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Xin Lin, Yinan Lu, "Research on Mathematical Model of Cost Budget in the Early Stage of Assembly Construction Project Based on Improved Neural Network Algorithm", Mathematical Problems in Engineering, vol. 2020, Article ID 3674929, 7 pages, 2020. https://doi.org/10.1155/2020/3674929
Research on Mathematical Model of Cost Budget in the Early Stage of Assembly Construction Project Based on Improved Neural Network Algorithm
Abstract
In view of the poor performance of the original mathematical model of assembly construction project precost budget, a mathematical model of assembly construction project precost budget based on improved neural network algorithm is proposed. This paper investigates the cost content of assembly construction project and analyzes its early cost. It finds that the early cost of assembly construction project includes component production cost, transportation component cost, and installation component cost. Based on the improved neural network algorithm to build an improved neural network model, the improved neural network model to mine the cost data in the early stage of assembly construction project is used. In this paper, the earned value variable is introduced to transform the project duration and project cost in the early stage of the prefabricated construction project into quantifiable cost data, and the earned value analysis method is used to estimate the implementation cost of the prefabricated construction project. According to the result of cost estimation, the mathematical model of precost budget of prefabricated construction project is built based on the project parameters. In order to prove that the cost budget performance of the mathematical model based on the improved neural network algorithm in the early stage of assembly construction project is better, the original mathematical model is compared with the mathematical model, the experimental results show that the cost budget performance of the model is better than the original model, and the cost budget performance is improved.
1. Introduction
In traditional construction projects, the pouring of concrete mainly adopts manual scaffolding, formwork supporting, and binding of steel bars at site. However, such kind of castinplace can cause a variety of problems, including making construction site in a mess, generating lots of construction waste, and polluting the surrounding environment [1]. Meanwhile, with the progress of population aging and the increase of labor costs in China, this extensive construction model is no longer suitable for the green, energysaving, and environmentally friendly concepts. Under this circumstance, the prefabricated construction project emerged at the right moment and eased this dilemma. As a new building construction technology, prefabricated construction project means to process and produce the prefabricated components in factory, move them to the construction site after maintenance, and then assemble the building components accordingly through machinery equipment to achieve the building functional requirements [2]. Compared with the castinplace construction method, the prefabricated construction project can save about 20% of materials and 80% of water resources in manufacturing and processing building components. At the same time, in prefabricated construction project, protective nets and scaffolding are not used during construction, which reduced construction waste and lowered the pollution and harm to the environment [3].
Under the trend of urbanization, people have put forward higher requirements for environmental protection and energysaving performance of buildings. Therefore, prefabricated buildings are getting popular, have achieved rapid development in China, and promoted the improvement of the installation quality and component precision of prefabricated components [4]. However, based on the current construction market, the overall production scale of prefabricated buildings is restricted so that it is unable to reduce the construction cost budget through expanding the scale, which limited the development of prefabricated buildings in China and affected the industrialization process of construction field. At its initial stage of development, prefabricated buildings in China are still facing many shortcomings in technology, experience, and cost control [5]. In order to accelerate the development of prefabricated buildings, an innovative research on the mathematical model of cost budget in early stage of prefabricated construction is carried out. On this basis, a mathematical model of cost budget in the early stage of prefabricated construction project based on improved neural network algorithm is proposed [6].
2. Design of Mathematical Model of Cost Budget in the Early Stage of Prefabricated Construction Project Based on Improved Neural Network Algorithm
2.1. Analysis of Cost in the Early Stage of Prefabricated Construction Project
As found in the cost analysis in early stage of the prefabricated construction project, the preliminary cost of prefabricated construction project includes component production cost, component transportation cost, and component installation cost. Among them, statistics found that component production cost contains labor cost, material cost, mold cost, amortization expense, cost of setting the embedded parts and pipelines, management and storage cost, and water and electricity charges. The specific contents are shown in Table 1 [7].

