PDF
Full
Dr. Mingliang Zhou is an Associate Professor and Assistant Dean at College of Civil Engineering, Tongji University, China. Dr. Zhou earned his BA, MEng, and Ph.D. from University of Cambridge, UK. His research focuses on tunnel engineering safety and disaster prevention in water-rich fault fracture zones. The research findings have been incorporated into two industry standards and successfully applied in major infrastructure projects, including the Qingdao Jiaozhou Bay Second Subsea Tunnel and the China-Laos Railway. He has authored more than 80 journal and conference papers and co-authored the book "AI-enhanced Safety Evaluation for Tunnelling in Rock Engineering". He holds international roles, including Asian Chair of the ISSMGE Young Members Presidential Group and membership in ISSMGE Technical Committees TC309 and TC222. His industry-linked research has received recognition through awards such as the ISSMGE Bright Spark Lecture Award, ITA Product Innovation Award, and the Gold Medal at the Geneva International Invention Exhibition.
Outline
Evaluation of compressive strength in underground lining structures is critical for ensuring structural integrity and safety. Traditional assessment methods are often destructive, time-consuming, and impractical in confined environments such as tunnels and utility corridors. This study introduces an automated, nondestructive approach to visualize and estimate the compressive strength of underground concrete lining using hyperspectral imaging (HSI) combined with deep neural network (DNN) models. High-dimensional spectral data of concrete lining are assembled and trained to develop two DNN-based regression models, namely the Mono-Spectrum Deep Neural Regressor (MS-DNR) and the Segmented-Spectrum Deep Neural Regressor (SegS_DNR). Utilizing the SegS_DNR model, two-dimensional (2D) compressive strength distribution heatmaps were generated for visualization and assessment of strength variations. The SegS_DNR model demonstrated excellent predictive performance, achieving a coefficient of determination () of 0.925 and a Residual Prediction Deviation (RPD) of 5.28 on the testing set for compressive strength estimation. The idea is further validated in site by investigating the capability of identifying the defect regions of the tunnel concrete lining, namely the cracked, spalling, and leaking areas, and demonstrated promising performance in comparison with experienced inspectors on site. This approach offers a contact-free technique for automated structural health monitoring, contributing to safer and more sustainable underground maintenance practices.
PDF
1
0
| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
关闭全屏
No. 86 Xueyuan South Road, Haidian District, Beijing
010-62199257
qkjq@cast.org.cn
Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House
