公路交通項目虛擬集成投資估算決策技術(shù)研究
發(fā)布時間：2019-01-19 17:09
【摘要】：工程項目前期的投資估算是優(yōu)選方案以及資金籌措的依據(jù),同時對工程項目總成本的控制有著至關(guān)重要的作用。以往投資估算方法的簡單滯后性導(dǎo)致估算誤差較大、結(jié)果不夠準確,因而尋找貼合工程實際、更為科學(xué)有效的投資估算方法來保證工程項目投資估算的準確度是急需解決的問題。 本文以公路交通項目為例,以全生命周期顯著性造價理論(WLCS)和已完類似工程為基礎(chǔ),通過對公路工程建設(shè)項目的特點分析,依據(jù)擁有訓(xùn)練樣本的不同情況建立相應(yīng)的非線性投資估算模型來擬合工程項目造價與其各影響因素之間的非線性關(guān)系,從而對公路交通項目進行造價預(yù)測。首先利用粗糙集(RS)的屬性約簡特性來挖掘工程數(shù)據(jù),提取建設(shè)項目的工程特征,克服以往尋找有效工程特征方法的主觀性,從而證明該方法的科學(xué)有效性。面對擬建工程項目的訓(xùn)練樣本數(shù)量的不同,分別采用不同的非線性投資估算方法。若訓(xùn)練樣本的數(shù)量一定,采用模糊聚類FC估算方法對擬建項目進行造價估算,通過實例驗證該方法是有效可行的。而面對大量訓(xùn)練樣本的情況,則采用智能集成估算方法,主要包括粗糙集—神經(jīng)網(wǎng)絡(luò)(RS-BP)估算法、蟻群—神經(jīng)網(wǎng)絡(luò)(ACO-BP)估算法以及粒子群—徑向基網(wǎng)絡(luò)(PSO-RBF)估算法。RS-BP估算法是用粗糙集首先對網(wǎng)絡(luò)輸入變量作約簡預(yù)處理,然后利用網(wǎng)絡(luò)進行訓(xùn)練預(yù)測。ACO-BP與PSO-RBF估算法是利用群智能對神經(jīng)網(wǎng)絡(luò)進行優(yōu)化,從而得到智能集成估算方法。通過案例仿真證明該算法更加貼合工程實際,大大加快訓(xùn)練速度,降低誤差,提高工程造價預(yù)測精度,體現(xiàn)了該算法的科學(xué)性與優(yōu)越性。在以上方法基礎(chǔ)上,運用虛擬技術(shù)建立投資方案虛擬可視化模型,使投資方案直觀形象展現(xiàn)在決策者面前。
[Abstract]:The investment estimation in the early stage of the project is the basis for the optimal selection of the project and the raising of funds. At the same time, it plays an important role in the control of the total cost of the project. In the past, the simple lag of investment estimation method resulted in large estimation error and inaccuracy of the result, so it is practical to find a fitting project. More scientific and effective investment estimation method to ensure the accuracy of project investment estimation is an urgent problem. This paper takes the highway transportation project as an example, based on the life-cycle significant cost theory (WLCS) and similar projects, through the analysis of the characteristics of highway construction projects. According to the different conditions with training samples, the corresponding nonlinear investment estimation model is established to fit the nonlinear relationship between the project cost and its influencing factors, so as to predict the cost of highway traffic projects. Firstly, the attribute reduction characteristic of rough set (RS) is used to mine engineering data, to extract the engineering features of construction projects, to overcome the subjectivity of the previous methods of finding effective engineering features, and to prove the scientific validity of this method. In the face of the different training samples of the proposed project, different nonlinear investment estimation methods are used. If the number of training samples is constant, the method of fuzzy clustering FC estimation is used to estimate the cost of the proposed project. The example shows that this method is effective and feasible. In the case of a large number of training samples, intelligent ensemble estimation method is used, including rough set neural network (RS-BP) estimation method. Ant Colony Neural Network (ACO-BP) estimation and Particle Swarm Radial basis Network (PSO-RBF) estimation. The RS-BP estimation method uses rough set to preprocess the input variables of the network. ACO-BP and PSO-RBF estimate method is to optimize the neural network by using swarm intelligence, and then the intelligent integrated estimation method is obtained. The simulation results show that the algorithm is more suitable to the engineering practice, greatly speeds up the training speed, reduces the error, and improves the accuracy of the project cost prediction, which reflects the scientific nature and superiority of the algorithm. On the basis of the above methods, the virtual visualization model of investment scheme is established by using virtual technology, so that the visual image of investment scheme can be displayed in front of decision makers.
