Jiang, Q (2020) Estimation of construction project building cost by back-propagation neural network. Journal of Engineering, Design and Technology, 18(3), pp. 601-609. ISSN 1726-0531
Abstract
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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | back-propagation neural network; building cost; cost estimation; estimation model; influence factors |
| Index terms: | influencing factor, estimation, investment decision-making, propagation, bp neural network, construction industry, cost estimating, neural network, accuracy, construction project, decision-making, methodology |
| Subjects: | cognitive psychology, decision analysis, industry analysis, research methods, production management, professional development, artificial intelligence, financial and cost management, risk assessment, engineering process |
| Topics: | Engineering Principles, Information Management, Research Practice, Project Management, Cost Management, Business Strategy, Risk Management, Digital Applications |
| Descriptive scope: | 3 PCT |
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