基于BP神经网络和模糊均值聚类融合算法的隧道工程造价建模与估算研究

Study on Cost Modeling and Estimation of Tunnel Engineering Based on BP Neural Network and Fuzzy Mean Clustering Fusion Algorithm

隧道工程投资估算编制对造价控制具有重要的作用,然而随着新技术、新工艺、新材料的不断涌现,传统的定额估算编制方法已较难适应新情况的发展,而信息技术和智能算法在工程造价中的应用和发展使得更先进、高效和准确的投资估算方式成为可能。文章基于BP神经网络和模糊均值聚类融合算法,对隧道工程造价进行建模与估算研究,在拟建隧道项目与已建项目的造价数据的相似程度较小和已知造价数据不充分的条件下构建非线性造价估算模型,并以鱼珠隧道为案例进行了实证分析,验证了成果的先进性与可靠性,为隧道工程前期造价控制提供了一种新的思路与方法。

The estimated cost of tunnel engineering investment plays a very important role in the cost control of tunnel engineering construction.However,the traditional estimation and compilation method is relatively simple and has a certain lag.With the continuous emergence of new technologies,new processes and new materials,the development and application of information technology.and intelligent algorithms in the project cost make the project cost management more accurate and scientific.In this paper,based on the fusion algorithm of BP neural network and fuzzy mean clustering,the cost of the tunnel project is modeled and estimated.This paper construct a nonlinear cost estimation model under the conditions of low similarity between the cost data of the proposed tunnel project and the existing project,as well as insufficient known cost data.And the Yuzhu tunnel is taken as an example to build the cost estimation model.Through the application,the control of the investment estimation of the Yuzhu tunnel project is better realized.