论文部分内容阅读
将人工神经网络技术应用于CFG桩复合地基承载力计算,建立起CFG桩复合地基承载力计算的BP网络模型。利用山西省太原市某高校住宅楼为主的30组CFG桩复合地基工程资料(包括工程设计资料、地质资料、静载荷试验资料),考虑到复合地基加固区内的土性参数变化较小,各参数均取加权平均值,对所建立的BP网络模型进行训练和仿真检验,所得结果精度完全满足要求,为CFG桩复合地基承载力计算提供一种新的思路。
The artificial neural network technology is applied to calculate the bearing capacity of CFG pile composite foundation, and the BP network model for calculating the bearing capacity of CFG pile composite foundation is established. Based on the engineering data (including engineering design data, geological data and static load test data) of 30 CFG pile composite foundations in a university residential building in Taiyuan, Shanxi Province, taking into account the small change of soil parameters in the reinforced area of composite foundation, The weighted average values of all the parameters are used to train and simulate the established BP network model. The accuracy of the obtained results fully satisfies the requirements and provides a new idea for calculating the bearing capacity of CFG pile composite foundation.