|Table of Contents|

Intelligent detection of post earthquake damage anomaly of RC frame structure based on wavelet scattering transform(PDF)

《建筑科学与工程学报》[ISSN:1673-2049/CN:61-1442/TU]

Issue:
2025年02期
Page:
27-38
Research Field:
建筑结构
Publishing date:

Info

Title:
Intelligent detection of post earthquake damage anomaly of RC frame structure based on wavelet scattering transform
Author(s):
KANG Shuai1;  WANG Zifa2;  ZHOU Ronghuan3;  HE Dongqing1;  YU Ye1;  LI Yimin1
(1.School of Civil Engineering and Architecture, Henan University, Kaifeng 475004, Henan, China; 2. Institute of Engineering Mechanics, China Earthquake Administration, Harbin 150080, Heilongjiang, China; 3. China Construction Eighth Bureau Development and Construction Co., Ltd., Zhengzhou 450000, Henan, China)
Keywords:
wavelet scattering transform;  abnormal detection;  post earthquake damage;  deep learning;  frame structure
PACS:
TU375
DOI:
10.19815/j.jace.2023.10033
Abstract:
Earthquake damage assessment based on traditional methods has the problems of low accuracy and efficiency. In order to achieve rapid assessment of post earthquake structural damage based on structural seismic response observation data and effectively solve the problem of data type imbalance, a damage abnormal data detection method based on time-frequency analysis and convolutional autoencoder(CAE)model was proposed. Firstly, the wavelet scattering transform was applied to process the original time domain signal to generate time-frequency data. Then, the corresponding convolutional autoencoder network model was established, and the time-frequency data was input into the CAE model for reconstruction training. The anomaly judgment threshold was determined according to the reconstruction error. Using this threshold, anomalies in the dataset were accurately distinguished, and the results were compared with those obtained using direct time-domain input method. Finally, the effectiveness of this method under noisy conditions was validated. The results show that the anomaly detection method based on the CAE model can well identify abnormal sequences in the damage data set, achieving a recall rate of over 90%, and the anomaly detection effect under the action of wavelet scattering transform is better. The proposed damage anomaly detection method integrating time-frequency analysis with the CAE model can significantly enhance the efficiency of post earthquake damage assessment. The computation time using wavelet scattering transform is only one-third of that using traditional time-domain input methods, and the method maintains high detection accuracy even in noisy environment.

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Last Update: 2025-03-20