【學(xué)位授予單位】：石家莊鐵道大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：F542
本文編號：2411568
[Abstract]:The investment estimation in the early stage of the project is the basis for the optimal selection of the project and the raising of funds. At the same time, it plays an important role in the control of the total cost of the project. In the past, the simple lag of investment estimation method resulted in large estimation error and inaccuracy of the result, so it is practical to find a fitting project. More scientific and effective investment estimation method to ensure the accuracy of project investment estimation is an urgent problem. This paper takes the highway transportation project as an example, based on the life-cycle significant cost theory (WLCS) and similar projects, through the analysis of the characteristics of highway construction projects. According to the different conditions with training samples, the corresponding nonlinear investment estimation model is established to fit the nonlinear relationship between the project cost and its influencing factors, so as to predict the cost of highway traffic projects. Firstly, the attribute reduction characteristic of rough set (RS) is used to mine engineering data, to extract the engineering features of construction projects, to overcome the subjectivity of the previous methods of finding effective engineering features, and to prove the scientific validity of this method. In the face of the different training samples of the proposed project, different nonlinear investment estimation methods are used. If the number of training samples is constant, the method of fuzzy clustering FC estimation is used to estimate the cost of the proposed project. The example shows that this method is effective and feasible. In the case of a large number of training samples, intelligent ensemble estimation method is used, including rough set neural network (RS-BP) estimation method. Ant Colony Neural Network (ACO-BP) estimation and Particle Swarm Radial basis Network (PSO-RBF) estimation. The RS-BP estimation method uses rough set to preprocess the input variables of the network. ACO-BP and PSO-RBF estimate method is to optimize the neural network by using swarm intelligence, and then the intelligent integrated estimation method is obtained. The simulation results show that the algorithm is more suitable to the engineering practice, greatly speeds up the training speed, reduces the error, and improves the accuracy of the project cost prediction, which reflects the scientific nature and superiority of the algorithm. On the basis of the above methods, the virtual visualization model of investment scheme is established by using virtual technology, so that the visual image of investment scheme can be displayed in front of decision makers.
【學(xué)位授予單位】：石家莊鐵道大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：F542
【參考文獻】
相關(guān)期刊論文 前10條
1 蔣慶全;虛擬現(xiàn)實技術(shù)淺析[J];兵工自動化;2001年01期
2 溫國鋒;建設(shè)項目投資估算模型分析[J];中國煤炭經(jīng)濟學(xué)院學(xué)報;2000年03期
3 董晉吉;梁劍;;全生命周期造價管理理論在電力工程造價管理中的應(yīng)用探討[J];電力建設(shè);2009年07期
4 楊志凌;劉永前;;應(yīng)用粒子群優(yōu)化算法的短期風(fēng)電功率預(yù)測[J];電網(wǎng)技術(shù);2011年05期
5 黎亞鵬;;高�；üこ掏顿Y估算方法評析[J];大眾科技;2011年08期
6 王國華;;虛擬現(xiàn)實技術(shù)在建筑工程中的應(yīng)用研究[J];硅谷;2008年12期
7 張小平;余建星;段曉晨;;基于CSIs、EVM、GM(1,1)理論的建設(shè)成本控制方法研究[J];中國農(nóng)機化;2008年02期
8 周怡安;盧毅;李玨;;公路工程全生命周期綜合造價估算模型[J];中外公路;2011年02期
9 宮赤坤,閆雪;基于RBF神經(jīng)網(wǎng)絡(luò)的預(yù)測控制[J];上海理工大學(xué)學(xué)報;2005年05期
10 唐俊;;基于動態(tài)模糊神經(jīng)網(wǎng)絡(luò)的建設(shè)工程造價估算系統(tǒng)[J];湖南城市學(xué)院學(xué)報(自然科學(xué)版);2008年04期
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