Ma Guoqing, Zang Xiaopeng, Chen Shitong, Zhi Momo, Huang Xiaoming
College of Civil Engineering, Shijiazhuang Tiedao University, Shijiazhuang, 050043, China.
Hebei Engineering Innovation Center for Traffic Emergency and Guarantee, Shijiazhuang Tiedao University, Shijiazhuang, 050043, China.
Sci Rep. 2024 Sep 5;14(1):20717. doi: 10.1038/s41598-024-71742-3.
To quickly assess slope stability based on field displacement monitoring data, this paper constructs a hybrid optimization model that predicts surface displacement during tunnel excavation in base-overburden slopes. The model combines Wavelet Decomposition (WD) with a Gated Recurrent Unit (GRU), and the GRU's hyperparameters are optimized using an Improved Particle Swarm Optimization algorithm (IPSO). The specific steps are as follows: First, the Wavelet Decomposition (WD) technique is applied to decompose the raw displacement data, extracting features at different time-frequency scales. Next, the Dropout technique is incorporated into the GRU model to prevent overfitting. Additionally, nonlinear inertia weight ω improved cognitive factor c, and social factor c are introduced. The PSO algorithm is improved by integrating crossover and mutation concepts from genetic algorithms. Finally, the IPSO is used to optimize the number of neural units h, H, L and dropout rates D and D in the GRU network architecture. After constructing the WD-IPSO-GRU model, a comprehensive comparison is made with various swarm intelligence algorithms and state-of-the-art models. The experimental results demonstrate that the WD-IPSO-GRU model significantly improves the prediction accuracy of surface displacement in slopes during tunnel excavation. Compared to directly using raw data for prediction, the introduction of the WD preprocessing technique improved the prediction accuracy at measurement points 01 and 02 by 28% and 45.9%, respectively. Additionally, with the model optimized by IPSO, the prediction accuracy at measurement points 01 and 02 increased by 76% and 56.7%, respectively. The WD-IPSO-GRU model effectively addresses the challenges of extracting features from univariate displacement time-series data and determining the parameters of the GRU network. It improves the prediction accuracy of surface displacement in base-overburden type slopes and demonstrates excellent generalization ability and reliability. The research results validate the potential application of the model in geotechnical engineering and provide strong support for assessing slope stability during tunnel excavation.
为基于现场位移监测数据快速评估边坡稳定性，本文构建了一个混合优化模型，用于预测覆盖层边坡隧道开挖过程中的地表位移。该模型将小波分解（WD）与门控循环单元（GRU）相结合，并使用改进粒子群优化算法（IPSO）对GRU的超参数进行优化。具体步骤如下：首先，应用小波分解（WD）技术对原始位移数据进行分解，提取不同时频尺度下的特征。其次，将随机失活（Dropout）技术引入GRU模型以防止过拟合。此外，引入了非线性惯性权重ω、改进的认知因子c和社会因子c。通过融合遗传算法的交叉和变异概念对粒子群优化算法进行改进。最后，使用IPSO对GRU网络架构中的神经单元数量h、H、L以及随机失活率D和D进行优化。构建WD - IPSO - GRU模型后，与各种群智能算法和现有先进模型进行了全面比较。实验结果表明，WD - IPSO - GRU模型显著提高了隧道开挖过程中边坡地表位移的预测精度。与直接使用原始数据进行预测相比，引入WD预处理技术后，测量点01和02的预测精度分别提高了28%和45.9%。此外，通过IPSO优化模型后，测量点01和02的预测精度分别提高了76%和56.7%。WD - IPSO - GRU模型有效解决了从单变量位移时间序列数据中提取特征以及确定GRU网络参数的挑战。它提高了覆盖层型边坡地表位移的预测精度，并展现出优异的泛化能力和可靠性。研究结果验证了该模型在岩土工程中的潜在应用，并为评估隧道开挖过程中的边坡稳定性提供了有力支持。
