【摘要】 控制路基沉降是公路工程中的一个关键技术问题,而路基沉降与其影响因素之间存在着线性、非线性关系。当输入自变量较多时,用传统神经网络建模容易出现过拟合现象,导致网络模型预测精度较低。针对此问题,本文用遗传算法对神经网络模型的权值和阈值进行优化,同时讨论遗传参数的设定对输出结果的影响。通过对成南高速的实测数据进行仿真,试验结果表明:优化后的BP神经网络具有较高的预测精度,预测效果明显优于传统神经网络模型的输出结果,该预测方法可作为高速公路路基长期沉降预测的一种有效辅助手段。更多还原

【Abstract】 Controlling subgrade settlement is essential in highway engineering. Subgrade settlement has a linear and nonlinear relationship with its influencing factors. Over-fitting easily occurs in traditional neural network modeling in the presence of numerous input independent variables and results in the low prediction accuracy of the network model. This work aims to address these issues. Thus, the ability of the genetic algorithm to optimize the weight and threshold of the neural network is investigated, and the influence of the set of genetic parameters on the output results is discussed. Experiments with the proposed method show that the optimized BP neural network has higher prediction accuracy and better prediction effect than the traditional neural network model in the simulation of measured data for the Chengdu—Nanchong Highway. The prediction method can be used as an effective auxiliary means for predicting the long-term settlement of highway subgrades.更多还原