Artificial neural network optimized by differential evolution for predicting diameters of jet grouted columns
Artificial neural network optimized by differential evolution for predicting diameters of jet grouted columns
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DOI：
10.1016/j.jrmge.2021.05.009
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发表时间：
2021-12-01
影响因子：
7.3
通讯作者：
Modoni, Giuseppe
中科院分区：
文献类型：
--
作者：
Njock, Pierre Guy Atangana;Shen, Shui-Long;Modoni, Giuseppe
首次引入使用差分进化 (DE) 优化的新颖有效的人工神经网络 (ANN)，以提供喷射灌浆柱直径的稳健且可靠的预测。所提出的计算方法采用DE算法来解决神经网络训练和性能方面的困难，并优化控制神经网络效能的四个典型超参数（即epoch大小、隐藏层神经元数量、隐藏层数量和正则化参数）。这种方法通过随机梯度优化算法得到进一步增强，以允许“昂贵”的计算工作。首先使用准备好的喷射灌浆数据集对 ANN-DE 进行训练，然后进行验证并与流行的机器学习工具（即神经网络和支持向量机 (SVM)）进行比较。结果表明，ANN-DE 优于现有的喷射灌浆柱直径预测方法，因为它很好地平衡了训练效率和模型性能。具体来说，ANN-DE 在训练和测试阶段的均方根误差 (RMSE) 值分别为 0.90603 和 0.92813。优化后的 ANN 的相应值分别为 0.8905 和 0.9006，优化后的 SVM 的相应值分别为 0.87569 和 0.89968。无论多维和非线性，所提出的范例必将有助于解决各种岩土工程问题。 (C)2021 中国科学院岩土力学研究所。由 Elsevier B.V. 制作和主持
A novel and effective artificial neural network (ANN) optimized using differential evolution (DE) is first introduced to provide a robust and reliable forecasting of jet grouted column diameters. The proposed computational method adopts the DE algorithm to tackle the difficulties in the training and performance of neural networks and optimize the four quintessential hyper-parameters (i.e. the epoch size, the number of neurons in a hidden layer, the number of hidden layers, and the regularization parameter) that govern the neural network efficacy. This approach is further enhanced by a stochastic gradient optimization algorithm to allow 'expensive' computation efforts. The ANN-DE is first trained using a prepared jet grouting dataset, then verified and compared with the prevalent machine learning tools, i.e. neural networks and support vector machine (SVM). The results show that, the ANN-DE outperforms the existing methods for predicting the diameter of jet grouting columns since it well balances training efficiency and model performance. Specifically, the ANN-DE achieved root mean square error ( RMSE) values of 0.90603 and 0.92813 for the training and testing phases, respectively. The corresponding values were 0.8905 and 0.9006 for the optimized ANN, then, 0.87569 and 0.89968 for the optimized SVM, respectively. The proposed paradigm is bound to be useful for solving various geotechnical engineering problems regardless of multi-dimension and nonlinearity. (C) 2021 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting by Elsevier B.V.