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.

Cost Estimation Model Based on Improved Spreadsheet Algorithm for Assembly Line Manufacturing Systems

2012 ◽  
Vol 490-495 ◽  
pp. 2173-2177
Author(s):  
Bin Zeng ◽  
Rui Wang ◽  
Chao Yang Ma
Keyword(s):  
Cost Estimation ◽  
Manufacturing Systems ◽  
Cost Model ◽  
Manufacturing Cost ◽  
Cost Models ◽  
Estimation Model ◽  
Stochastic Variation ◽  
Virtual Plant ◽  
Model Based ◽  
Cost Estimation Model

Traditionally assembly cost models are established through static spreadsheet algorithms. However, there are some inherent problems in using spreadsheets for the estimation of manufacturing cost. Among these is the lack of accounting for dynamic effects caused by stochastic variation such as inventory fluctuation, downtimes, supply interruptions, and system failures. Therefore, a dynamic cost estimation model is proposed which can be seen as an integration method between spreadsheet modeling and the virtual plant concept, which maintained the accessibility and flexibility of the spreadsheet model, and did not require a significant increase in the effort level to build a simulation. However, it still includes the effects of interaction between machines, along with simulating random failures, maintenance dispatch and repair. A case study is also tested and the results verify that the methodology demonstrates the feasibility of dynamic cost model based on a number of improvements on static spreadsheet algorithms

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.

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