城市轨道交通投资估算大数据分析与BIM集成展示

Big Data Analysis and BIM Integrated Display of Urban Rail Transit Investment Estimation

基于城市轨道交通在初步设计阶段设计概算所积累的历史数据,利用随机森林等机器学习算法挖掘不同设计参数、环境地质、施工资源配置条件下,实现迅速对总体设计阶段车站、区间及人防工程的工程造价的评估预测,并结合BIM数据集成与可视化展示的特点进行三维交互集成展示,迅速形成总体设计方案整体效果,辅助决策。

Based on the historical data accumulated in the preliminary design phase of urban rail transit,machine learning algorithms such as random forest are used to mine different design parameters,environmental geology,and construction resource allocation conditions,to quickly evaluate and predict the engineering cost of stations,sections,and civil air defense projects in the overall design phase.The three-dimensional interactive integration display is carried out by combining the characteristics of BIM data integration and visualization display,to quickly form the overall effect of the overall design plan to assist decision-making.