en.Wedoany.com Reported - Recently, a key breakthrough has been achieved in the full-face tunnel boring machine AI knowledge large model, jointly developed by China Railway Construction Heavy Industry Corporation Limited (CRCHI), in collaboration with China Railway Siyuan Survey and Design Group Co., Ltd. (CRSSD), China Railway 18th Bureau Group Co., Ltd., and Hunan University. Among its components, the shield tunneling operation and maintenance and risk prevention intelligent agent (intelligent equipment agent) has been officially deployed in engineering applications. This intelligent agent has been integrated into full-face tunnel boring machines and has been applied in over 5,000 rings cumulatively in major national-level projects in China, including plateau railways, the Han River Diversion Project, and the expansion of the Hangzhou-Jiaxing-Huzhou Southern Drainage Western Channel Project. This advancement drives the transformation of tunnel construction from traditional experience-driven methods to data-empowered and intelligent decision-making.

Founded in 2007 and headquartered in Changsha City, Hunan Province, CRCHI is a core equipment manufacturing subsidiary of China Railway Construction Corporation Limited, specializing in the R&D and manufacturing of tunnel construction equipment, track systems, and specialized equipment. It was listed on the Shanghai Stock Exchange in 2019 (stock code: 688425). To address data silos and coordination challenges in tunnel design, construction, and equipment processes, the research team has developed a tunnel AI large model integrating a tunnel design intelligent agent, a construction management intelligent agent, and an intelligent equipment agent. This model connects the entire chain of tunnel engineering, from "design optimization and construction management to equipment operation and maintenance decision-making." Specifically, the design intelligent agent focuses on tunnel scheme design and support structure optimization; the construction management intelligent agent enables integrated management of construction safety, quality, and progress; and the intelligent equipment agent integrates functions such as intelligent operation and maintenance, automatic deviation correction during tunneling, auxiliary decision-making for tunneling parameters, surface settlement prediction, and TBM jamming warning.

The intelligent equipment agent establishes a fully closed-loop technical system of "behavior perception, risk warning, and intelligent decision-making." By leveraging intelligent algorithms to dynamically match ground conditions, it enables autonomous deviation correction and automatic step change for shields in complex geological environments. After seven rounds of iteration, the R&D team compiled nearly 10,000 engineering documents to form a specialized knowledge base and upgraded the intelligent operation and maintenance Q&A module. Engineering measurements show that after application, surface settlement prediction accuracy reaches the millimeter level, the accuracy of auxiliary decision-making for tunneling parameters reaches 90%, and the deviation in tunneling attitude control is less than 30 millimeters (compared to 50 millimeters in traditional modes). Equipment fault troubleshooting efficiency has increased by 20%, and overall project construction efficiency has improved by 10%. This enables full-process closed-loop control of risks such as adverse geology, tunneling exceedances, and excessive ground settlement, effectively reducing construction safety risks caused by human misjudgment.

Currently, the R&D team is steadily advancing the application and transformation of cutting-edge technologies, such as multimodal reasoning alignment and end-to-end model training, in the field of tunneling equipment. The engineering application of this tunnel AI large model provides independent and controllable technical support for intelligent construction of underground engineering, helping to consolidate China's leading position in the localization and intelligentization of shield equipment.

This article is compiled by Wedoany. All AI citations must indicate the source as "Wedoany". If there is any infringement or other issues, please notify us promptly, and we will modify or delete it accordingly. Email: news@wedoany.com