The three-dimensional laser scanners for detecting tunnel cracks have
disadvantages such as low recognition accuracy and poor anti-interference ability. Therefore, a new research approach for crack
detection based on federated weighted learning algorithm is proposed. Based on
tunnel laser point cloud data, an optimized federated weighted learning
algorithm is employed, and asynchronous and residual testing adaptive
adjustment algorithms are adopted to achieve overall accurate detection of
tunnel cracks. Experiments are conducted in the Linyi-Tengzhou
expressway tunnel, focusing on several indicators such as reliability,
accuracy, and measurement accuracy of crack detection. The proposed algorithm
is compared with traditional ones. The results show that the proposed method
can effectively improve the reliability and accuracy of tunnel crack detection,
exhibiting good performance in detecting crack width accuracy. When
interference factors such as dust and exposed steel bars appear in the
detection results, the proposed algorithm still exhibits significant advantages
in reliability compared to traditional algorithms, achieving an accuracy of
over 95% and a misidentification rate of less than 10%, thus ensuring the
robustness of the algorithm′s application effect.
Through an on-site engineering practice, the minimum
deviation between the crack width identified by the proposed algorithm and the
manually measured value is only 0.06 mm, verifying its good crack recognition
accuracy.
YUAN Yueming, LIU Hongliang, YAN Zongwei, ZHANG Ziqi, GUO Peifan, ZHANG Zirui, YANG Guang
.
Three-Dimensional Laser Tunnel Crack Detection Based on Federated Weighted Learning Algorithm
[J]. Tunnel Construction, 2024
, 44(S1)
: 478
-484
.
DOI: 10.3973/j.issn.2096-4498.2024.S1.051
