大数据背景下基于ABC-SVM的建筑工程造价预测

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C和核函数参数g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.

Cost prediction in construction project based on ABC-SVM under the background of big data

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.