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<p>Reliable prediction of tunneling-induced ground settlement in coastal environments is challenged by coupled hydro-mechanical processes, site heterogeneity, and limited labeled data in new projects. This study presents a physics-informed and transferable deep learning framework that embeds dual physical constraints (Peck-type settlement envelope and Darcy-based pore-pressure consistency) within a dynamic transfer learning architecture and an actionable explainable-AI (XAI) layer. The model fuses heterogeneous, time-synchronized streams from tunnel boring machines, geotechnical sensors, and groundwater monitoring systems, adapting across segments and sites through similarity-aware transfer. Applied to a coastal shield-tunneling project (~ 80 000 samples), the framework achieved <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\({R}^{2}\)</EquationSource> </InlineEquation>= 0.95, RMSE = 1.5 cm, MAE = 1.2 cm, outperforming empirical regressions and unconstrained deep baselines. Ablation studies confirm that physics regularization enhances calibration and stability, while transfer learning improves cross-site generalization. For field deployment, the end-to-end pipeline operates in a 30 s cycle with median latency ≈ 1.6 s and peak inference time ≈ 0.05 s/sample (≈ 20 predictions/s). The XAI layer translates feature attributions into actionable engineering guidance—such as adjusting shield pressure, advance rate, or dewatering—to enable interpretable, auditable decision-making. These findings highlight physics-regularized, explainable transfer learning as a practical route toward trustworthy AI for geotechnical risk prediction in coastal tunneling.</p>

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Physics-informed and transferable explainable AI framework for reliable prediction of tunnel-induced settlement in coastal environments

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月1日

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  • Maral Mirzamohammadi,
  • Mehdi Haghighatjoo,
  • Hossein Jonah Taheri,
  • Navid Mirzaei Varzeghani

摘要

Reliable prediction of tunneling-induced ground settlement in coastal environments is challenged by coupled hydro-mechanical processes, site heterogeneity, and limited labeled data in new projects. This study presents a physics-informed and transferable deep learning framework that embeds dual physical constraints (Peck-type settlement envelope and Darcy-based pore-pressure consistency) within a dynamic transfer learning architecture and an actionable explainable-AI (XAI) layer. The model fuses heterogeneous, time-synchronized streams from tunnel boring machines, geotechnical sensors, and groundwater monitoring systems, adapting across segments and sites through similarity-aware transfer. Applied to a coastal shield-tunneling project (~ 80 000 samples), the framework achieved \({R}^{2}\) = 0.95, RMSE = 1.5 cm, MAE = 1.2 cm, outperforming empirical regressions and unconstrained deep baselines. Ablation studies confirm that physics regularization enhances calibration and stability, while transfer learning improves cross-site generalization. For field deployment, the end-to-end pipeline operates in a 30 s cycle with median latency ≈ 1.6 s and peak inference time ≈ 0.05 s/sample (≈ 20 predictions/s). The XAI layer translates feature attributions into actionable engineering guidance—such as adjusting shield pressure, advance rate, or dewatering—to enable interpretable, auditable decision-making. These findings highlight physics-regularized, explainable transfer learning as a practical route toward trustworthy AI for geotechnical risk prediction in coastal tunneling.