Component transportation cost covers the cost of transporting components from the factory to the construction site, which is directly related to the size and weight of the components, as well as the distance between the factory and the construction site [8].
Component installation costs involves the cost of vertical component transportation, labor cost of component installation, machinery cost of component installation, material cost of component installation, castinplace cost, and amortization cost, as shown in Table 2 [9].

2.2. Data Mining of Cost in the Early Stage of Prefabricated Construction Project
As shown in the analysis of cost budget in early stage of prefabricated construction project, an improved neural network model is built based on the improved neural network algorithm so as to conduct data mining on the cost budget in early stage of prefabricated construction project [10]. The specific steps are as follows:(1)First, the improved neural network is initialized: is adopted to represent the input and output sequence of the improved neural network. Based on this sequence, the specific number of nodes corresponding to the output layer, hidden layer, and input layer of the improved neural network is clarified, which is , , and nodes, respectively. Next, the threshold and the connection weight are initialized. The neuron connection weight between the hidden layer and the input layer is set to ; the neuron connection weight between the output layer and the hidden layer is set to ; the thresholds of the output layer and hidden layer is and , respectively. Finally, the learning rate of the improved neural network is set to , and the excitation neuron function is set to [11].(2)The specific output of the hidden layer is computed: the specific output of the hidden layer is obtained based on the specific input variable of the improved neural network, the threshold value of the hidden layer, and the neuron connection weight between the hidden layer and the input layer. The details are as follows: In formula 1, represents the specific output of the hidden layer, represents the th input value, represents the threshold of the th hidden layer, and represents the specific number of nodes of the hidden layer [12].(3)The specific output of the input layer is computed: the specific output of the input layer is obtained according to the specific output of the hidden layer, the threshold value of the output layer, and the neuron connection weight between the output layer and the hidden layer: In forrmula 2, represents the specific output of the input layer and represents the threshold of the th output layer [13].(4)Error calculation: the specific error of the improved neural network is predicted based on the specific output of the input layer and the specific expected output of the improved neural network.(5)The neuron connection weight between the hidden layer and the input layer, and the neuron connection weight between the output layer and the hidden layer are updated according to the specific predicted error of the improved neural network.(6)Threshold updating: the thresholds and of the output layer and hidden layer are updated according to the specific predicted error of the improved neural network.(7)The construction of improved neural network model is realized and the data mining on the cost in early stage of prefabricated construction project is performed on this basis [14].
2.3. Cost Budget in Early Stage of Prefabricated Construction Project
Based on the data mining of cost in early stage of prefabricated construction project, a variable called earned value is introduced to convert the project duration and project cost in early stage of prefabricated construction into quantifiable cost data. Meanwhile, the cost in early stage of prefabricated construction project is estimated through the earned value analysis so as to carry out the cost budget of prefabricated construction project [15]. The calculation formula of earned value is as follows:
In formula 3, represents earned value, represents the project actual workload and represents the cost budget of the completed project.
For the analysis by using the earned value, the difference variables of two analyzes must be obtained at first, including schedule deviation and cost deviation. Their calculation formulas are separately as follows:
In formula 4, represents the schedule deviation and represents the cost budget of planned project volume.
In formula 5, represents cost deviation and represents the specific cost of the completed project volume [16].
Along with two variable indexes, including performance progress index and performance cost index, the calculation formulas are as follows:
In formula 6, represents the performance progress index:
In formula 7, represents the performance cost index.
The data of the cost budget of completed project volume, the specific cost of completed project volume, and the cost budget of planned project volume are added separately [17] in analysis so that 3 corresponding cumulative series are obtained. By inputting data of cost budget of completed project volume, the specific cost of completed project volume and the cost budget of planned project volume into a twodimensional coordinate axis of time and cost, and 3 analysis curves are obtained and applied to analyze the period and cost in early stage of prefabricated construction project. Among them, when the cost deviation is greater than 0, it indicates that the early stage of prefabricated construction project is in a costsaving state; when the cost deviation is less than 0, it indicates that the early stage of prefabricated construction project is in the overcost state; when the progress deviation is greater than 0, it indicates that the early stage of prefabricated construction project is in a state of advanced progress; when the progress deviation is less than 0, it indicates that the early stage of prefabricated construction project is in a state of delayed progress. The cost of prefabricated buildings can be estimated by inputting the construction period and cost analysis results of early stage of the prefabricated construction project as well as the actual status of the early stage of project into the project management software.
