|
||||||||
|
|
| 2023, Vol. 60(4): 67-75 DOI: | ||
| Study on a Deep Learning-based Model for Detecting Apparent Defects in Shield Tunnel Lining | ||
| (1. China Energy Engineering Group Jiangsu Power Design Institute Co., Ltd., Nanjing 211102; 2. Department of Geotechnical Engineering, College of Civil Engineering, Tongji University, Shanghai 200092; 3. Key Laboratory of Geotechnical and Underground Engineering of the Ministry of Education, Tongji University, Shanghai 200092) | ||
| Received null Revised null | ||
| Supporting info | ||
|
