Work

Intelligent identification and warning method of disc cutter abnormal wear in TBM construction based on extreme learning machine

Year: 2024

Type: article

Abstract: Abnormal cutter wear has a serious impact on TBM construction. If not found in time, it may lead to the cutterhead overall failure. Aiming at this problem, a general model and method to identify and warn the abnormal cutter wear using Extreme Learning Machine (ELM) is proposed. Based on multiple projects data, taking the general characteristic parameters as model inputs, the tunneling parameters in normal cutter wear are used to establish an ELM prediction model of advance speed, which predicts the abnormal cutter wear condition through the difference between predicted and actual advance speed. Through the project case, the model is verified and the early-warning threshold of abnormal cutter wear is given. The results show that the model prediction results are consistent with the actual cutter replacement situation. The method is effective and universal, which can provide a useful guidance for the identification, warning and replacement of TBM disc-cutter abnormal wear.

Source: Scientific Reports

DOI: 10.1038/s41598-024-76172-9

Authors: Qingwei Li, Lijie Du, Yalei Yang, Zhihua Ni, Xiangbo Zhao

Institution: Shijiazhuang Tiedao University

Language: English

Topic: Tunneling and Rock Mechanics

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,590