文章摘要
董娜,卢泗化,熊峰.大数据背景下基于ABC-SVM的建筑工程造价预测[J].技术经济,2021,40(8):25-32.
大数据背景下基于ABC-SVM的建筑工程造价预测
Cost prediction in construction project based on ABC-SVM under the background of big data
投稿时间:2020-03-02  修订日期:2021-07-19
DOI:
中文关键词: 大数据  造价预测  人工蜂群算法  支持向量机
英文关键词: big data  cost prediction  artificial bee colony algorithm  support vector machine
基金项目:四川省科学技术协会支撑项目:大数据背景下的建设项目智慧评价(19H0469)
作者单位E-mail
董娜 四川大学 建筑与环境学院 dongna@scu.edu.cn 
卢泗化 四川大学 lsh459745264@126.com 
熊峰 四川大学 建筑与环境学院 fxiong@scu.edu.cn 
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中文摘要:
      建筑工程项目决策阶段信息量少,精准高效的造价预测是科学决策的关键。为了提高项目前期工程造价预测的精度,探讨如何利用历史项目大数据及机器学习进行新建建筑工程项目的造价预测至关重要。本文首先通过文献研究确定了建筑工程决策阶段造价的主要影响因素,然后利用人工蜂群算法(ABC)对支持向量机(SVM)参数即惩罚因子和核函数参数进行优化计算,最终构建了基于ABC-SVM的建筑工程造价预测模型。最后以某工程造价数据平台上的84个建筑工程项目为数据源进行模型验证,结果显示,与GRID-SVM模型和BP神经网络模型相比,本文所提的ABC-SVM模型的预测精度更高,具有更好的适用性。
英文摘要:
      The construction project decision-making stage is lack of information, accurate and efficient cost prediction is the key to scientific decision-making. In order to improve the accuracy of pre-project construction cost prediction, it is very important to discusses how to use the big data of historical projects and machine learning to predict the cost of new construction projects. Firstly, the main influencing factors of the cost in the decision-making stage of construction engineering were determined through literature research. Then, the artificial bee colony algorithm (ABC) was used to optimize the support vector machine (SVM) parameters, namely penalty factorand kernel function parameter. Finally, the construction cost prediction model based on ABC-SVM was built. And then, 84 construction projects from a construction cost data platform were used as data sources for model validation. The results showed that, compared with GRID-SVM model and BP neural network model, the ABC-SVM model has higher prediction accuracy and better applicability.
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