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<p>The occurrence of the overbreak (OB) phenomenon during tunnel construction is a significant concern for technical, operational, and economic stakeholders, representing a major challenge in subterranean environments. The OB threatens structural safety and extends project timelines. To predict, manage, and assess the stability of OB in tunnels, it is crucial to identify and analyze the influencing factors. These factors are generally categorized into two main groups: explosion parameters and geological parameters, which exhibit a nonlinear interdependence. This study utilizes a dataset of 217 real-world data sets related to factors influencing and instances of OB from drill and blast operations in northern Iran's Alborz tunnel. Using intelligent methodologies and the sinus-cosinus (SC) algorithm, a predictive model for the OB was developed, achieving coefficient determinations of 0.9 and 0.91 for the training and testing data sets, respectively. Further analysis of tunnel stability under the influence of OB was conducted using first-order reliability method (FORM), Monte Carlo simulation (MCS), and second-order reliability method (SORM) with the Reliability RTX software. The probabilities of OB occurrence for FORM, SORM, and MCS were found to be 0.049%, 0.04%, and 0.051%, respectively. The results demonstrate the effectiveness of these methods in analyzing the stability of the Alborz tunnel, with all three reliability analysis methods showing a low probability of OB occurrence. Additionally, sensitivity analysis of random variables affecting OB was performed, highlighting the significant role of specific charge in the probability of OB occurrence, second only to the rock mass rating.</p>

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Managing Overbreak in Tunnel Construction: Advanced Application of Intelligent Models and Reliability Methods

  • 期刊论文

  • 发表日期：

    2025年7月11日

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  • Hadi Fattahi,
  • Fatemeh Abdoli Junooshi,
  • Hossein Ghaedi

摘要

The occurrence of the overbreak (OB) phenomenon during tunnel construction is a significant concern for technical, operational, and economic stakeholders, representing a major challenge in subterranean environments. The OB threatens structural safety and extends project timelines. To predict, manage, and assess the stability of OB in tunnels, it is crucial to identify and analyze the influencing factors. These factors are generally categorized into two main groups: explosion parameters and geological parameters, which exhibit a nonlinear interdependence. This study utilizes a dataset of 217 real-world data sets related to factors influencing and instances of OB from drill and blast operations in northern Iran's Alborz tunnel. Using intelligent methodologies and the sinus-cosinus (SC) algorithm, a predictive model for the OB was developed, achieving coefficient determinations of 0.9 and 0.91 for the training and testing data sets, respectively. Further analysis of tunnel stability under the influence of OB was conducted using first-order reliability method (FORM), Monte Carlo simulation (MCS), and second-order reliability method (SORM) with the Reliability RTX software. The probabilities of OB occurrence for FORM, SORM, and MCS were found to be 0.049%, 0.04%, and 0.051%, respectively. The results demonstrate the effectiveness of these methods in analyzing the stability of the Alborz tunnel, with all three reliability analysis methods showing a low probability of OB occurrence. Additionally, sensitivity analysis of random variables affecting OB was performed, highlighting the significant role of specific charge in the probability of OB occurrence, second only to the rock mass rating.