摘要
基于大数据分析,对铁路桥梁混凝土工程造价计算模型的优化进行了研究。通过分析BP神经网络模型原理,发现该模型多输入多输出因素的非线性网络关系处理能力与本工程造价计算的非线性映射关系相吻合。利用计算机软件MATLAB中的BP神经网络工具箱进行搜索和查询,对于所采集得到的混凝土工程样本数据进行学习训练,把各项模型模拟数据结果和实际得到的样品数据结果进行比较分析,本研究构建的BP神经网络工程造价计算模型的误差率为3.29%,可满足铁路桥梁混凝土工程造价计算的精度要求。
Based on the analysis of large data,it studies the optimization of concrete cost calculation model of railway bridge.By analyzing the principle of BP neural network model,it is found that the processing ability of the nonlinear network relation of the multi-input and multi-output factors of the model is consistent with the nonlinear mapping relation of the cost calculation of the project.The BP neural network toolbox built by MATLAB software is used to train and learn the collected sample data step by step.The simulation data of the network model is compared with the actual sample data.The error rate of the BP neural network cost calculation model constructed in this study is 3.29%,which can meet the requirements of railway bridge concrete engineering construction.The accuracy requirement of price calculation.
作者
王淑桃
WANG Shutao(Xinxiang University,Xinxiang 453003,China)
出处
《混凝土》
CAS
北大核心
2020年第2期175-178,共4页
Concrete
