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This chapter introduces the UT model, which is an empirical, geologically based framework for classifying the boreability of rock masses and predicting the performance of tunnel boring machines (TBMs) in rock. Based on Iran’s extensive mechanized tunnelling data, the UT model highlights rock mass characteristics as the main factors influencing the efficacy of rock cutting and the machine’s penetration rate, while also taking into account machine operational parameters. The model development process comprised five stages including (1) Data collection from completed projects; (2) Rigorous data screening to establish a comprehensive database; (3) Geological classification of rock masses (lithological types); (4) Statistical analysis of recorded data and development of empirical models and (5) Validation of the models using field data. Data sets collected from 199 tunnel sections from eight mechanized tunnelling projects in Iran were compiled into an inclusive database and analyzed using statistical techniques. The database included the averages of (i) actual machine performance parameters; (ii) geological/geotechnical properties of rock mass; and (iii) operating parameters of machine, for each tunneling section. The rock masses in the database were categorized into six lithological types (LT-I to LT-VI) based on parameters such as lithology and the engineering characteristics of the rock mass. Regression analyses (single and multivariable) revealed that the field penetration index (FPI) correlates strongly with intact rock strength (UCS) and rock quality designation (RQD). Therefore, the final empirical equations relating FPI to UCS and RQD for various lithological types enable estimation of penetration rate (PR) of machine under varied geological settings. The model also extends to cutter wear and utilization factor estimation and includes a boreability classification chart which estimate the FPI range for a given rock mass. Validation of the obtained models using data from several case studies shows good overall agreement with measured performance, highlighting the model’s utility for various stages of project design and construction. The limitations acknowledge constraints in cutter geometry and adverse geological conditions affects the boring process by TBM.

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University of Tehran Model, a Geological-Based Model for Rock Mass Boreability Classification and TBM Performance Prediction

  • 章节

  • 发表日期：

    2026年

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  • Jafar Hassanpour,
  • Jamal Rostami

摘要

This chapter introduces the UT model, which is an empirical, geologically based framework for classifying the boreability of rock masses and predicting the performance of tunnel boring machines (TBMs) in rock. Based on Iran’s extensive mechanized tunnelling data, the UT model highlights rock mass characteristics as the main factors influencing the efficacy of rock cutting and the machine’s penetration rate, while also taking into account machine operational parameters. The model development process comprised five stages including (1) Data collection from completed projects; (2) Rigorous data screening to establish a comprehensive database; (3) Geological classification of rock masses (lithological types); (4) Statistical analysis of recorded data and development of empirical models and (5) Validation of the models using field data. Data sets collected from 199 tunnel sections from eight mechanized tunnelling projects in Iran were compiled into an inclusive database and analyzed using statistical techniques. The database included the averages of (i) actual machine performance parameters; (ii) geological/geotechnical properties of rock mass; and (iii) operating parameters of machine, for each tunneling section. The rock masses in the database were categorized into six lithological types (LT-I to LT-VI) based on parameters such as lithology and the engineering characteristics of the rock mass. Regression analyses (single and multivariable) revealed that the field penetration index (FPI) correlates strongly with intact rock strength (UCS) and rock quality designation (RQD). Therefore, the final empirical equations relating FPI to UCS and RQD for various lithological types enable estimation of penetration rate (PR) of machine under varied geological settings. The model also extends to cutter wear and utilization factor estimation and includes a boreability classification chart which estimate the FPI range for a given rock mass. Validation of the obtained models using data from several case studies shows good overall agreement with measured performance, highlighting the model’s utility for various stages of project design and construction. The limitations acknowledge constraints in cutter geometry and adverse geological conditions affects the boring process by TBM.