2.4. Mathematical Model of Cost Budget in Early Stage of Prefabricated Construction Project
According to the estimated cost in the early stage of prefabricated construction project, the mathematical model of cost budget in early stage of prefabricated construction project is constructed based on project parameters. The items of material budget in early stage of prefabricated construction project are shown in Table 3.
 
 
The items of machinery budget in early stage of prefabricated construction project are shown in Table 5. 

The project parameters are described according to the type of project, in which the parameter of production progress in the early stage of prefabricated construction project is set to , the parameter of production calculation period is set to , the parameter of periodic component production batch is set to , labor demand cost is set to , machinery demand cost is set to , other expenses such as management fee are set to , demand machinery value is set to , site cost is set to , equipment rental cost is set to , the upper limit of transportation cost is set to , and the assembly cost is set to .
The production process in early stage of prefabricated construction project can be summarized as follows: the first if component production, followed by component assembly and transportation. In this way, the cost in early stage of prefabricated construction project includes construction production cost, component assembly, and transportation fee [19]. Therefore, by setting parameters of production progress in early stage of prefabricated construction project as independent variables and the minimum budget cost as the model objective function, then the mathematical model of cost budge in the early stage of prefabricated construction projects is established as follows [20]:
In formula 8, represents the minimum cost budget in the early stage.
3. Experimental Research and Result Analysis
3.1. Experiment Design
The experiment of cost budget in early stage of prefabricated construction project is carried out by using the mathematical model of cost budget in early stage of prefabricated construction project designed based on improved neural network algorithm. With a total area of 920 square meters, the prefabricated construction project in this experiment contains 14 floors in prefabricated structures of assembled shear wall. The prefabricated component nodes are manufactured by secondary castinplace. The outer wall of the building is made of prefabricated thermal insulation Sandwich panel; the floor is made of concrete prestressed composite slab; the loadbearing wall is made of shear prefabricated wall panel; the staircase is prefabricated; the inner partition wall is made of lightweight wall panel. The prefabricated parts involved in this prefabricated construction project are listed as follows: prefabricated parts for stair, prefabricated parts for laminated panel, prefabricated parts for partition wall, shear wall, etc. Considering that it is a prefabricated construction project, all the prefabricated parts are produced at the prefabricated production base and then transported to the construction site after maintenance. Meanwhile, the transportation and production of prefabricated parts for different places and floors are arranged separately according to the specific project progress.
The specific building parameters of this prefabricated building project are shown in Table 6.
 
The specific contracting situation of this prefabricated construction project is shown in Table 7. 

In this prefabricated construction project, all the floors are standard, and the early stage is set to a sixday construction period per floor. According to the building structure and engineering quantity, the construction sequence is arranged reasonably and the whole project is divided into three phases. The specific arrangements for the construction of each floor are as follows: hoisting 60 pieces of wall in the first construction phase, hoisting 60 pieces of wall in the second construction phase, grouting sleeve in the first construction phase, grouting sleeve in the second construction phase, hoisting 30 pieces of wall in the third construction phase, grouting sleeves in the third construction phase, binging steel bars with postcasting belts, reinforcing formwork with postcasting belts, erecting supportive frames, hoisting composite beams, hoisting stairs, hoisting of 60 pieces of composite slabs, hoisting 57 pieces of composite slabs, preburying hydropower and other pipelines, binding the upper stair reinforcement, supporting formwork joints, and pouring concrete. The cost budget in early stage of this prefabricated building construction is estimated through the mathematical model. In order to ensure the effectiveness and contrast of this experiment, the original mathematical model of cost budget in the early stage of prefabricated construction project is compared to the mathematical model of cost budget in the early stage of prefabricated construction project designed based on the improved neural network algorithm in this paper. Among them, the original mathematical model of cost budget in early stage of prefabricated construction project includes the mathematical model of cost budget in early stage of prefabricated construction project based on cost control, random function, and system dynamics. As learned from comparing the performance of different mathematical models of cost budget in early stage of prefabricated construction project, that is, from analyzing the cost budget accuracy of the experimental model, the higher the cost budget accuracy, the more reasonable the cost budget result of the prefabricated construction project and the better cost budget performance [21].
