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规则波作用下双桩基础周围海床超孔隙水压力智能预测

侯建康 程永舟 王敦格 韩小锐 李沅龙 石一

侯建康,程永舟,王敦格,等. 规则波作用下双桩基础周围海床超孔隙水压力智能预测[J]. 海洋学报,2026,48(4):81–93 doi: 10.12284/hyxb20260034
引用本文: 侯建康,程永舟,王敦格,等. 规则波作用下双桩基础周围海床超孔隙水压力智能预测[J]. 海洋学报,2026,48(4):81–93 doi: 10.12284/hyxb20260034
Hou Jiankang,Cheng Yongzhou,Wang Dunge, et al. Intelligent prediction of excess pore water pressure of seabed around double pile foundation under the action of regular wave[J]. Haiyang Xuebao,2026, 48(4):81–93 doi: 10.12284/hyxb20260034
Citation: Hou Jiankang,Cheng Yongzhou,Wang Dunge, et al. Intelligent prediction of excess pore water pressure of seabed around double pile foundation under the action of regular wave[J]. Haiyang Xuebao,2026, 48(4):81–93 doi: 10.12284/hyxb20260034

规则波作用下双桩基础周围海床超孔隙水压力智能预测

doi: 10.12284/hyxb20260034
基金项目: 国家自然科学基金(52371258);湖南省研究生科研创新项目(CX20251352)。
详细信息
    作者简介:

    侯建康(1995—),男,河南省周口市人,主要从事港口、海岸及近海工程研究。E-mail:24904030111@csust.edu.cn

    通讯作者:

    程永舟,教授,主要从事港口、海岸及近海工程研究。E-mail:chengyongzhou@163.com

  • 中图分类号: P731.22

Intelligent prediction of excess pore water pressure of seabed around double pile foundation under the action of regular wave

  • 摘要: 本研究针对波浪作用下双桩基础周围海床超孔隙水压力预测问题,开展了多目标智能预测研究。首先,通过波浪水槽试验,分析了不同波高条件下双桩基础周围海床超孔隙水压力时程演化和空间分布规律。其次,采用相位滞后检测与动态对齐方法对数据进行预处理,并分别利用门控循环单元(gated recurrent unit, GRU)和极限学习机(extreme learning machine,ELM)神经网络进行训练预测。最后,采用动态误差择优融合方法对两项模型的输出进行融合。结果表明:在当前试验条件下,随着波高增加,双桩基础周围海床超孔隙水压力幅值显著增大,沿深度方向呈现出明显的幅值衰减和相位滞后现象,且双桩基础周围超孔隙水压力最大幅值存在明显的空间差异。此外,构建的融合模型相较于原始模型或单一模型评估指标表现最优,其中皮尔逊相关系数(Pearson correlation coefficient, PCC)为0.9827,纳什效率系数(Nash-sutcliffe efficiency,NSE)为0.9218,均方根误差(root mean square error,RMSE)为0.003305,平均绝对误差(mean absolute error,MAE)为0.002559。研究成果为波浪作用下桩基周围海床多目标孔压智能预测提供了一种有效途径。
  • 图  1  试验布置示意图

    Fig.  1  Schematic diagram of test arrangement

    图  2  相位滞后检测与动态对齐流程图

    Fig.  2  Phase lag detection and dynamic alignment flow chart

    图  3  GRU网络结构

    Fig.  3  GRU network structure

    图  4  ELM网络结构

    Fig.  4  ELM network structure

    图  5  不同波高面历时曲线

    Fig.  5  Different wave height surface duration curves

    图  6  前侧超孔隙水压力历时曲线

    Fig.  6  Front side excess pore water pressure duration curve

    图  7  超孔压最大幅值对比

    Fig.  7  Comparison of the maximum amplitude of excess pore pressure

    图  8  实际波高与超孔压(入射波高0.06 m工况下)

    Fig.  8  Actual wave height and excess pore pressure (under the condition of an incident wave height of 0.06 m)

    图  9  动态对齐后波高与超孔压

    Fig.  9  Wave height and excess pore pressure after dynamic alignment

    图  10  融合模型累计绝对误差对比

    Fig.  10  Comparison of cumulative absolute error of fusion model

    图  11  绝对误差演变曲线

    Fig.  11  Absolute error evolution curve

    图  12  绝对误差箱型分布

    Fig.  12  Absolute error box distribution

    图  13  评估指标箱型分布(a:PCC; b:NSE; c:RMSE; d:MAE)

    Fig.  13  Evaluation metrics box distribution(a:PCC; b:NSE; c:RMSE; d:MAE)

    图  14  超孔压幅值预测结果

    Fig.  14  Prediction results of excess pore pressure amplitude

    图  15  超孔压最大幅值预测结果

    Fig.  15  Prediction results of the maximum amplitude of excess pore pressure

    表  1  最大相关系数与最优滞后步数

    Tab.  1  Maximum correlation coefficient and optimal lag step

    孔压测点 最大相关系数 最优滞后步数
    P1 0.9859 5
    P2 0.9865 9
    P3 0.9763 15
    P4 0.7869 20
    P5 0.9886 12
    P6 0.9872 20
    P7 0.9751 26
    P8 0.8749 35
    P9 0.9863 21
    P10 0.9856 26
    P11 0.9752 33
    P12 0.9597 39
    下载: 导出CSV

    表  2  不同模型评估指标均值

    Tab.  2  The mean value of evaluation metrics of different models

    模型名称 PCC NSE RMSE MAE
    融合模型 0.9827 0.9218 0.003305 0.002559
    ZDGRU 0.9811 0.9143 0.003607 0.002907
    ZDELM 0.9809 0.9141 0.003609 0.002978
    GRU 0.6292 0.4227 0.015709 0.014037
    ELM 0.6198 0.4119 0.015847 0.014172
    下载: 导出CSV
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出版历程
  • 收稿日期:  2026-03-08
  • 修回日期:  2026-04-26
  • 网络出版日期:  2026-05-22
  • 刊出日期:  2026-04-30

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