应用遗传-神经网络方法预测软土路基沉降

Soft Subgrade Settlement Prediction by Generic-Neutral Network

摘要:

理论方法预测软土地基沉降与实际存在较大的差距，使得预测结果很难达到设计要求，不利于指导施工。将现有的理论方法同现场观测信息相结合，对软土地基变形作出更为准确的预测，有利于指导和控制工程施工。采用遗传算法和BP最优化法相结合的算法来训练网络，用遗传算法来优化BP神经网络中权值；用龚帕斯曲线来分解沉降时序，通过沉降趋势线偏移量来训练网络。采用这种方法预测软土路基沉降取得了较好的应用效果。

Abstract:

There exists a large gap in the soft ground settlement between theory prediction and the practice，so it is difficult to meet the design requirements and is not conducive to guide the construction. Combination of existing theoretical prediction methods and field observation information is helpful to control the engineering construction. With the combination of genetic algorithm and BP optimization method to train the network, weights of BP neural network can be optimized; with Gong Paz curve to decompose the settlement timing, network was trained by offset of settlement trend line. Good application results were achieved in predicting soft ground settlement by using this method.