Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Foundation pit deformation prediction method
The invention provides a foundation pit deformation prediction method. The foundation pit deformation prediction method comprises the steps of 1, setting an initial weight and a threshold value of a neural network; 2, determining parameters of a network structure model, and compiling a neural network operation program; 3, training the neural network, and inputting training samples into a training network in the neural network; 4, performing training sample output on foundation pit deformation data of the training samples by running the neural network; 5, processing a prediction sample by adopting an error grading iteration method, inputting the prediction sample into the trained neural network for sample prediction, and carrying out grading iteration until an error iteration value is smaller than a set error value; 6, outputting a final prediction sample result. According to the method, the error result between a predicted value and an actual monitoring value is optimized through processing of the error grading iteration method, the predicted value result better conforms to the actual monitoring value, the prediction accuracy is improved, and therefore the prediction performance of foundation pit deformation is improved.
本发明提供一种基坑变形预测方法，包括：步骤一：对神经网络的初始权值与阈值进行设置；步骤二：确定网络结构模型的参数，进行神经网络运行程序的编制；步骤三：训练神经网络，将训练样本输入神经网络中的训练网络；步骤四：通过运行神经网络对训练样本的基坑变形数据进行训练样本输出；步骤五：采用误差分级迭代法将预测样本进行处理，预测样本输入已完成训练神经网络进行样本预测，通过进行分级迭代，直至将误差迭代值小于设定误差值；步骤六：输出最终的预测样本结果。本发明经过误差分级迭代法的处理，使得预测值与实际监测值之间误差结果得到优化，预测值结果更加符合实际监测值，提高预测精准度，从而提高基坑变形的预测性能。
Foundation pit deformation prediction method
The invention provides a foundation pit deformation prediction method. The foundation pit deformation prediction method comprises the steps of 1, setting an initial weight and a threshold value of a neural network; 2, determining parameters of a network structure model, and compiling a neural network operation program; 3, training the neural network, and inputting training samples into a training network in the neural network; 4, performing training sample output on foundation pit deformation data of the training samples by running the neural network; 5, processing a prediction sample by adopting an error grading iteration method, inputting the prediction sample into the trained neural network for sample prediction, and carrying out grading iteration until an error iteration value is smaller than a set error value; 6, outputting a final prediction sample result. According to the method, the error result between a predicted value and an actual monitoring value is optimized through processing of the error grading iteration method, the predicted value result better conforms to the actual monitoring value, the prediction accuracy is improved, and therefore the prediction performance of foundation pit deformation is improved.
本发明提供一种基坑变形预测方法，包括：步骤一：对神经网络的初始权值与阈值进行设置；步骤二：确定网络结构模型的参数，进行神经网络运行程序的编制；步骤三：训练神经网络，将训练样本输入神经网络中的训练网络；步骤四：通过运行神经网络对训练样本的基坑变形数据进行训练样本输出；步骤五：采用误差分级迭代法将预测样本进行处理，预测样本输入已完成训练神经网络进行样本预测，通过进行分级迭代，直至将误差迭代值小于设定误差值；步骤六：输出最终的预测样本结果。本发明经过误差分级迭代法的处理，使得预测值与实际监测值之间误差结果得到优化，预测值结果更加符合实际监测值，提高预测精准度，从而提高基坑变形的预测性能。
Foundation pit deformation prediction method
一种基坑变形预测方法
LIU JINGLEI (Autor:in) / ZHANG GUOPENG (Autor:in) / LI CHUNYU (Autor:in) / ZHANG ZHENG (Autor:in) / WU HAO (Autor:in) / WEI BAOCHUAN (Autor:in) / YANG SHUO (Autor:in)
06.08.2021
Patent
Elektronische Ressource
Chinesisch
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