Education, Science, Technology, Innovation and Life
Open Access
Sign In

Project Cost Prediction for Building Complexes Based on Grey BP Neural Network Model

Download as PDF

DOI: 10.23977/cpcs.2022.060201 | Downloads: 3 | Views: 139


Haochuan Jia 1


1 Zhejiang College of Security Technology, Wenzhou, Zhejiang, 325016, China

Corresponding Author

Haochuan Jia


Engineering cost prediction is a common topic in the construction field, but there is a problem of large prediction error. In order to avoid investment loss, a method of project cost prediction for building complex based on gray BP neural network model is designed. The construction area is divided into two factors: above-ground construction area and underground construction area, the cost influencing factors of the construction project are extracted, the density of data points at the center of the construction point is defined, the pricing model of the construction group project is optimized, the cost of similar projects is projected, and the cost prediction method is designed using the gray BP neural network model. Experimental results: The average values of relative errors of the designed construction group project cost prediction method and the other two construction group project cost prediction methods are: 3.596%, 6.505% and 6.213% respectively, indicating that the designed construction group project cost prediction method is more practical after combining with the gray BP neural network model.


Grey BP neural network, Building complex engineering, Cost prediction, Renovation project cost, Project management, Project quality


Haochuan Jia, Project Cost Prediction for Building Complexes Based on Grey BP Neural Network Model. Computing, Performance and Communication Systems (2022) Vol. 6: 1-9. DOI:


[1] Sdino L, Brambilla A, Dell'Ovo M, et al. Hospital Construction Cost Affecting Their Lifecycle: An Italian Overview[J]. Healthcare, 2021, 9(7):888.
[2] Oyieyo P A, Rambo C M, Ndiritu A. Ranking the prevalence of construction cost overrun risk factors in completion of public-private partnership projects[J]. International Journal of Research in Business and Social Science, 2020, 9(5):351-356.
[3] Jung S, Pyeon J H, Lee H S, et al. Construction Cost Estimation Using a Case-Based Reasoning Hybrid Genetic Algorithm Based on Local Search Method[J]. Sustainability, 2020, 12(19):7920.
[4] Alfraidi Y, Alzahrani S M, Binsarra F, et al. Impact of political risk on construction cost in PPP project in KSA[J]. International Journal of ADVANCED AND APPLIED SCIENCES, 2020, 7(5):6-11.
[5] Prokhorova Y S, Karakozova I V. Organizational Framework of Construction Cost Management in the Context of the Implementation of Targeted Investment Programs (Through the Example of Moscow)[J]. Economics and Management, 2020, 26(6):656-664.
[6] Yousif J H, Majeed S, Azzawi F. Web-Based Architecture for Automating Quantity Surveying Construction Cost Calculation[J]. Infrastructures, 2020, 5(6):45.
[7] Vm A, Shao Z B, Tlkr B. Early construction cost and time risk assessment and evaluation of large-scale underground cavern construction projects in Singapore - ScienceDirect[J]. Underground Space, 2020, 5(1):53-70.
[8] Musakanya M M. Construction Cost Estimates Related Risks[J]. International Journal of Sciences, 2020, 9(1):61-65.
[9] Kaiser M J, Liu M. Cost factors and statistical evaluation of gas transmission pipeline construction and compressor-station cost in the USA, 2014-2019[J]. International Journal of Oil Gas and Coal Technology, 2021, 26(4):422.
[10] Li Junda, Li Yuanfu, Wang Guangkai. A Highway Engineering Cost Prediction Model Based on CBR[J]. Journal of Highway and Transportation Research and Development, 2020, 37(6): 44-49, 67.
[11] Gu Runping, Lai Jinghan, Wei Zhiqiang. Prediction Method of Flightdelaybased on Grey GA-BPneural Network[J]. Computer Simulation, 2022, 39(5): 38-43, 59.
[12] Chai Zhijun, Ouyang Zhonghui, Yue Jiong. An Improved Prediction Model of Grey BP Neural Network[J]. Ordnance Industry Automation, 2020, 39(10): 84-88, 96.
[13] Feng Qianzhen, Huang Teng. Application of Gray BP Neural Network Combination Model in Dam Settlement Monitoring[J]. Journal of Gansu Sciences, 2020, 32(1): 14-17.
[14] Sun Chengsheng, Zhang Hongmin, Wang Yan, et al. An Improved Prediction Method of Photovoltaic Output Based on Grey BP Neural Network[J]. Journal of Chongqing Institute of Technology, 2020, 34(2): 217-221.
[15] Jin Lin, Ma Zhongyun, Wang Honghong. Forecasting of Carbon Emission Trading Price based on Grey BP Neural Network Model[J]. Journal of HeiBei University of Environmental Engineering, 2020, 30(1): 27-32, 41.

Downloads: 1477
Visits: 66017

Sponsors, Associates, and Links

All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.