Hongwei Huang


Prof. Hongwei HUANG is the Distinguished Professor of Tongji University, China. He is mainly engaged in risk assessment on Geo-structural system, underground infrastructure safety and health monitoring and inspection, etc. Currently, he is the director of Shanghai Institute of Disaster Prevention and Relief, the founding director of International Joint Research Centre for Resilient Infrastructure of Tongji University. And also he has been the Founding Chair of Engineering Risk and Insurance Research Branch of China Civil Engineering Society since 2009. While serving as core members for international academic committees including GeoSNet, Geo-Institute on Risk Management of ASCE, TC304 of ISSMEGE, WG2 of ITA, etc., Prof. HUANG is also the Associate editor of ASCE-ASME Journal of Risk and Uncertainty of Engineering System, Editorial Board Members of Tunneling and Underground Space Technology, and GeoRisk, Chief editor of Journal of Tongji University (natural science) . 

There have been numerous scientific works granted by National “973” and “863” Projects, 15 projects of National Natural Science Fund of China, and 17 major scientific research projects lead by him. And with more than 200 journal papers and more than 6 books published, over 15 keynotes in various prestigious international conferences delivered, Prof. Hongwei HUANG has also chaired more than 5 international conferences and further received 2 International Distinguished Service Awards. And especially, he was honored to give the invited lecture for EUROCK1993 in Lisbon with the full funding by conference origination.


Machine Learning for Safety Risk Assesment on Rock Tunnel Driving

The excavation process of conventional tunneling method encounters complex uncertainties in geological conditions, posing an exceptional challenge to establishing a universally applicable rock mass classification system. Consequently, methodologies urgently necessitate comprehensive, objective, and accurate acquisition of geological information, despite the inherent abundance of information in excavation fronts. The unresolved persistence of relying on subjective expertise of on-site engineers for decisions regarding rock mass classification and stability assessment is noteworthy. There is an urgent need to effectively gather multisource datasets during excavation and objectively evaluate the rock mass quality and safety stability of tunnel excavation. In this context, this lecture proposes a set of excavation front rock mass intelligent classification based on computer vision and a stability assessment mechanism based on the upper bound limit analysis method, aiming to achieve intelligent management of safety risks associated with complex excavation fronts. 

Specifically, a framework has been established for extracting key information of rock mass working faces based on convolutional neural networks by collecting visual data from hundreds of working faces from over twenty tunnels. This framework encompasses the rock structure classification model based on Inception-ResNet-V2, the quantitative segmentation model for weak interlayers and seepage based on improved DeepLab V3+, and the statistical model for joint fractures based on the self-developed FraSegNet algorithm. Subsequently, 3 dimensional point cloud models were reconstructed using Structure from Motion (SfM) technology. Correspondingly, algorithms were developed to automate the extraction of 3D discontinuous faces and trace lines, thus establishing a multi-source heterogeneous rock mass working face dataset based on computer vision extraction algorithms. By correlating the acquired information with drilling data, mechanical and geometric data of the surrounding rock, the main types of indicators were determined: (1) primary parameters of discontinuous rock faces; (2) basic parameters of surrounding rock; and (3) on-site parameters. 

Based on this, research efforts were directed towards establishing an intelligent rock mass classification system based on computer vision and data-driven approaches. Machine learning models were utilized to predict rock mass grades. To enhance model performance, a Tree-structured Parzen Estimator based Bayesian optimization method was employed to select optimal model hyperparameters, followed by a robustness analysis of the model. The study indicates that the proposed hybrid ensemble learning models perform well in rock mass quality assessment. Subsequently, considering the structural characteristics of rock masses, the impact of groundwater, and the influence of artificial factors such as construction excavation schemes and parameters, a theoretical model for stability analysis of working faces was constructed within the framework of limit analysis. By integrating multiple regression algorithms and response surface theory, a rapid calculation formula for the safety factor of tunnel working faces was developed. By incorporating multifaceted information, a predictive model for on-site construction schemes was constructed. 

The superiority of the proposed methodology in practical engineering rock mass classification was validated by comparing the application of existing classification standards in the Qingdao Jiaozhou Bay Subsea under construction tunnel and the Yunnan Mountain tunnel. Combining considerations of complex geological environments and construction parameters enabled effective evaluation of stability and on-site validation, thereby providing an important foundation for risk management of subsequent excavation fronts.