摘要
为保障基坑施工安全,快速发现沉降问题,本文提出一种基于遗传算法的灰色Elman神经网络,利用沉降监测数据预测围护结构沉降。通过计算发现该模型迭代收敛速度较快,25次即能达到0.001的均方误差,可以在较短时间得到预测结果。与其他3种基础模型对比,发现该模型精度较高,在SSE、MRE、MSE 3种精度评价中均处于较高水平。
In order to ensure the safety of foundation pit construction and quickly find the settlement problem, this paper proposes a grey Elman neural network based on genetic algorithm to predict the settlement of retaining structure through the settlement monitoring data. It is found that the convergence speed of the model is fast and the mean square error of 25 skills is 0.001. The prediction results can be obtained in a short time. Compared with the other three basic models, it is found that the accuracy of the model is high, which is at a high level in SSE, MRE and MSE.
作者
张邵贺
ZHANG Shaohe(Xi′an Engineering Investigation&Design Research Institute of China National Nonferrous Metals Industry Co.,Ltd.,Xi′an 710000,China)
出处
《测绘与空间地理信息》
2022年第2期194-197,共4页
Geomatics & Spatial Information Technology
关键词
遗产算法
深基坑
灰色模型
神经网络
heritage algorithm
deep foundation pit
grey model
neural network
