Citation: Liu, J.; Li, K. Research on an
Improved SOM Model for Damage
Identification of Concrete Structures.
Appl. Sci. 2022, 12, 4152. https://
doi.org/10.3390/app12094152
Academic Editor: Dario De
Domenico
Received: 26 March 2022
Accepted: 19 April 2022
Published: 20 April 2022
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Article
Research on an Improved SOM Model for Damage
Identification of Concrete Structures
Jinxin Liu
1
and Kexin Li
2,
*
1
School of Medical Technology, BeiHua University, Jilin City 132000, China; liujinxin1990@163.com
2
School of Civil Engineering and Transportation, BeiHua University, Jilin City 132000, China
* Correspondence: likenefu@126.com
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 by 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.
Keywords: damage identification; neural network; concrete structure; improved SOM
1. Introduction
Structural damage detection research is one of the most critical research contents
in Structural Health Monitoring (SHM) [
1
–
4
]. As the relevant technology for structural
damage detection, pattern recognition processes various forms of structural damage infor-
mation to carry out structural damage analysis and is an important part of information
science and artificial intelligence. Selecting an intelligent detection method suitable for prac-
tical engineering, combining damage indicators with feature-level and decision-level data,
thereby simplifying calculation and inference time, and realizing efficient and automated
intelligent evaluation are key issues that need further research [5–8].
The neural network has the learning ability to deal with nonlinear problems, strong
fault tolerance and robustness [
9
,
10
]. Damage identification based on the neural network is
based on the physical parameters or dynamic parameters of the structure in different states
of health. The parameters sensitive to structural damage are selected as the input of the
neural network [
11
]. The neural network is trained with a large number of damage cases in
numerical simulations. Finally, the mature network is trained to realize automatic damage
recognition based on the real structural response [
12
,
13
]. Scholars choose various pattern
recognition techniques for in-depth research on structural damage recognition, such as
Appl. Sci. 2022, 12, 4152. https://doi.org/10.3390/app12094152 https://www.mdpi.com/journal/applsci
