Multivariate Linear Regression and CART Regression Analysis of TBM Performance at Abu Hamour Phase-I Tunnel

Abstract

The first phase of the Abu Hamour drainage and storm tunnel was completed in early 2017. The 9.5 km long, 3.7 m diameter tunnel was excavated with two Earth Pressure Balance (EPB) Tunnel Boring Machines from Herrenknecht. TBM operation processes were monitored and recorded by Data Acquisition and Evaluation System. The authors coupled collected TBM drive data with available information on rock mass properties, cleansed, completed with secondary variables and aggregated by weeks and shifts. Correlations and descriptive statistics charts were examined. Multivariate Linear Regression and CART regression tree models linking TBM penetration rate (PR), penetration per revolution (PPR) and field penetration index (FPI) with TBM operational and geotechnical characteristics were performed for the conditions of the weak/soft rock of Doha. Both regression methods are interpretable and the data were screened with different computational approaches allowing enriched insight. The primary goal of the analysis was to investigate empirical relations between multiple explanatory and responding variables, to search for best subsets of explanatory variables and to evaluate the strength of linear and non-linear relations. For each of the penetration indices, a predictive model coupling both regression methods was built and validated. The resultant models appeared to be stronger than constituent ones and indicated an opportunity for more accurate and robust TBM performance predictions.

Archives of Mining Sciences

“…Multivariate regression is a statistical method, which is used to find out the relationship between dependent and independent variables, as well as for data analysis in modeling. It allows predicting the dependent variable from the independent variables and their relationship (Jakubowski & Tajduś, 2014;Enayatollahi et al, 2014;Esmaeili et al, 2014;Jakubowski et al, 2017). Given 146 blastings recorded in the studied six limestone mines, 80% of the data were utilized for modeling while the other 20% were randomly used to test the model.…”

Comparison of Job Satisfaction Prediction Models for Construction Workers: CART vs. Neural Network

“…At the same time, in the field of engineering, the CART is mainly used in equipment improvements and analysis of geotechnical engineering characteristics. In engineering construction, CART could be combined with multiple regression, monitoring and recording the operation process of TBM (tunnel boring machine) and evaluation system [13], which could evaluate important problems of dam operation. Design and safety are evaluated for dam structure in the estimation of dam storage capacity [14].…”

Comparison of artificial neural networks (ANN), support vector machine (SVM) and gene expression programming (GEP) approaches for predicting TBM penetration rate

“…Salimi et al [21] developed a model for TBM performance prediction by regression technique. Jakubowski et al [22] proposed a new model for the tunnel boring machine performance by multivariate linear regression technique. Liu et al [23] presented a model for TBM performance prediction using a rock mass classification system.…”