基于ELM模型的混凝土坝坝基渗压预测
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摘要
为更精确的预测某混凝土坝坝基渗压变化趋势以保证大坝安全,利用混凝土重力坝渗压监测数据建立基于极限学习机(Extreme Learning Machine, ELM)的大坝基础渗压预测模型,并与传统逐步回归和传统BP神经网络方法进行对比。结果表明:ELM模型能够准确反映大坝坝基渗透系统的不确定性非线性关系,相比于逐步回归模型,ELM模型则可使h_(rmse)减幅至少有34.1%,误差区间降低有36.5%。ELM模型在精度和稳定性上均优于其余2种模型,其仿真曲线与测点渗压实测动态基本一致。该模型可作为渗透压力预测的推荐模型。
In order to more accurately predict the seepage pressure variation trend of a concrete dam foundation, the seepage monitoring data of concrete gravity dams are used to establish the extreme learning machine(ELM) model. ELM model is compared with traditional stepwise regression and BP neural network. The results showed that the ELM model could accurately reflect the uncertainty non-linear relationship of dam foundation seepage system, compared with stepwise regression model, ELM model can reduce the root mean square error(h_(rmse)) by at least 34.1% and error interval by 36.5%. Obviously, the ELM model is better than the other 2 models in accuracy and stability, and its simulation curve is basically consistent with the measured seepage pressure. The ELM could be used as a recommended model to predict seepage pressure.
In order to more accurately predict the seepage pressure variation trend of a concrete dam foundation, the seepage monitoring data of concrete gravity dams are used to establish the extreme learning machine(ELM) model. ELM model is compared with traditional stepwise regression and BP neural network. The results showed that the ELM model could accurately reflect the uncertainty non-linear relationship of dam foundation seepage system, compared with stepwise regression model, ELM model can reduce the root mean square error(h_(rmse)) by at least 34.1% and error interval by 36.5%. Obviously, the ELM model is better than the other 2 models in accuracy and stability, and its simulation curve is basically consistent with the measured seepage pressure. The ELM could be used as a recommended model to predict seepage pressure.
引文
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[14] 黄立维,符平,张金接.基于BP神经网络的差压式浆液密度监测技术[J].水利与建筑工程学报,2016,14(2):6-10.
[15] 田谷,续若楠,缪长青,等.基于BP神经网络的大跨桥梁结构混凝土温度预测[J].水利与建筑工程学报,2010,8(5):107-109.
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2019年2月水利与建筑工程学报Journal of Water Resources and Architectural EngineeringVol.17 No.1Feb.,2019
[2] 陈兰,陈浙新,王少伟.古田溪四级大坝坝基扬压力综合分析[C]//中国水力发电工程学会大坝安全监测专委会年会暨学术交流会论文集,2012:351-354.
[3] 杜京浓,王健平,宋汉周,等.日调节水库大坝基础扬压力的改进统计模型及其应用[J].水电与抽水蓄能,2015,39(1):33-37.
[4] 梅一韬,何鹏.3种混凝土坝坝基扬压力监控模型的比较[J].河海大学学报(自然科学版),2010,38(S2):352-355.
[5] 喻和平,张聪,郭英,等.小浪底大坝心墙渗透压力预测模型研究[J].人民黄河,2015,37(1):130-132.
[6] Das S K. Artificial neural networks in geotechnical engineering: modeling and application issues[C]//Metaheuristics in Water, Geotechnical and Transport Engineering, Netherlands: Elsevier Inc. 2013:231-270.
[7] 董梦思.基于BP神经网络的混凝土坝安全监测统计模型研究[D].昆明:昆明理工大学,2015.
[8] 朱创家,季昀,李同春,等.基于极限学习机的面板堆石坝施工期沉降预测模型及应用[J].水电能源科学,2014,32(6):69-71.
[9] Huang Guangbin, Zhang Hongming, Ding Xiaojian, et al. Extreme learning machine for regression and multiclass classification[J]. IEEE Transactions on Systems Man and Cybernetics Part B: Cybernetics, 2012,42(2):513-529.
[10] 张淼,孔盼,李雁华,等.基于极限学习机的土壤硝态氮预测模型研究[J].农业机械学报,2016,47(6):93-99.
[11] Huang G B, Ding X, Zhou H. Optimization method based extreme learning machine for classification[J]. Neurocomputing, 2010,74(1/3):155-163.
[12] 仲云飞,梅一韬,吴邦彬,等.遗传算法优化BP神经网络在大坝扬压力预测中的应用[J].水电能源科学,2012,30(6):98-101.
[13] 顾浩钦,仲云飞,程井,等.基于IPSO-BP神经网络的坝基扬压力预测方法研究[J].三峡大学学报(自然科学版),2013,35(2):20-24.
[14] 黄立维,符平,张金接.基于BP神经网络的差压式浆液密度监测技术[J].水利与建筑工程学报,2016,14(2):6-10.
[15] 田谷,续若楠,缪长青,等.基于BP神经网络的大跨桥梁结构混凝土温度预测[J].水利与建筑工程学报,2010,8(5):107-109.
[16] 王小川,史峰,郁磊,等.MATLAB神经网络43个案例分析[M].北京:北京航空航天大学出版社,2013:11-19. DOI:10.3969/j.issn.1672-1144.2019.01.029第17卷第1期
2019年2月水利与建筑工程学报Journal of Water Resources and Architectural EngineeringVol.17 No.1Feb.,2019