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[1]JIN Kanhui,YANG Tao,HUO Shuyi,et al.Research on Prediction Methods of Shear Strength of Rolled Clay Based on Different PSO-ELM Models[J].Research of Soil and Water Conservation,2022,29(03):213-219+227.

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Research on Prediction Methods of Shear Strength of Rolled Clay Based on Different PSO-ELM Models

Research of Soil and Water Conservation[ISSN 1005-3409/CN 61-1272/P] Volume: 29 Number of periods: 2022 03 Page number: 213-219+227 Column: Public date: 2022-04-20

Title:: Research on Prediction Methods of Shear Strength of Rolled Clay Based on Different PSO-ELM Models

Author(s):: JIN Kanhui¹, YANG Tao¹, HUO Shuyi¹, WANG Lei¹, WANG Chengjie¹, JIANG Yue¹, ZHANG Huanling²; (1.Hebei University of Water Resource and Electric Engineering & Hebei Key Laboratory of Geotechnical Engineering Safety and Deformation Control, Cangzhou, HeBei 061000, China; 2.Tianjin Rail way Section of China Rail way Beijing Bureau Group CO., LTD, Tianjin 300011, China)

Keywords:: rolled clay; shear strength; particle swarm optimization; extreme learning machine; activation function

CLC:: TU411.7

DOI:: -

Abstract:: The shear strength of rolled clay directly affects the quality and service life of roller compacted earth-rock dams. In order to obtain the optimal prediction model for the shear strength of rolled clay, we used particle swarm optimization to optimize the extreme learning machine model(PSO-ELM), which is based on the three activation functions of Sine function, radbas function and hardlim function to construct PSO-ELM_sin, PSO-ELM_rad and PSO-ELM_hard. We compared the accuracy with ELM model, generalized regression neural network model(GRNN), random forest model(RF)and BP neural network model. The results show that: in the fitting results of cohesion and internal friction angle, the PSO-ELM_sin model has the highest accuracy, and the slopes of the fitting equations are 1.005 and 1.032, respectively, which are closer to the standard value ‘1'; in the monthly simulation, the PSO-ELM_sin model has the relative error of between 6.0% and 9.3%; RMSE, RRMSE and MAE of the PSO-ELM_sin model in the conhesion simulation of the PSO-ELM_sin model, the RMSE, RRMSE, MAE, E_ns and R² are 0.776 kPa, 1.80%, 0.641 kPa, 0.993 and 0.997, respectively. In the internal friction angle simulation, the RMSE, RRMSE, MAE, E_ns and R² are 1.635°, 6.98%, 1.616°, 0.983 and 0.998, respectively. The accuracy of PSO-ELMsin model ranks first. respectively. Therefore, the PSO-ELM_sin model has the highest accuracy among all models and can be used as a precise model for predicting the shear strength of rolled clay.

References:: [1] 胡赛潇,张云发,刘超,等.基于坝坡稳定复核的土石坝整体填筑质量评价[J].人民黄河,2018,40(5):133-138.
[2] 刘东海,王光烽.实时监控下土石坝碾压质量全仓面评估[J].水利学报,2010,41(6):720-726.
[3] Lai J, Wu S, Chiang C H. Evaluating the compaction quality of backfills by stress wave velocities[J]. Journal of Testing and Evaluation, 2011,39(5):785-791.
[4] 国家能源局.碾压式土石坝施工规范:DL/T 5129-2013[S].北京:中国电力出版社,2014:20-25.
[5] 赵惠新,吴志琴,李兆宇,等.基于BP神经网络冻融作用下细粒土抗剪强度的研究[J].水电能源科学,2012,30(4):32-33,78.
[6] 蒋佳莉,张卫兵,王红雨.干湿循环作用下银川地区重塑粉质黏土强度劣化试验研究[J].公路交通科技,2020,37(5):33-42.
[7] 陈佳雨,刘之葵.含水率及干密度对桂林红黏土抗剪强度的影响[J].中国岩溶,2019,38(6):930-936.
[8] 尚松浩,毛晓敏.基于BP神经网络的土壤冻结温度及未冻水含量预测模型[J].冰川冻土,2001,23(4):414-418.
[9] 王志会,吴爱祥,王洪江.基于PSO-BPNN的充填体围岩界面抗剪强度预测[J].矿业研究与开发,2020,40(3):130-134.
[10] 谢文强,蔡国军,王睿,等.基于CPTu数据的黏性土不排水抗剪强度神经网络预测[J].土木工程学报,2019,52(S2):35-41.
[11] 许健,冯灿,王掌权.基于BP神经网络的重塑黄土冻融过程抗剪强度劣化特性研究[J].水利与建筑工程学报,2016,14(6):13-17.
[12] Wang Y M, Traore S, Kerh T. Neural network approach for estimating reference evapotranspiration from limited climatic data in Burkina Faso[J]. Wseas Transactions on Computers, 2008,7(6):704-713.
[13] Traorea S, Wang Y M, Kerh T. Artificial neural network for modeling reference evapotranspiration complex process in Sudano-Sahelian zone[J]. Agricultural Water Management, 2010,97(5):707-714.
[14] Huang G B, Zhu Q Y, Siew C K. Extreme learning machine:theory and applications[J]. Neurocomputing, 2006,70:489-501.
[15] Zhu B, Feng Y, Gong D Z, et al. Hybrid particle swarm optimization with extreme learning machine for daily reference evapotranspiration prediction from limited climatic data[J]. Computers and Electronics in Agriculture, 2020,173:1-13.
[16] 冉梽乂,肖璐,崔宁博,等.基于广义回归神经网络模型模拟夏玉米蒸发蒸腾量[J].中国农村水利水电,2020(2):93-99.
[17] 张智韬,劳聪聪,王海峰,等.基于FOD和SVMDA-RF的土壤有机质含量高光谱预测[J].农业机械学报,2020,51(1):156-167.
[18] 展超.基于BP神经网络的富水砂层渣土改良试验效果预测[J].隧道建设(中英文),2020,40(7):988-996.
[19] 王杰,毕浩洋.一种基于粒子群优化的极限学习机[J].郑州大学学报:理学版,2013,45(1):100-104.
[20] 张念,崔宁博,赵璐,等.基于PSO-ELM的温室梨枣树液流量模拟[J].灌溉排水学报,2019,38(8):1-8.
[21] 张颖,李梅.基于粒子群优化极限学习机的水质评价新模型[J].环境科学与技术,2016,39(5):135-139.
[22] 王新民,万孝衡,朱阳亚,等.基于PSO-ELM的建筑物爆破震动速度预测[J].科技导报,2014,32(19):15-20.
[23] 陆业奇.含水率对黏土与混凝土接触面抗剪强度影响研究[J].水力发电,2018,44(12):126-129.

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Last Update: 2022-04-20