基于R软件神经网络算法的工程量估算案例

Case Design of Engineering Quantity Estimation Based on R Software Neural Network Algorithm

随着我国工程造价改革的深入推进,利用大数据、人工智能辅助工程造价估算的重要性和迫切性被提到了前所未有的高度,通过数学模型方式解决工程造价估算和造价控制的问题,以此加强投资控制,提高管理水平,实现为建设项目增值保值的目标。文章在对建设项目技术特征参数与分部分项工程单位含量进行相关性分析的基础上,利用R语言软件,构建BP神经网络的分部分项工程单位含量估算模型,进而估算工程量和工程造价,并进行了实例验证。该案例设计拓展了基于R软件的神经网络模型在工程造价领域的应用,模型可直接预测工程量,避免了复杂建模,简化了预测过程,为项目前期工程造价预测提供了方法和工具,可为工程建设各方通过数据积累,学习和尝试数学模型解决工程造价估算和控制的问题提供参考。

With the deepening of China's engineering cost reform,the importance and urgency of using big data and artificial intelligence to assist engineering cost estimation has been raised to an unprecedented height.The problems of engineering cost estimation and cost control are solved by mathematical model,so as to strengthen investment control,improve management level and realize the goal of adding value and maintaining value for construction projects.Based on the correlation analysis between the technical characteristic parameters of construction projects and the unit content of partial projects,this paper uses R language software to construct the estimation model of unit content of partial projects based on BP neural network,and then estimates the engineering quantity and engineering cost,which is verified by an example.This case design expands the application of the neural network model based on R software in the field of engineering cost.The model can directly predict the engineering quantity,avoid complicated modeling,simplify the prediction process,provide methods and tools for the prediction of engineering cost in the early stage of the project,and provide reference for all parties in engineering construction to solve the problems of engineering cost estimation and control through data accumulation,learning and trying mathematical models.