The Analytical Research on Construction Project Cost Estimation Model Which Is Based on Artificial Neural Network

2011 ◽  
pp. 129-134
Author(s):  
Junming Hong
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Cost Estimation ◽  
Construction Project ◽  
Estimation Model ◽  
Project Cost ◽  
Analytical Research ◽  
Artificial Neural ◽  
Cost Estimation Model ◽  
Construction Project Cost

Cost Estimation Model for I-Girder Bridge Superstructure Using Multiple Linear Regression and Artificial Neural Network

2018 ◽  
Vol 881 ◽  
pp. 142-149 ◽  
Author(s):  
Inas Winalytra ◽  
Arief Setiawan Budi Nugroho ◽  
Andreas Triwiyono
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Linear Regression ◽  
Cost Estimation ◽  
Estimation Model ◽  
Bridge Span ◽  
Factors Influencing ◽  
Cost Estimation Model ◽  
Girder Bridge ◽  
The Cost

One of the owner’s common problem before executing construction projects is the complexity in estimating the project cost in an early stage. Inaccurate cost estimation will force the owner to make further arrangement to the project budget. This study aims to develop an initial cost estimation model for superstructures of Precast I-girder Bridge. Cost estimation model was developed based on thirteen data of detail engineering design of I-girder bridge in Daerah Istimewa Yogyakarta (DIY). Factors influencing the cost of the superstructures of the I-girder bridge were identified. Bridge span and width, the size of the sidewalk, and railing’s type are considered as variables affecting the cost of superstructures. These variables are then arranged into two different analysis Multiple Linear Regression (MLR) analysis and Artificial Neural Network (ANN), in order to obtain the best estimation model. The results of the analysis showed that bridge span and width were the significant factors influencing cost. The correlation value of bridge span is 89.0%, bridge width is 74.2%, the size of the sidewalk is 66.1%, and railing’s type is 46.1% as identified factors that affect the cost of the superstructure. A comparative model of two approaches shows that the ANN has better accuracy than that of MLR, although the difference was not significant.

scholarly journals Research on Construction Cost Estimation based on Artificial Intelligence Technology

2021 ◽  
Vol 22 (2) ◽  
pp. 93-104
Author(s):  
Bin Wang ◽  
Jianjun Yuan ◽  
Kayhan Zrar Ghafoor
Keyword(s):  
Neural Network ◽  
System Theory ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Cost Estimation ◽  
Estimation Model ◽  
Project Cost ◽  
Construction Project Cost ◽  
Bp Neural Network Model

For the prediction of economic expenses involved in construction industry, cost estimation has become an important aspect of construction management for the prediction of economic expenses and successful completion of the construction work. Cost analysis is crucial and require expertise for accurate and comprehensive estimation. In order to effectively improve the accuracy of construction project cost, this paper establishes an estimation model based on gray BP neural network. It combines the MATLAB toolbox for program design, and learns and tests the input and output of training samples. This article determines the application of grey system theory to optimize the estimation model of Back Propagation (BP) neural network. The viability of the method established in this article, is tested by collecting the engineering cost data in Zhengzhou city and comparing between the standard BP neural network and the gray BP neural network methods. The results show that the average error of the gray system theory optimized BP neural network model designed in this paper is 2.33%. The gray BP neural network model studied in this paper can not only quickly estimate the project cost, but also has high accuracy rate. The outcomes obtained establishes a model with scientific and reasonable construction project cost estimation.

Practical Application Study of Gaussian Process Model in Construction Project Cost Estimation

2013 ◽  
Vol 671-674 ◽  
pp. 3100-3106
Author(s):  
Xin Liang Liu ◽  
Tao Yin ◽  
Guo Dong Wu
Keyword(s):  
Gaussian Process ◽  
Cost Estimation ◽  
Process Model ◽  
Critical Factor ◽  
Estimation Model ◽  
Project Cost ◽  
Gaussian Process Model ◽  
Model Based ◽  
Cost Estimation Model ◽  
Construction Project Cost

Early understanding of construction cost represents a critical factor of a feasibility study in the early design phase of a project. A new project cost estimation model based on Gaussian Process was proposed. Gaussian Process model theory was introduced, and project cost estimation model based on Gaussian Process’ flow chart was analyzed in detail. Through example analysis, project cost estimation model based on Gaussian Process using Nelder-Mead and genetic algorithms algorithm was proven feasible for this problem and represented accuracy than BP neural network.

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