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<p>Shield TBM selection involves multi-criteria coupling, while the traditional analytic hierarchy process (AHP) relies on subjective scoring with insufficient objectivity and consistency verification difficulties. This study proposes an intelligent selection method by enhancing AHP with the Transformer self-attention mechanism. Pre-trained language models enable semantic vectorization, and the multi-head self-attention mechanism captures inter-indicator correlations to generate semantics-informed judgment matrices; beneficial-type membership functions and the weighted average fuzzy composition operator are adopted for fuzzy comprehensive evaluation; attention weight heatmaps are generated for visual analysis. The Yangshan–Jingguan section of Xuzhou Metro Line 4 serves as a case study; two candidate TBMs are evaluated and compared with traditional AHP, alongside parameter sensitivity analysis and multi-case validation across seven projects. The judgment matrix consistency ratio is 0.0114, and the CREC172/173 composite earth pressure balance (EPB) shield is recommended with a score of 95.38. Compared with traditional AHP, the Transformer-based method exhibits an 8.79% difference in the safety criterion weight, more accurately reflecting engineering semantic features such as karst development. Parameter sensitivity analysis shows safety criterion weight variation rate remains below 2%, confirming parameter robustness. Multi-case validation reveals dynamic weight adjustment under varying engineering conditions, with selection results consistent with field-adopted schemes, preliminarily demonstrating its applicability across multiple engineering scenarios.</p>

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Adaptive shield TBM selection model integrating AHP with attention mechanism and its engineering application

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月31日

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  • Yanchao Zhang,
  • Yundong Ma,
  • Shuai Zheng,
  • Pengyuan Yao,
  • Fu Te

摘要

Shield TBM selection involves multi-criteria coupling, while the traditional analytic hierarchy process (AHP) relies on subjective scoring with insufficient objectivity and consistency verification difficulties. This study proposes an intelligent selection method by enhancing AHP with the Transformer self-attention mechanism. Pre-trained language models enable semantic vectorization, and the multi-head self-attention mechanism captures inter-indicator correlations to generate semantics-informed judgment matrices; beneficial-type membership functions and the weighted average fuzzy composition operator are adopted for fuzzy comprehensive evaluation; attention weight heatmaps are generated for visual analysis. The Yangshan–Jingguan section of Xuzhou Metro Line 4 serves as a case study; two candidate TBMs are evaluated and compared with traditional AHP, alongside parameter sensitivity analysis and multi-case validation across seven projects. The judgment matrix consistency ratio is 0.0114, and the CREC172/173 composite earth pressure balance (EPB) shield is recommended with a score of 95.38. Compared with traditional AHP, the Transformer-based method exhibits an 8.79% difference in the safety criterion weight, more accurately reflecting engineering semantic features such as karst development. Parameter sensitivity analysis shows safety criterion weight variation rate remains below 2%, confirming parameter robustness. Multi-case validation reveals dynamic weight adjustment under varying engineering conditions, with selection results consistent with field-adopted schemes, preliminarily demonstrating its applicability across multiple engineering scenarios.