Method for evaluating applicability of TBM (Tunnel Boring Machine) of underground cavern of pumped storage power station

By constructing a non-AHP-driven dynamic multi-source data fusion machine learning evaluation framework, combined with a real-time monitoring system and high real-time sensors, the problems of the specificity and single index system of existing TBM applicability evaluation methods are solved, and efficient and safe construction of underground cavern TBMs for pumped storage power stations is realized.

CN121684306APending Publication Date: 2026-03-17CHINA RAILWAY 23RD BUREAU GRP THIRD ENG CO LTD
0 Cites 0 Cited by

Patent Information

Application Number
CN202511865146.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-11
Publication Date
2026-03-17

AI Technical Summary

Technical Problem

Existing TBM applicability evaluation methods are insufficient to meet the special needs of underground caverns in pumped storage power stations. They lack specificity and have a single indicator system, resulting in evaluation results that are out of touch with actual needs.

Method used

We adopt a non-AHP-driven, dynamic multi-source data fusion, and machine learning-enabled evaluation framework to construct static and dynamic indicator systems. By combining real-time monitoring systems and high real-time sensors, we automatically learn indicator weights through machine learning models, dynamically update applicability scores, and use SHAP values ​​to explain the model decision-making process.

Benefits of technology

It has achieved a highly targeted and comprehensive TBM applicability evaluation, which has improved construction efficiency and safety, covered non-traditional indicators such as ecological environment and life cycle cost, and ensured the timeliness and understandability of the evaluation results.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The invention relates to the technical field of TBM equipment, and discloses a pumped storage power station underground cavity TBM applicability evaluation method, which comprises the following steps: S1, firstly carding an existing framework of pumped storage power station underground cavity TBM applicability evaluation, and defining core technical elements and a protection range of the framework; an evaluation framework of non-AHP driving, dynamic multi-source data fusion and machine learning enabling is provided. According to the method, an evaluation framework of non-AHP driving, dynamic multi-source data fusion and machine learning enabling is provided, an index system is constructed, the index system is divided into static indexes (in a design stage, cavern burial depth, surrounding rock category and turning radius) and dynamic indexes (in a construction stage, TBM attitude error, cutter wear rate, surrounding rock deformation rate and ventilation efficiency), a real-time monitoring system is integrated, and the dynamic index is divided into a dynamic index and a dynamic index; and data such as TBM attitude, cutter wear and surrounding rock deformation are input into the model in real time, and the applicability score is dynamically updated, so that the method has the advantages of high pertinence, comprehensive index system and the like.
Need to check novelty before this filing date? Find Prior Art