Introduction of a modified QTBM model for predicting TBM penetration rate in rock, based on data from mechanized tunneling projects in Iran

• Published in: Bulletin of engineering geology and the environment Vol. 83; no. 5; p. 165
• Main Authors: Hassanpour, Jafar, Kazemi, Chamran, Rostami, Jamal
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-05-2024; Springer Nature B.V
• Subjects: Boring machines; Cutting tools; Drilling & boring machinery; Earth and Environmental Science; Earth Sciences; Foundations; Geoecology/Natural Processes; Geoengineering; Geology; Geotechnical Engineering & Applied Earth Sciences; Hydraulics; Mathematical models; Model accuracy; Nature Conservation; Original Paper; Parameters; Performance prediction; Prediction models; Rocks; Validity; Iran; Penetration rate; Q; model; TBM performance prediction model; Q system
• ISSN: 1435-9529; 1435-9537
• DOI: 10.1007/s10064-024-03659-0

One of the TBM performance prediction models that has been introduced in recent years and used for estimating penetration rate of TBM in hard rock conditions is the Q
TBM
model. This model was introduced by Barton in the early 2000s. There have been many reviews of this model and the estimated penetration rates in various projects. This study was also conducted to check the validity of this model and propose modifications to models for different geological conditions. For this purpose, a database of TBM field performance along with geological information was prepared from 109 tunnel sections (with reliable geological and machine data). The results of calculated penetration rate by the Q
TBM
model were compared with the field observation in the database. The results showed that this model offers low accuracy in predicting the machine penetration rate in different geological conditions. Subsequently, the relationship between input parameters of the Q
TBM
model with the penetration rate was re-examined to see the impact of individual input parameters on the penetration rate of the machine. A new model is proposed, where the input parameters with lower impact on the penetration rate were removed and an attempt was made to obtain an empirical relationship between the more influential parameters and the field penetration index (FPI). New empirical relationships are more accurate in predicting machine penetration rates due to their simplicity and the need for fewer input data.