基于自动化参数建模与迭代优化的隧洞智能支护设计平台研发与应用

doi:10.3724/1000-6915.jrme.2025.0593

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Abstract Support optimization for hydraulic tunnels poses a significant technical challenge during dynamic adjustments in site investigation, design, and construction. Traditional analytical methods and conventional numerical software often fall short of meeting the on-site engineering demands for high accuracy and rapid response. Consequently, there is an urgent need for the development of automated and ultimately intelligent design solutions in underground engineering. To address challenges in hydraulic tunnel support design, such as low modeling efficiency and insufficient standardization of parameters, this study first utilizes the 3DEC platform interface to propose an integrated modeling framework. This framework combines a unified representation of geometric parameters with a quantitative representation of multi-source rock mass parameters, enabling high-fidelity reconstruction of tunnel models under realistic geological conditions. Next, based on rock mass quality and the spatial distribution characteristics of discontinuities, the study automatically generates an initial sample library of support schemes, facilitating the automatic matching of support design options to surrounding rock classes and cavern spans. Concurrently, by incorporating a rock bolt spatial positioning algorithm and a stiffness equivalence model, the study introduces a parametric design approach and a unified mechanical representation method for the support system. Following this, the study analyzes the tunnel stress-deformation response and the spatial distribution of block collapse by traversing candidate schemes. A multi-criteria matrix evaluation is then conducted to identify the optimal support design. Finally, an intelligent tunnel support design platform is developed, which integrates “parametric modeling, automatic scheme generation, iterative comparison and selection, and multi-criteria decision-making” into a cohesive workflow. Using a large-scale hydraulic tunnel project as a case study, this work combines orthogonal experimental design, range analysis, and multi-criteria matrix evaluation. The results demonstrate that, in practical engineering applications, the three categorical indices achieve prediction accuracies exceeding 80%, while the quantitative prediction indices attain coefficients of determination (R²) greater than 0.89, confirming the feasibility and validity of the developed platform. Thus, the proposed approach offers an efficient and portable computational tool along with technical support for optimizing support and enabling dynamic adjustments during hydraulic tunnel construction.

Key words： tunneling engineering hydraulic tunnels intelligent support design automated modeling numerical simulation optimization and comparative selection

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	Articles by authors
	HE Peng1
	WANG Bin1
	LIU Ning2*
	MA Zhenghu1
	GAO Yaohui2
	LIU Kexin1

Cite this article:

HE Peng1,WANG Bin1,LIU Ning2*, et al. Research and application of an intelligent tunnel support design platform based on automated parameter modeling and iterative optimization[J]. , 2026, 45(4): 1199-1216.

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https://rockmech.whrsm.ac.cn/EN/10.3724/1000-6915.jrme.2025.0593 OR https://rockmech.whrsm.ac.cn/EN/Y2026/V45/I4/1199

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