Automatic interpretation algorithm for tunnel geological prediction based on DBN(PDF)
长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]
- Issue:
- 2017年03期
- Page:
- 90-96
- Research Field:
- 桥梁与隧道工程
- Publishing date:
Info
- Title:
- Automatic interpretation algorithm for tunnel geological prediction based on DBN
- Author(s):
- LI Bao-qi; HE Yu-yao; GUO Yuan-shu; QIU Ye-ji
- 1. School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, Shaanxi, China; 2. School of Information Engineering, Chang’an University, Xi’an 710064, Shaanxi, China; 3. Xi’an Highway Institute, Xi’an 710065, Shaanxi, China
- Keywords:
- tunnel engineering; tunnel geological prediction; GPR; DBN; compressed sensing; deep learning
- PACS:
- U452.11
- DOI:
- -
- Abstract:
- In the process of tunnel geological prediction, Interpretations of GPR (ground penetrating radar) line images are mainly determined by experience of experts, so that the accuracy is low. Through in-depth study on the principle of GPR, the geological characteristics of tunnel as well as the analysis on computational complexity of DBN (deep belief networks), an interpretation model of GPR line images was proposed based on compressed sensing and DBN. Firstly, the raw GPR line images were compressed by compressed sensing to obtain reasonable image with compression ratio. Secondly, the compressed images were sent to DBN model for classification. Then, the raw GPR line images were interpreted according to the classification results. Finally, the measured data of Liuyi (Liuzhai to Yizhou) Highway tunnels in Guangxi were used to verify the effectiveness of the proposed model. The experimental data contained 20 000 images which could be classified into 6 types of tunnel geology. 15 000 images of the experimental data were used as training dataset, and the other 5 000 images were used as testing dataset. The results show that when the compression ratio of GPR line images is 256, the reserve fine turning data is 1 000 images and the iteration number of DBN model is 30, the classification accuracy of the proposed model on the 6 types in testing dataset is 100%, and the single training time reduces to about 8% of that of the original DBN model. According to a large number of simulation experiments, the reasonable range of image compression ratio is 64 to 1 024, among which, the size of image can be greatly reduced and the information of raw image can be effectively preserved. Thus, it can be seen that the proposed model has the advantages of high accuracy of interpretation and fast training speed, and can provide reasonable basis for construction and excavation plan of tunnel.
Last Update: 2017-06-06