The Fast Estimation Model of Project Cost Based on BP Neural Network

2013 ◽  
Author(s):  
Wang Zhihong ◽  
Wang He ◽  
Wang Yan
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Estimation Model ◽  
Project Cost ◽  
Fast Estimation

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.

Estimating nitrogen contents of apple leaves based on hyperspectral parameter models at different phenophases

2016 ◽  
Vol 6 (2) ◽  
pp. 942-952
Author(s):  
Xicun ZHU ◽  
Zhuoyuan WANG ◽  
Lulu GAO ◽  
Gengxing ZHAO ◽  
Ling WANG
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Shoot Growth ◽  
Hyperspectral Data ◽  
Estimation Model ◽  
Apple Leaves ◽  
Bp Neural Network Model ◽  
Nitrogen Contents

The objective of the paper is to explore the best phenophase for estimating the nitrogen contents of apple leaves, to establish the best estimation model of the hyperspectral data at different phenophases. It is to improve the apple trees precise fertilization and production management. The experiments were done in 20 orchards in the field, measured hyperspectral data and nitrogen contents of apple leaves at three phenophases in two years, which were shoot growth phenophase, spring shoots pause growth phenophase, autumn shoots pause growth phenophase. The study analyzed the nitrogen contents of apple leaves with its original spectral and first derivative, screened sensitive wavelengths of each phenophase. The hyperspectral parameters were built with the sensitive wavelengths. Multiple stepwise regressions, partial least squares and BP neural network model were adopted in the study. The results showed that 551 nm, 716 nm, 530 nm, 703 nm; 543 nm, 705 nm, 699 nm, 756 nm and 545 nm, 702 nm, 695 nm, 746 nm were sensitive wavelengths of three phenophases. R551+R716, R551*R716, FDR530+FDR703, FDR530*FDR703; R543+R705, R543*R705, FDR699+FDR756, FDR699*FDR756and R545+R702, R545*R702, FDR695+FDR746, FDR695*FDR746 were the best hyperspectral parameters of each phenophase. Of all the estimation models, the estimated effect of shoot growth phenophase was better than other two phenophases, so shoot growth phenophase was the best phenophase to estimate the nitrogen contents of apple leaves based on hyperspectral models. In the three models, the 4-3-1 BP neural network model of shoot growth phenophase was the best estimation model. The R2 of estimated value and measured value was 0.6307, RE% was 23.37, RMSE was 0.6274.

scholarly journals Research on Substation Project Cost Prediction Based on Sparrow Search Algorithm Optimized BP Neural Network

2021 ◽  
Vol 13 (24) ◽  
pp. 13746
Author(s):  
Xiaomin Xu ◽  
Luyao Peng ◽  
Zhengsen Ji ◽  
Shipeng Zheng ◽  
Zhuxiao Tian ◽  
...  
Keyword(s):  
Neural Network ◽  
Influencing Factors ◽  
Bp Neural Network ◽  
Prediction Accuracy ◽  
Search Algorithm ◽  
Power Grid ◽  
Engineering System ◽  
Project Cost ◽  
Total Investment ◽  
Engineering Cost

The prediction of power grid engineering cost is the basis of fine management of power grid engineering, and accurate prediction of substation engineering cost can effectively ensure the fine operation of engineering funds. With the continuous expansion of the engineering system, the influencing factors and data dimensions of substation project investment are gradually diversified and complex, which further increases the uncertainty and complexity of substation project cost. Based on the concept of substation engineering data space, this paper investigates the influencing factors and constructs the static total investment intelligent prediction model of substation engineering. The emerging swarm intelligence algorithm, sparrow search algorithm (SSA), is used to optimize the parameters of the BP neural network to improve the prediction accuracy and convergence speed of neural network. In order to test the validity of the model, an example analysis is carried out based on the data of a provincial substation project. It was found that the SSA-BP can effectively improve the prediction accuracy and provide new methods and approaches for practical application and research.

Estimation of construction project building cost by back-propagation neural network

2019 ◽  
Vol 18 (3) ◽  
pp. 601-609
Author(s):  
Qinghua Jiang
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Bp Neural Network ◽  
Cost Estimation ◽  
Back Propagation ◽  
Estimation Accuracy ◽  
Average Error ◽  
Estimation Model ◽  
Content Type ◽  
Building Cost

Purpose Building cost is an important part of construction projects, and its correct estimation has important guiding significance for the follow-up decision-making of construction units. Design/methodology/approach This study focused on the application of back-propagation (BP) neural network in the estimation of building cost. First, the influencing factors of building cost were analyzed. Six factors were selected as input of the estimation model. Then, a BP neural network estimation model was established and trained by ten samples. Findings According to the experimental results, it was found that the estimation model converged at about 85 times; compared with radial basis function (RBF), the estimation accuracy of the model was higher, and the average error was 5.54 per cent, showing a good reliability in cost estimation. Originality/value The results of this study provide a reliable basis for investment decision-making in the construction industry and also contribute to the further application of BP neural network in cost estimation.

Scientific Research Project Cost Estimating Method and System Based on Improved BP Neural Network

2013 ◽  
Vol 756-759 ◽  
pp. 1696-1700 ◽  
Author(s):  
Yi Lin Wang ◽  
Guo Xin Wang ◽  
Yan Yan
Keyword(s):  
Neural Network ◽  
Influencing Factors ◽  
Bp Neural Network ◽  
Scientific Research ◽  
Cost Estimating ◽  
Research Project ◽  
Project Cost ◽  
Model Based ◽  
Scientific Research Project ◽  
Testing Experiment

Traditional scientific research project cost estimating method cannot meet accuracy and practicability at the same time. Aiming at this problem, scientific research project cost estimating method based on neural network was built. Firstly, the construction and influencing factors of scientific research project cost were analyzed. Secondly, an estimating model based on improved BP neural network was built; a nonlinear expression between influencing factors (input) and cost (output) was created. Finally, an estimating system with the model was implemented by Java. The effectiveness of the method was tested. Testing experiment showed the estimating model based on improved BP neural network is reliable and the precision is high.

Engine Torque Estimation Based on BP Neural Network

2011 ◽  
Vol 403-408 ◽  
pp. 2848-2851
Author(s):  
Kai Sheng Huang ◽  
Dong Liang Wang ◽  
Zhi Hua Lin ◽  
Xiang Rui Zeng
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Estimation Method ◽  
Time Estimation ◽  
Torque Control ◽  
Steady Condition ◽  
Estimation Model ◽  
Engine Torque ◽  
Unsteady Condition ◽  
Torque Estimation

Engine torque estimation function is the base of engine torque control. This paper establishes the model for engine torque estimation respectively under steady condition and unsteady condition based on BP Neural network, and develops a new engine torque real-time estimation method. The experiment results under steady condition and unsteady condition show that the engine torque estimation model can estimate the engine output torque and the precision is remarkable.

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