TIM-FEM-ML synthetic technology for longitudinal optimization of tunnel excavated in the interlayered rock mass

• Published in: Underground space (Beijing) Vol. 23; pp. 327 - 342
• Main Authors: Li, Hui, Chen, Weizhong, Shu, Xiaoyun, Tan, Xianjun, Sui, Qun
• Format: Journal Article
• Language: English
• Published: Shanghai Elsevier B.V 01.08.2025; KeAi Publishing Communications Ltd; KeAi Communications Co., Ltd
• Subjects: Algorithms; Artificial intelligence; Building information modeling; Computer simulation; Construction accidents & safety; Data conversion; Design optimization; Discrete element method; Efficiency; Engineering; Finite element analysis; Finite element method; Geology; Layered rock mass; Machine learning; Mechanical analysis; Numerical analysis; Occupational safety; Optimization algorithms; Prediction models; Rock masses; Simulation; Tunnel information modeling; Tunnels; Underground engineering; Layered rock mass; Tunnel information modeling; Machine learning; Underground engineering; Design optimization
• ISSN: 2467-9674; 2096-2754; 2467-9674
• DOI: 10.1016/j.undsp.2025.03.001

•Transformation of informational and numerical models is realized.•An automatic numerical simulation process is coded.•Optimization algorithm is implanted into the automatic simulation scripts.•An automatic optimization framework integrating machine learning (ML) is proposed. The layout of underground engineering objects significantlyinfluences the stability of the surrounding rock mass and construction safety. Despite advancements toward intellectualization and informatization in design optimization and safety assessments, mechanical analysis-based engineering computations still face certain impediments. Consequently, this paper proposes a comprehensive framework integrating tunnel information modelling (TIM), finite element method (FEM) and machine learning (ML) technology to optimize the tunnel longitudinal orientation. It also delves into the specifics of addressing the challenges associated with each technology. The framework encompasses three phases: parametric modelling based on TIM, automatic numerical simulation based on FEM, and intelligent optimization leveraging ML. Initially, geometric models of the geological formations and engineering structures are constructed on the TIM platform. Subsequently, data conversion is facilitated through the proposed transformation interface. Python codes are programmed to realize automatic processing of numerical simulation and results are extracted to the ML algorithm for the prediction model. An optimization algorithm is implanted in the numerical stream file to retrieve the optimal relative intersection angle between the tunnel axis and the trend of rocks. A case study is conducted to evaluate the feasibility of the proposed framework. Results demonstrate a substantial improvement in design and optimization accuracy and efficiency. This framework holdsimmensepotential to propel the intellectualization and informatization of underground engineering.