3.2. Analysis Results
The result of comparative experiment on cost budget performance between the original mathematical model of cost budget in early stage of prefabricated construction project and the mathematical model of cost budget in early stage of prefabricated construction project designed based on the improved neural network algorithm is shown in Figure 1.
According to the result of comparative experiment on cost budget performance between two different mathematical models, the mathematical model of cost budget in early stage of prefabricated construction project designed based on the improved neural network algorithm is better in cost budget accuracy. That means the cost budget performance of the mathematical model of cost budget in early stage of prefabricated construction project based on the improved neural network algorithm is superior to that of the original mathematical model of cost budget in early stage of prefabricated construction project.
4. Conclusions
The cost budget performance of the mathematical model of cost budget in the early stage of prefabricated construction project based on the improved neural network algorithm can realize the improvement of cost budget performance in the early stage of prefabricated construction project, which is of great reference significance to accurate cost budget of the overall prefabricated construction project.
Data Availability
Simulation data and our model and related hyperparameters used are provided within the article.
Conflicts of Interest
The authors declare that they have no conflicts of interest.
Acknowledgments
This work was supported by the Chongqing Municipal Education Commission Science and Technology Research Project (KJQN201904003) and research project of Chongqing Technology and Business Institute (ZD201604).
References
 G. E. Gurcanli, S. Bilir, and M. Sevim, “Activity based risk assessment and safety cost estimation for residential building construction projects,” Safety Science, vol. 80, pp. 1–12, 2015. View at: Publisher Site  Google Scholar
 Y. T. Chae, R. Horesh, Y. Hwang, and Y. M. Lee, “Artificial neural network model for forecasting subhourly electricity usage in commercial buildings,” Energy and Buildings, vol. 111, pp. 184–194, 2016. View at: Publisher Site  Google Scholar
 M.Y. Cheng, H.C. Tsai, and E. Sudjono, “Conceptual cost estimates using evolutionary fuzzy hybrid neural network for projects in construction industry,” Expert Systems with Applications, vol. 37, no. 6, pp. 4224–4231, 2010. View at: Publisher Site  Google Scholar
 K. H. Hyari, A. AlDaraiseh, and M. ElMashaleh, “Conceptual cost estimation model for engineering services in public construction projects,” Journal of Management in Engineering, vol. 32, Article ID 04015021, 2016. View at: Publisher Site  Google Scholar
 B. Pal, A. Mhashilkar, A. Pandey, B. Nagphase, and V. Chandanshive, “Cost estimation model (CEM) of buildings by ANN (artificial neural networks)–A review,” Neural Networks, vol. 5, pp. 1–15, 2018. View at: Google Scholar
 J. Liu, X. Li, D. Wu, and J. Dong, “Cost estimation of building individual cooperative housing with crowdfunding model: case of Beijing, China,” Journal of Intelligent Manufacturing, vol. 28, no. 3, pp. 749–757, 2017. View at: Publisher Site  Google Scholar
 H. Piili, A. Happonen, T. Väistö, V. Venkataramanan, J. Partanen, and A. Salminen, “Cost estimation of laser additive manufacturing of stainless steel,” Physics Procedia, vol. 78, pp. 388–396, 2015. View at: Publisher Site  Google Scholar
