# Research on an Improved SOM Model for Damage Identification of Concrete Structures
###### Abstract
The reason is that, compared with other industries, steel structure and concrete engineering are the products of industrialization, and the degree of mechanization, automation, intelligence and informatization of infrastructure is still relatively backward [33]. The complex constraints faced by engineering structures make the number of damage samples that can be extracted extremely limited, and the identification accuracy cannot meet the needs of engineering [34; 35]. A SOM neural network effectively preserves the network topology. It can obtain higher identification accuracy based on the limited damage sample space, effectively reducing the demand for the space sample size for the establishment of an intelligent damage monitoring system [36]. However, the traditional SOM network model algorithm has certain limitations. In the damage identification research, based on the traditional SOM algorithm model, the selected data is normalized by manual measurement and then directly input to the network model as an input sample [37]. However, the extraction process of input samples is often affected by subjective factors, which seriously affects the efficiency of network recognition. At the same time, the core of damage identification research, based on the SOM algorithm model, is to analyze the spatial sample layout form after the network model is learned [38]. This layout form is used as the topology structure of the network model. By continuously reorganizing the arrangement of the topology structure, each weight vector is located in the cluster center of the input vector [39]. When the SOM neural network training is completed, cluster analysis will be performed according to the spatial layout of the training data. Based on this spatial layout, both the topological position of the winning neuron and the spatial distribution of each neuron can be obtained. Therefore, the analysis of the topological spatial layout of neurons directly determines the SOM neural network clustering results. The core purpose of the SOM neural network for damage identification of concrete structures is to realize the classification of damage types, that is, to perform cluster analysis on the spatial structure constructed by damage indicators. However, in the process of damage clustering based on the traditional SOM algorithm model, there are often problems such as unclear damage categories, low damage identification efficiency and inaccurate identification caused by the difficulty in comparing the depth of the topology map. Therefore, in this paper, the research on the improved SOM model algorithm for damage identification of concrete structures is carried out. The self-developed 3D laser scanning system is used to obtain structural damage images and an improved method for constructing input samples of SOM network model based on the gray level co-occurrence matrix and digital feature screening is proposed. Based on the principle of network topology map analysis and the characteristics of grayscale images of topology maps, an improved SOM topology map analysis algorithm is proposed. The improved SOM algorithm model was applied to the bending fatigue test of reactive powder concrete. Based on the improvement of the recognition accuracy and the test effect, the validity of the proposed improved algorithm model is verified.