复杂地质隧洞施工突涌水的集成学习预测与稳定性优化

doi:10.3724/1000-6915.jrme.2026.0046

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摘要针对复杂地质条件下隧洞突涌水预测精度低、非线性致灾关系难捕捉的难题，提出一种集成学习驱动的智能化预测框架。基于国内外249组涌水灾害实例数据的总结，筛选出22项致灾因子，并采用合成少数类过采样技术（SMOTE）克服少数类数据的不平衡问题，利用主成分分析（PCA）与K-means聚类建立灾害评价体系；进而通过堆叠集成（SG）策略，融合随机森林（RF）与BP神经网络（BPNN）构建预测模型，并结合层次分析法（AHP）识别关键因子。结果表明：（1） SG模型能够利用基学习器之间的误差互补特征，决定系数R2达到0.98，平均绝对误差（MAE）和均方误差（MSE）分别为0.68和0.87，整体预测性能及泛化能力优于单一基学习器；（2）对于涌水量超过300 m3/（d•m）的重大突涌水案例，SG模型的相对预测误差稳定控制在10%以内，有效降低了单一基学习器在高流量区间的预测波动；（3）引用层次分析法（AHP），在RF模型初始权重基础上，以工程认知加以修正，最终分析得出渗透系数、不良地质类型、地质构造、负地形及地下水赋存类型等5项指标为核心致灾因子（总权重＞48.56%）。由此可见，研究成果为地下工程涌水防灾预警提供了一种较为可靠的技术路径。

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	陈舟1
	2*
	王凯1
	2
	陈自豪1
	2
	王锦国1
	2
	宗晨宇1
	2
	窦智1
	2
	朱淳1
	2

关键词 ：隧道工程, 机器学习, 突涌水灾害, 随机森林, BP神经网络, 堆叠集成, K-means聚类

Abstract：To address the low accuracy of tunnel water-inrush prediction under complex geological conditions and the difficulty in capturing the nonlinear relationships among disaster-inducing factors, an intelligent prediction framework driven by ensemble learning is proposed. Based on a review of 249 documented water-inrush disaster cases in China and abroad, 22 disaster-inducing factors were selected. The synthetic minority over-sampling technique (SMOTE) was employed to overcome the class imbalance of the minority-class data, and principal component analysis (PCA) combined with K-means clustering was used to establish a hazard assessment system. A stacked generalization (SG) strategy was then adopted to integrate a random forest (RF) model and a backpropagation neural network (BPNN) to construct the prediction model, and the analytic hierarchy process (AHP) was further introduced to identify the key disaster-inducing factors. The results show that: (1) By exploiting the complementary error characteristics between the base learners, the SG model achieves a coefficient of determination (R2) of 0.98, a mean absolute error (MAE) of 0.68 and a mean square error (MSE) of 0.87, and its overall prediction performance and generalization ability are superior to those of a single base learner. (2) For major water-inrush cases with water-inflow volumes exceeding 300 m3/(d•m), the relative prediction error of the SG model remains stably within 10%, which effectively reduces the prediction fluctuations of a single base learner in the high-flow range. (3) Based on the initial weights obtained from the RF model and the correction made by engineering judgment, the AHP identifies the hydraulic conductivity, adverse geological conditions, geological structure, negative landform and groundwater occurrence conditions as the five core disaster-inducing factors, with a cumulative weight of more than 48.56%. These findings provide a relatively reliable technical approach for water-inrush disaster prevention and early warning in underground engineering.

Key words： tunnel engineering machine learning water inrush disaster random forest (RF) BP neural network (BPNN) stacked generalization (SG) K-means clustering

引用本文:

陈舟1，2*，王凯1，2，陈自豪1，2，王锦国1，2，宗晨宇1，2，窦智1，2，朱淳1，2. 复杂地质隧洞施工突涌水的集成学习预测与稳定性优化[J]. 岩石力学与工程学报, 2026, 45(11): 3403-3419.
CHEN Zhou1, 2*, WANG Kai1, 2, CHEN Zihao1, 2, WANG Jinguo1, 2, ZONG Chenyu1, 2, DOU Zhi1, 2, ZHU Chun1, 2. Ensemble learning-based prediction and stability enhancement of water inrush during tunnel construction under complex geological conditions. , 2026, 45(11): 3403-3419.

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https://rockmech.whrsm.ac.cn/CN/10.3724/1000-6915.jrme.2026.0046 或 https://rockmech.whrsm.ac.cn/CN/Y2026/V45/I11/3403

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