale the topological distance map: According to the gray level of the topological map, the topological distance color image is converted into a grayscale image, which is called a topological grayscale map.
* The third step is to create a sliding window: Suppose the number of neurons is \(m\), create a sliding window, label \(L_{1}\)-\(L_{m}\) and assign grayscale values \(g_{1}\)-\(\delta_{m}\) to each neighborhood polygon in the topological grayscale map.
* The fourth step is to discriminate the category of neurons: The gray values \(gi\) of all neighboring neurons of the unknown neuron \(i\) are extracted, compared and sorted. Determine whether \(g_{i}\) is the largest gray value in the neighborhood. If so, the neuron connecting the neighborhood polygon is a class, and the output \(i\) belongs to this class; If not, rejudge until the attribution category of all unknown neurons is determined, and the result is output.  
Figure 4: The flow of the TOP-G algorithm.