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王如會

（ 山東東方監理咨詢有限公司 ， 山東 濟寧 272000)

摘   要 ：針對建筑工程造價樣本中特征量較多、難以提取導致的數值預測精準度不高的問題，提出基于改  進灰色Markov 模型的預測研究，以實現有效解決 。首先采用線性回歸分析法獲得建筑造價樣本中人工費 、機械  使用費 、材料費以及總造價等子明細特征的先驗數據 ，并建立數據矩陣 。然后通過前后相鄰數值求得概率均值， 并初值化處理均值數據得到對應子明細特征的先驗模型 。基于灰色Markov 模型一階代表一個變量的特點 ，建立微分預測模型 ，計算每次數據更新生成的特征值變化，并與初始值對比，生成特征向量變化序列 。最后采用最小二乘積算法求得數據狀態轉移概率，結合待預測點的向量和轉移概率，通過查找對比得出有效工程造價預測。實驗數據證明，所提方法針對建筑工程造價的特征量分布情況預測精準度高 ，造價預測值與真實值間的差距較小，預測性能表現優異。

關鍵詞 ：改進灰色Markov 模型 ；建筑工程造價 ；最小二乘積算法 ；數據狀態轉移概率 ；特征向量

中圖分類號 ：TU789           

文獻標志碼 ：A            

文章編號 ：1005- 8249  (2023)   06- 0122- 06

DOI : 10. 19860/j . cnki . issn1005- 8249. 2023 . 06. 020

WANG Ruhui

( Shandong Oriental Supervision Consulting Co. ，Ltd. ，Jining 272000 ,   China)

Abstract: In response to the problem of low numerical prediction accuracy caused by the large number of feature quantities and difficulty in extracting in construction project cost samples, a prediction study based on an improved grey Markov model is proposed to effectively solve the problem. This method first uses linear regression analysis to obtain prior data on sub detailed features such as labor cost, machinery usage cost, material cost, and total cost in the construction cost sample, and establishes a data matrix. Then, the probability mean is obtained by adjacent values before and after, and the mean data is initialized to obtain a prior model of the corresponding sub detailed features. Based on the first order representation of a variable in the grey Markov model, a differential prediction model is established to calculate the changes in eigenvalues generated by each data update and compare them with the initial values to generate a sequence of eigenvector changes. Finally, the least squares integration method is used to obtain the probability of data state transition, and combined with the vector and transition probability of the predicted points, effective engineering cost prediction is obtained through search and comparison. The experimental data shows that the proposed method has high prediction accuracy for the distribution of characteristic quantities of construction project costs, and the difference between the predicted cost value and the actual value is small. The prediction performance is excellent.

Keywords: improving the grey markov model; construction project cost; minimum quadratic product algorithm; probability of data state transition; feature vector

作者簡介 ：王如會  (1974—) ,  男 ，本科 ，高級工程師 ，研究方向 ：建筑工程。

收稿日期 ：2023- 10-31