z-logo
open-access-imgOpen Access
Damage Identification of Prefabricated Reinforced Concrete Box Culvert Based on Improved Fuzzy Clustering Algorithm and Acoustic Emission Parameters
Author(s) -
Yafeng Gong,
Siyuan Lin,
He Feng,
He Yang,
Jiaxiang Song
Publication year - 2021
Publication title -
advances in materials science and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.356
H-Index - 42
eISSN - 1687-8442
pISSN - 1687-8434
DOI - 10.1155/2021/6660915
Subject(s) - culvert , materials science , acoustic emission , cluster analysis , identification (biology) , fuzzy logic , structural engineering , algorithm , composite material , computer science , machine learning , artificial intelligence , engineering , botany , biology
Prefabricated box culvert is a new structure in road engineering, whose health is very important to road safety. The use of acoustic emission (AE) as a detection method and the use of other improved algorithms to evaluate the damage of prefabricated box culverts are still insufficient. In this paper, two kinds of prefabricated box culverts are tested and studied, and the damage process of the box culverts is analysed based on the AE parameters of the box culvert using the traditional fuzzy C-means method (FCM). In addition, an improved algorithm based on the combination of grid density and distance (G-DFCM) was proposed, which was simulated and applied to the AE data analysis of the prefabricated box culvert. The research results show that the application effect of the G-DFCM algorithm is good, which not only overcomes the shortcomings of the original algorithm but also improves the effectiveness of the algorithm. This work can provide a supplement to the damage identification of fabricated box culverts.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom