多策略改进的IPOA-CNN-BiGRU模型在基坑降水预测中的应用  

Application of Improved Pelican Optimization Algorithm-Convolutional Neural Networks-Bidirectional Gated Recurrent Unit Model in Predicting Foundation Pit Dewatering

作　　者：于维鹤 黄志强 王乃毅 张宁 YU Weihe;HUANG Zhiqiang;WANG Naiyi;ZHANG Ning(PetroChina Liaoning Dandong Sales Branch,Dandong 118016,Liaoning,China;School of Architecture and Civil Engineering,Shenyang University of Technology,Shenyang 110870,Liaoning,China)

机构地区：[1]中国石油辽宁丹东销售分公司,辽宁丹东118016 [2]沈阳工业大学建筑与土木工程学院,辽宁沈阳110870

出　　处：《隧道建设(中英文)》2024年第S2期286-299,共14页Tunnel Construction

基　　金：中铁上海工程局引导课题(SH-2020-引导-20)。

摘　　要：为提高基坑降水水位预测精度,加强对基坑降水水位的控制,在标准鹈鹕优化算法(POA)的基础上引入Logistic混沌映射、自适应动态权重因子w及萤火虫扰动最优位置3种优化策略,提出改进鹈鹕优化算法(IPOA)。选取4种基准测试函数对IPOA算法进行仿真测试,并选用其他5种优化算法进行对比分析。以沈阳北站基坑降水工程为实例进行研究,采用IPOA算法优化CNN-BiGRU模型中的隐含层节点个数、初始学习率以及L2正则化系数,形成IPOA-CNN-BiGRU水位预测模型,选用GWO、WOA、POA算法优化CNN-BiGRU模型并搭建Midas GTS NX基坑降水有限元数值模拟模型,进行对比分析,验证本文提出的预测模型在水位预测任务中的优越性。结果表明:1)IPOA优化算法在单峰及多峰函数的寻优任务中均体现出较好的性能,其在4种基准测试函数中的寻优速度更快,寻优结果要明显优于其余5种算法。2)在水位预测任务中,IPOA-CNN-BiGRU水位预测模型的EMAE、ERMSE、EMAPE分别为0.2138、0.2539、0.7713,预测精度比GWO、WOA、POA优化下的CNN-BiGRU模型分别提高了32.36%、20.96%、15.79%。3)相比Midas GTS NX有限元模型,IPOA-CNN-BiGRU模型的参数依赖性更低,其预测精度提高了80.65%,验证了采用IPOA-CNN-BiGRU模型对基坑降水水位进行预测的可行性。To enhance the prediction accuracy of foundation pit dewatering level and strengthen the control of dewatering level,three optimization strategies,Logistic chaos mapping,adaptive dynamic weighting factor wand firefly disturbance optimal position,are introduced to improve standard pelican optimization algorithm(POA).Four benchmark test functions are selected to simulate and test the improved POA(IPOA),and five optimization algorithms are selected for comparison and analysis.Taking a foundation pit in Shenyang North Railway Station as an example,the IPOA algorithm is used to optimize the number of nodes in the hidden layer,the initial learning rate,and the L2 regularization coefficient in the convolutional neural networks(CNN)-bidirectional gated recurrent unit(BiGRU)model to form the IPOA-CNN-BiGRU water level prediction model.Additionally,the grey wolf optimizer(GWO),whale optimization algorithm(WOA),and POA are selected to optimize the CNN-BiGRU model and build the Midas GTS NX finite element numerical simulation model.The comparative results among these models in predicting dewatering level of the foundation pit reveal the following:(1)The IPOA optimization algorithm shows better performance in both single-peak and multi-peak function optimization tasks,and its optimization speed is the fastest in the four benchmark functions,and the optimization results are significantly better than the remaining five algorithms.(2)In the water level prediction task,the EMAE,ERMSE,and EMAPE of the IPOA-CNN-BiGRU model are 0.2138,0.2539,and 0.7713,respectively,and the prediction performances are 32.36%,20.96%,and 15.79%higher than those of the CNN-BiGRU model under GWO,WOA,and POA optimization,respectively.(3)The IPOA-CNN-BiGRU model has a low dependence on the parameters compared with the Midas GTS NX finite element model,and its prediction accuracy is improved by 80.65%,which verifies the feasibility of the IPOA-CNN-BiGRU model in predicting dewatering level of the foundation pits.

关 键 词：基坑降水预测 改进鹈鹕优化算法 超参数优化 数值模拟 对比验证 

分 类 号：U45[建筑科学—桥梁与隧道工程]