成本预估是建筑工程管理中的一个重要研究方向,传统模型无法精准描述建筑工程造价的变化趋势,导致建筑工程造价成本预估精度低,为此,设计了基于数据挖掘的建筑工程造价成本预估模型.建立建筑工程造价成本预估的数学模型,采用最小二乘支持向量机对建筑工程造价成本预估数学模型进行求解,并采用改进粒子群算法对模型再优化,通过具体建筑工程造价成本预估实验对有效性进行验证,数据挖掘提高了建筑工程造价成本预估的精度,且建筑工程造价成本预估性能要优于当前其他建筑工程造价成本预估模型.%The cost estimation is an important research direction in the construction project management. The traditional model can't accurately describe the change tendency of the construction project cost,and has low estimation accuracy of con-struction engineering cost. Therefore,a data mining based estimation model of construction project cost was designed. The mathe-matical model of the construction project cost estimation was established. And then the least square support vector machine (SVM)is adopted to solve the mathematical model of the construction project cost estimation. The improved particle swarm opti-mization(PSO)algorithm is used to optimize the model. The concrete estimation experiment of construction project cost was per-formed to verify the effectiveness of the model. The data mining can improve the estimation accuracy of construction project cost,and the performance of the model is higher than that of other construction project cost estimation models.
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