# Research on an Improved SOM Model for Damage Identification of Concrete Structures
###### Abstract  
In order to solve the problem of intelligent detection of damage of modern concrete structures under complex constraints, an improved self-organizing mapping (SOM) neural network model algorithm was proposed to construct an accurate identification model of concrete structure damage. Based on the structure and algorithm of the SOM network model, the whole process of the core construction of the concrete structure damage identification network model is summarized. Combined with the damage texture characteristics of concrete structures, through the self-developed 3D laser scanning system, an improved method based on a small number of samples to effectively improve the effectiveness of network input samples is proposed. Based on the principle of network topology map analysis and its image characteristics, a SOM model improvement method that can effectively improve the accuracy of the network identification model is studied. In addition, based on the reactive powder concrete bending fatigue loading test, the feasibility and accuracy of the improved method are verified. The results show that the improved SOM concrete structure damage identification model can effectively identify unknown neuron categories in a limited sample space, and the identification accuracy of the SOM network model is improved 4.69%. The proposed improved SOM model method fully combines the network topology and its unique image features and can accurately identify structural damage. This research contributes to the realization of high-precision intelligent health monitoring of damage to modern concrete structures. In addition, it is of great significance for the timely detection, identification and localization of early damage to structures. damage identification; neural network; concrete structure; improved SOM +
Footnote †: journal: Applied  
1
Footnote 1: School of Medical Technology, BeHua University, Jilin City 132000, China; [EMAIL_ADDRESS]  
2
Footnote 2: School of Civil Engineering and Transportation, BeiHua University, Jilin City 132000, China
Fuzzy Algorithms, Support Vector Machines, etc. [14; 15; 16]. [PERSON] et al. (2021) proposed an integrated framework for data augmentation in a structural health monitoring system using machine learning algorithms [17]. [PERSON] et al. (2021) summarize the main methods for detecting, localizing and characterizing damage through algorithms and metrics in structural health monitoring, using electromechanical impedance spectroscopy [18]. [PERSON] et al. (2021) propose a global methodology for damage detection based on a recently developed version of the Negative Selection Algorithm [19]. With the continuous development of intelligent technology, various network models have achieved good research results and are alternately used in research in various fields [20; 21; 22]. [PERSON] et al. (2022), [PERSON] et al. (2021) and [PERSON] et al. (2021) used a convolutional neural network to build a network model for structural damage recognition [23; 24; 25]. [PERSON] et al. (2021) proposed to construct a back-propagation neural network model in vibration signal analysis [26]. [PERSON] et al. (2021) studied a probabilistic neural network model for damage status assessment of steel truss bridge joints [27]. [PERSON] et al. (2021) proposed to carry out research on structural damage identification through a support-vector machine neural network model [28]. [PERSON] et al. (2021) constructed general regression neural network model for damage identification of steel-concrete composite beams [29]. [PERSON] et al. (2021) proposed the use of a supervised self-organizing map in structural health monitoring [30]. [PERSON] et al. (2022) used SOM to develop damage pattern recognition and crack propagation prediction [31]. In addition, [PERSON] et al. (2022) comprehensively summarized the application of artificial neural network models in structural health monitoring [32]. At present, the research field of damage identification of concrete structures is facing difficulties.