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昆明冶金高等专科学校学报 ›› 2021, Vol. 37 ›› Issue (3): 87-.DOI: 10.3969/j.issn.1009-0479.2021.03.016

• 建筑设计与土建施工 • 上一篇    下一篇

基于GA-PB神经网络的山区公路桥梁造价预测模型—以简支T型梁桥为例

陈 璨,罗婧文   

  1. (1.重庆师范大学后勤与资产管理处,重庆 401331;2.重庆交通大学经济与管理学院,重庆 400074:3.重庆城市管理职业学院审计处,重庆 401331)
  • 收稿日期:2020-12-31 出版日期:2021-06-13 发布日期:2023-12-12
  • 作者简介:陈 璨 (1987-),女,重庆合川人,管理学博士研究生,中级经济师 (建筑经济),主要从事土建工程施工 技术项目管理与应急管理。

Prediction Model of Highway Bridge Cost in Mountain Areas Basedon GA-PB Neural Network: A Case Study of T-Beam Bridges

CHEN Can,LUO Jingwen   

  1. (1. Logistics and Asset Management Service , Chongqing Normal University, Chongqing 401331 , China; 2. Schoolof Economics and Management, Chongqing Jiaotong University, Chongqing 400074, China;3. Audit Office,Chongqing City Management College, Chongqing 401331, China)
  • Received:2020-12-31 Online:2021-06-13 Published:2023-12-12

摘要: 对山区公路桥梁的工程特征进行分析,利用直觉模糊分析法筛选出对山区公路桥梁造价影响较大的4个工程特征,并将其作为模型的输入量,构建了基于模糊逻辑和 CA-BP 神经网络的山区公路桥梁造价预测模型用MATLAB 神经网络工具箱进行程序设计,并选取 36 组已完工程数据对模型进行训练、测试、验证,验证表明该模型预测精度满足要求。将 CA-BP 神经网络模型与 BP 神经网络模型的结果进行对比,表明 CA-BP 神经网络模型收敛速度更快、精确度更高、稳定性更好,进一步验证了基于模糊逻辑和 CA-BP 神经网络的山区公路桥梁造价预测模型的可行性和有效性。

关键词: GA-BP模糊神经网络, 山区公路桥梁, 造价预测

Abstract: After analyzing the engineering characteristics of highway bridges in mountainous areas, thispaper used intuitionistic fuzzy analysis to find out four engineering characteristics which have greater in-fluence on the construction cost of highway bridges in mountainous areas, which are in turn adopted asthe inputs to the model to construct a prediction model of highway bridge cost in mountain area based onfuzzy logic and GA-BP neural network. It also designed a program with MATLAB neural network toolbox.and select 36 sets of completed engineering data to train, test, and verify the model. The results showthat the prediction precision of the model meets the requirements. By comparing the results of the CA-BPneural network model and the BP neural network model, it shows that the CA-BP neural network modelhas faster convergence speed, higher accuracy and better stability. It has further verified the feasibilitand validity of the model based on fuzzy logic and GA-BP neural network.

Key words:  CA-BP neural network, highway bridges in mountainous areas, cost prediction

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