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Radar Image Recognition of Tunnel Lining Cavity Fillings Based on SVM
(1. School of Civil Engineering, Chongqing Jiaotong University, Chongqing 400074; 2. State Key Laboratory of Mountain Bridge and Tunnel Engineering, Chongqing Jiaotong University, Chongqing 400074)
Abstract Cavities and fillings behind the tunnel lining have an important impact on the safety of the tunnel struc? ture, and it is crucial to carry out cavity detection and identification to realize the assessment of the structural safety and the treatment of defects. Firstly, this paper adopts a combination method of indoor tests and FDTD forward simulations to obtain the radar mapping data under the conditions of filling the cavity with air, water, dry and wet sand,and compares and analyzes the waveform patterns of different fillings. Then, this paper extracts and classifies the waveform features based on support vector machine (SVM) algorithm and establishes an artificial intelligence recognition method for cavity fillings. The results show that the six types of fillings behind the lining can be effectively distinguished by taking four statistics (the mean, variance, mean absolute deviation before Fourier transform and maximum amplitude value max (fft(X)) after Fourier transform) as the SVM identifying features; and the recognition accuracy is better when single propensity data is taken, where all the six substances ′ binary classification can reach an accuracy of over 90%.
Abstract：
Cavities and fillings behind the tunnel lining have an important impact on the safety of the tunnel struc? ture, and it is crucial to carry out cavity detection and identification to realize the assessment of the structural safety and the treatment of defects. Firstly, this paper adopts a combination method of indoor tests and FDTD forward simulations to obtain the radar mapping data under the conditions of filling the cavity with air, water, dry and wet sand,and compares and analyzes the waveform patterns of different fillings. Then, this paper extracts and classifies the waveform features based on support vector machine (SVM) algorithm and establishes an artificial intelligence recognition method for cavity fillings. The results show that the six types of fillings behind the lining can be effectively distinguished by taking four statistics (the mean, variance, mean absolute deviation before Fourier transform and maximum amplitude value max (fft(X)) after Fourier transform) as the SVM identifying features; and the recognition accuracy is better when single propensity data is taken, where all the six substances ′ binary classification can reach an accuracy of over 90%.
HENG Aichen1,
2 ZHAO Haoran1,
2 TAN Bingxin1 etc
.Radar Image Recognition of Tunnel Lining Cavity Fillings Based on SVM[J] MODERN TUNNELLING TECHNOLOGY, 2022,V59(2): 45-52
2022, Vol. 59 