土工基础

土工基础 ›› 2025, Vol. 39 ›› Issue (4): 590-595.

• 专题论述 • 上一篇    下一篇

基于新型生物算法优化的非饱和黏土抗剪强度预测研究

梁仕超，朱乃江，赵红波，刘志强   

  1. （中冀建勘集团有限公司，石家庄 050227）
  • 收稿日期:2023-07-10 修回日期:2023-07-21 出版日期:2025-08-31 发布日期:2025-08-27
  • 作者简介:梁仕超（1994-），男，工程师，研究方向为岩土工程等。

Prediction of Shear Strength of an Unsaturated Clay by sing on a Novel Biological Algorithm

LIANG Shichao, ZHU Naijiang, ZHAO Hongbo, LIU Zhiqiang   

  1. (China Hebei Construction & Geotechnical Investigation Group Co. Ltd., Shijiazhuang 050227)
  • Received:2023-07-10 Revised:2023-07-21 Online:2025-08-31 Published:2025-08-27

摘要： 为得出非饱和黏土抗剪强度的最优预测模型，以长短期记忆神经网络模型（LSTM）为基础，基于哈里斯鹰算法（HHO）、鸽群算法（PIO）、麻雀算法（SSA）、卷尾猴算法（CAP）共4种生物算法，以黏土干密度、含水率、孔隙比、温度为输入数据，构建了4种优化模型，结果表明：HHOLSTM模型的拟合效果最优，该模型拟合方程斜率为0.991，模拟粘聚力时均方根误差、平均绝对误差、决定系数和模型效率系数分别为1.328 kPa、1.125 kPa、0.991和0.991，GPI为1.875，模拟内摩擦角时的精度指标分别为0.693度、0.584度、0.969和0.954，GPI为1.805。HHO-LSTM模型可作为非饱和黏土抗剪强度预测的推荐模型使用。


关键词: 非饱和黏土, 抗剪强度, 长短期记忆神经网络, 生物算法, 哈里斯鹰算法

Abstract: In order to obtain the optimal prediction model for the shear strength of an unsaturated clay, the long and short-term memory neural network model (LSTM) is used as the basis, and four biological algorithms, namely Harris Hawk algorithm (HHO), pigeon swarm algorithm (PIO), Sparrow algorithm (SSA) and Capuchin algorithm (CAP) are also used in this paper. The clay dry density, water content, pore ratio and temperature are used as the input data. Four types of optimization models are constructed in this paper. The results show that: the HHO-LSTM model has the best fitting effect. The slope of the fitting equation of the model was 0.991, the root-mean-square error, the average absolute error, the determination coefficient and the model efficiency coefficient are 1.328 kPa, 1.125kPa, 0.991 and 0.991, respectively. The GPI is 1.875. The accuracy indexes of simulated internal friction angle are 0.693, 0.584, 0.969 and 0.954 degrees, respectively with a related GPI of 1.805. The HHO-LSTM model can be used as a recommended model to predict the shear strength of the unsaturated clay.

Key words: Unsaturated Clay, Shear Strength, Long and ShortTerm Memory Neural Network Model, Biological Algorithms, Harris Hawk Algorithm

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