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分析小波概率神经网络(WPNN)与数据融合技术在预测单桩竖向承载力中的应用原理,建立基于小波概率神经网络和数据融合技术的预测模型。根据长期的工程实测资料,利用高层建筑物静载试验数据对模型进行检验,并选取典型的样本进行预测值的误差分析。结果表明,预测的结果与静载试验数据吻合较好,从而证实了WPNN预测方法具有较好的可靠性和工程应用价值。
This paper analyzes the application principle of wavelet probability neural network (WPNN) and data fusion technology in predicting the vertical bearing capacity of single pile, and establishes a prediction model based on wavelet probability neural network and data fusion technology. According to the long-term engineering measured data, the static load test data of high-rise buildings are used to test the model, and the typical samples are selected for the error analysis of the predicted values. The results show that the predicted results are in good agreement with the static load test data, which proves that the WPNN prediction method has good reliability and engineering application value.