 O. Tatari and M. Kucukvar, “Cost premium prediction of certified green buildings: a neural network approach,” Building and Environment, vol. 46, no. 5, pp. 1081–1086, 2011. View at: Publisher Site  Google Scholar
 S. C. Lhee, I. Flood, and R. R. Issa, “Development of a twostep neural networkbased model to predict construction cost contingency,” Journal of Information Technology in Construction (ITcon), vol. 19, pp. 399–411, 2014. View at: Google Scholar
 V. Chandanshive and A. R. Kambekar, “Estimation of building construction cost using artificial neural networks,” Journal of Soft Computing in Civil Engineering, vol. 3, pp. 91–107, 2019. View at: Google Scholar
 J. A. Rodger, “A fuzzy nearest neighbor neural network statistical model for predicting demand for natural gas and energy cost savings in public buildings,” Expert Systems with Applications, vol. 41, no. 4, pp. 1813–1829, 2014. View at: Publisher Site  Google Scholar
 G. Ngowtanasuwan, “Mathematical model for optimization of construction contracting in housing development project,” ProcediaSocial and Behavioral Sciences, vol. 105, pp. 94–105, 2013. View at: Publisher Site  Google Scholar
 D.K. Bui, T. Nguyen, J.S. Chou, H. NguyenXuan, and T. D. Ngo, “A modified firefly algorithmartificial neural network expert system for predicting compressive and tensile strength of highperformance concrete,” Construction and Building Materials, vol. 180, pp. 320–333, 2018. View at: Publisher Site  Google Scholar
 E. Asadi, M. G. d. Silva, C. H. Antunes, L. Dias, and L. Glicksman, “Multiobjective optimization for building retrofit: a model using genetic algorithm and artificial neural network and an application,” Energy and Buildings, vol. 81, pp. 444–456, 2014. View at: Publisher Site  Google Scholar
 M. Ceylan, M. H. Arslan, R. Ceylan, M. Y. Kaltakci, and Y. Ozbay, “A new application area of ANN and ANFIS: determination of earthquake load reduction factor of prefabricated industrial buildings,” Civil Engineering and Environmental Systems, vol. 27, no. 1, pp. 53–69, 2010. View at: Publisher Site  Google Scholar
 A. Nasirian, M. Arashpour, B. Abbasi, and A. Akbarnezhad, “Optimal work assignment to multiskilled resources in prefabricated construction,” Journal of Construction Engineering and Management, vol. 145, pp. 4019–4034, 2019. View at: Publisher Site  Google Scholar
 H. Quan, D. Srinivasan, and A. Khosravi, “Particle swarm optimization for construction of neural networkbased prediction intervals,” Neurocomputing, vol. 127, pp. 172–180, 2014. View at: Publisher Site  Google Scholar
 H.L. Yip, H. Fan, and Y.H. Chiang, “Predicting the maintenance cost of construction equipment: comparison between general regression neural network and BoxJenkins time series models,” Automation in Construction, vol. 38, pp. 30–38, 2014. View at: Publisher Site  Google Scholar
 J. Sobhani, M. Najimi, A. R. Pourkhorshidi, and T. Parhizkar, “Prediction of the compressive strength of noslump concrete: a comparative study of regression, neural network and ANFIS models,” Construction and Building Materials, vol. 24, no. 5, pp. 709–718, 2010. View at: Publisher Site  Google Scholar
 R. Sonmez, “Range estimation of construction costs using neural networks with bootstrap prediction intervals,” Expert Systems with Applications, vol. 38, no. 8, pp. 9913–9917, 2011. View at: Publisher Site  Google Scholar
 A. O. Elfaki, S. Alatawi, and E. Abushandi, “Using intelligent techniques in construction project cost estimation: 10year survey,” Advances in Civil Engineering, vol. 2014, pp. 1023–1031, 2014. View at: Google Scholar
Copyright
Copyright © 2020 Xin Lin and Yinan Lu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.