The precise estimation of water inflow (WI) into the tunnel during the construction phase, as one of the engineering geological hazards, is one of the most critical factors for project advancement and utilization, especially in the early design stages. To address this, the current study developed several predictor networks, including hybrid-optimized supervised learning models such as AdaDelta-recurrent neural network (AdaD-RNN), AdaGrad-long short-term memory (AdaG-LSTM), AdaGrad-gated recurrent unit (AdaG-GRU), Adam optimization-back propagation neural network (AO-BPNN), automatic linear forward stepwise information criterion (ALFS-IC), and a novel stacking-ensemble model. These models were trained and validated using a collected database from 13 drill-and-blast road tunnels in Iran. A new empirical model for predicting tunnel WI was introduced using ALFS-IC with high accuracy (R2 = 0.95). The models were trained on a dataset with five features and 600 data points (85 % training, 15 % testing), including physical factors of tunnels (tunnel depth, groundwater level), geomechanical characteristics of materials (rock quality designation), and water inrush feature (water yield property). The importance ranking and multi-task sensitivity analysis revealed that groundwater level and water yield property are the most influential parameters on the road tunnel WI. The analysis indicated strong correlations between predicted and observed values, with the stacking-ensemble and AdaG-GRU models exhibiting superior accuracy in predicting WI into the tunnel with R2 = 0.97 and 0.95 and NRMSE = 0.0017 and 0.0019, respectively. The stacking-ensemble algorithm had the highest accuracy rate of 90 % and AUC-ROC value of 98 %.

中文翻译：

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应用混合优化和堆叠集成标记神经网络预测钻孔爆破隧道中的水流


隧道进水量作为工程地质灾害之一，在施工阶段对隧道进水量的精确估算是项目推进和利用的最关键因素之一，尤其是在设计初期阶段。为了解决这个问题，目前的研究开发了几种预测器网络，包括混合优化的监督学习模型，如 AdaDelta 递归神经网络 （AdaD-RNN）、AdaGrad 长短期记忆 （AdaG-LSTM）、AdaGrad 门控递归单元 （AdaG-GRU）、Adam 优化反向传播神经网络 （AO-BPNN）、自动线性前向逐步信息准则 （ALFS-IC） 和一种新颖的堆叠集成模型。这些模型使用从伊朗 13 条钻爆公路隧道收集的数据库进行训练和验证。使用 ALFS-IC 引入了一种新的预测隧道 WI 的经验模型，精度高 （R2 = 0.95）。这些模型在具有 5 个特征和 600 个数据点（85% 训练，15% 测试）的数据集上进行训练，包括隧道的物理因素（隧道深度、地下水位）、材料的地质力学特性（岩石质量名称）和突水特征（产水特性）。重要性排序和多任务敏感性分析表明，地下水位和产水特性是影响公路隧道 WI 影响最大的参数。分析表明预测值和观测值之间具有很强的相关性，堆叠集成和 AdaG-GRU 模型在预测隧道中的 WI 方面表现出卓越的准确性，R2 = 0.97 和 0.95 以及 NRMSE = 0.0017 和 0.0019。stacking-ensemble 算法的准确率最高，为 90 %，AUC-ROC 值为 98 %。