Affiliations:
School of Traffic and Transportation, Shijiazhuang Tiedao University, Shijiazhuang, China
Correspondence:
[*]
Corresponding author. Sicong Tang, School of Traffic and Transportation, Shijiazhuang Tiedao University, Shijiazhuang 050000, China. E-mail: sicong.tang001145879@hotmail.com.
Abstract: With the continuous deepening of the urbanization process and the progress of science and technology, people transform nature and develop nature on a larger and larger scale, among which the most iconic transformation is a variety of building structures built by people. And with the passage of time, the building structure in the perennial wind and sun, there will be signs of “illness”, if not timely treatment, it will have a huge impact on the stability and safety of the building structure. Based on this, in this paper, according to the characteristics of crack identification on the surface of concrete structure, background subtraction algorithm is selected for image noise reduction processing. Through three steps of digital image noise reduction, crack extraction and crack parameter identification, the quantitative recognition of cracks is completed and a complete system of crack parameter identification is formed. The experimental results show that the machine learning model of building structure health monitoring and damage recognition algorithm proposed in this paper has excellent statistical performance, and the relative error accuracy of recognition can be controlled within 10%.
Keywords: Image processing, building structure, health monitoring, damage identification, crack identification
