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<p>The selection of an appropriate Sequential Excavation Method (SEM) strongly affects tunnel stability, costs, and project schedules. Existing predictive approaches often rely on parameters that are difficult and time-consuming to determine, particularly in small-scale projects, and their accuracy is limited compared with actual excavation practices worldwide. This study introduces a practical decision-making graph to support SEM selection. Two empirical correlations from laboratory and field studies were used to generate input parameters for numerical modeling, and 672 simulations were performed to construct the graph, which is based on tunnel span and the axial failure strain of the ground. The graph’s validity was evaluated using a confusion matrix for 96 tunnel projects, yielding 100% precision and 91% recall for the Full-Face method, 90% precision and recall for the Top Heading–Benching method, 59% precision and 71% recall for the Central Diaphragm method, and 89% precision and 80% recall for the Double Side Drift method. The overall accuracy was 85%, and Cohen’s Kappa was 0.77, indicating substantial agreement and confirming that predictions were not due to chance. Importantly, the proposed graph requires only two simple and readily available parameters, making it particularly useful in the preliminary design stage to screen feasible excavation methods and narrow down candidates for more detailed evaluation based on technical, logistical, economic, and risk considerations. These results demonstrate that the graph provides a simple, efficient, and practical tool for SEM selection, guiding both design and construction phases of tunnel projects.</p>

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A Span–Strain Framework for Determining Sequential Excavation Strategies in Tunnelling

  • 期刊论文

  • 发表日期：

    2026年4月25日

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  • Ako Daraei

摘要

The selection of an appropriate Sequential Excavation Method (SEM) strongly affects tunnel stability, costs, and project schedules. Existing predictive approaches often rely on parameters that are difficult and time-consuming to determine, particularly in small-scale projects, and their accuracy is limited compared with actual excavation practices worldwide. This study introduces a practical decision-making graph to support SEM selection. Two empirical correlations from laboratory and field studies were used to generate input parameters for numerical modeling, and 672 simulations were performed to construct the graph, which is based on tunnel span and the axial failure strain of the ground. The graph’s validity was evaluated using a confusion matrix for 96 tunnel projects, yielding 100% precision and 91% recall for the Full-Face method, 90% precision and recall for the Top Heading–Benching method, 59% precision and 71% recall for the Central Diaphragm method, and 89% precision and 80% recall for the Double Side Drift method. The overall accuracy was 85%, and Cohen’s Kappa was 0.77, indicating substantial agreement and confirming that predictions were not due to chance. Importantly, the proposed graph requires only two simple and readily available parameters, making it particularly useful in the preliminary design stage to screen feasible excavation methods and narrow down candidates for more detailed evaluation based on technical, logistical, economic, and risk considerations. These results demonstrate that the graph provides a simple, efficient, and practical tool for SEM selection, guiding both design and construction phases of tunnel projects.