| 基于SFLA-GRNN模型的基坑地表最大沉降预测 |
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| 引用本文： | 钟国强,王浩,李莉,王成汤,谢壁婷.基于SFLA-GRNN模型的基坑地表最大沉降预测[J].岩土力学,2019,40(2):792-798. |
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| 作者姓名： | 钟国强 王浩 李莉 王成汤 谢壁婷 |
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| 作者单位： | 1. 中国科学院武汉岩土力学研究所 岩土力学与工程国家重点实验室，湖北 武汉 430071；
2. 中国科学院大学，北京100049；3. 武汉船舶通信研究所，湖北 武汉430079 |
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| 基金项目： | 国家自然科学基金面上项目（No.41472288, No.41172287, No.51579235）。 |
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| 摘 要： | 为可靠预测基坑周边地表沉降的发展趋势，提出了一种基于混合蛙跳算法和广义回归神经网络模型的基坑地表最大沉降预测模型（SFLA-GRNN模型）。首先，在沉降机制分析并初选输入变量集的基础上，利用灰色相关度分析对模型输入、输出变量的相关性进行量化，并剔除与输出变量相关性明显偏小的输入变量；其次，利用混合蛙跳算法（SFLA）对广义回归神经网络模型（GRNN）的平滑因子进行优化确定，减少人为因素对模型精度和泛化能力的不良影响；最后，利用筛选得到的输入变量集建立基坑地表最大沉降预测的广义回归神经网络模型。实例应用及对比计算结果表明，基于灰色相关度的输入变量筛选和基于混合蛙跳算法的平滑因子优化均能够有效提高广义回归神经网络模型的精度和泛化能力，以上结论可为类似变形预测提供参考。
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| 关 键 词： | 混合蛙跳算法 广义回归神经网络 平滑因子 灰色相关度分析 沉降预测 |
| 收稿时间： | 2017-08-07 |
Prediction of maximum settlement of foundation pit based on SFLA-GRNN model |
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| ZHONG Guo-qiang,WANG Hao,LI Li,WANG Cheng-tang,XIE Bi-ting.Prediction of maximum settlement of foundation pit based on SFLA-GRNN model[J].Rock and Soil Mechanics,2019,40(2):792-798. |
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| Authors: | ZHONG Guo-qiang WANG Hao LI Li WANG Cheng-tang XIE Bi-ting |
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| Institution: | 1. State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics,
Chinese Academy of Sciences, Wuhan, Hubei 430071, China; 2. University of Chinese Academy of Sciences, Beijing 100049, China;
3. Wuhan Maritime Communication Research Institute, Wuhan, Hubei 430079, China |
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| Abstract: | To predict the development trend of ground settlement around foundation pit accurately, a prediction model for maximum ground settlement of foundation pit was proposed based on shuffled frog leaping algorithm and generalized regression neural network model(SFLA-GRNN model). Firstly, through the settlement mechanism analysis and the initial selection of the input variable set, grey correlation analysis was used to quantify the correlation between model input and output variables. Some of input variables that are significantly less correlated with output variables were eliminated. Secondly, the smoothing factor of the generalized regression neural network model (GRNN) was optimized by using the shuffled frog algorithm (SFLA), so as to reduce the adverse effects of human factors on the accuracy and generalization ability of the model. Finally, a generalized regression neural network model for predicting the maximum settlement of the foundation pit was established by using the selected input variables set. Example application and comparative analysis show that input variables selection based on gray correlation degree and smoothing factor optimization based on shuffled frog leaping algorithm all can effectively improve the accuracy and generalization ability of GRNN model. The above conclusions can provide reference for similar deformation prediction. |
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| Keywords: | shuffled frog leaping algorithm generalized regression neural network smoothing factor grey correlation analysis settlement prediction |
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