Comparison and analysis of denoising method in TBM key tunnelling data

Abstract

ABSTRACT Environmental vibrations and other factors cause significant random errors in raw data collected during TBM tunnelling. Such errors can reach up to 25% and will severely reduce the performance of data mining and machine learning model. To improve the data quality and find a suitable denoising method for TBM data, this study first proposed two assurance indicators to evaluate the effectiveness of data denoising, such as distortion degree (θ) and denoising magnitude (λ). Then, the effects of the data denoising schemes were compared. The results demonstrate that: (1) The Convlove19 scheme has better performance and is recommended for the denoising processing for TBM data. It can preserve the statistical characteristics of the raw data to the maximum extent possible and can improve the performance of the machine learning model by 15∼25%. (2) “Boring cycle segmentation after denoising strategy” (SAD) has better performance than “Boring cycle segmentation before denoising strategy” (SBD) and is recommended. The findings in this study can help with TBM data cleaning and quality improvement, as well as provide a reference for noise reduction of other data with similar features.