海外铁路建设项目造价人工智能估算方法研究

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Abstract Research purposes: The investment estimation of overseas railway construction projects urgently needs a valuation system integrating reliable historical data and scientific estimation methods to solve the problem of project losses due to the low efficiency and accuracy of investment estimation. This paper uses nonlinear methods such as PSO (Particle Swarm Optimization) and BPNN (Back Propagation Neural Network) to establish an intelligent estimation model for overseas railway investment to make up for the shortcomings of traditional estimation methods such as strong subjectivity and low reliability.
Research conclusions:(1) 19 factors affecting the overseas railway investment estimation are identified, including construction market and geological conditions. (2) It is found that the nonlinear methods such as PSO and BPNN can be well applied to the study of overseas railway investment estimation. (3) Through empirical research, the error of the model established in this paper is controlled within 6.44%, with high accuracy, which can be popularized and applied to the investment estimation of subway and high-speed railway in China. (4) This research can provide reference for other overseas railway construction project cost estimation research.

Key words： overseas railway BPNN PSO intelligence estimate

Received: 30 June 2021

PACS:

F282

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	Articles by authors
	DUAN Xiaochen
	BAO Mo
	NIU Yanliang
	LEI Peijun

Cite this article:

DUAN Xiaochen,BAO Mo,NIU Yanliang, et al. Cost Estimation Method for Overseas Railway Construction Project Based on Artificial Intelligence[J]. Journal of Railway Engineering Society, 2022, 39(9): 112-118.

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https://tdgcxb.crec.cn/EN/ OR https://tdgcxb.crec.cn/EN/Y2022/V39/I9/112

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