基于支持向量机的铁路隧道洞身工程造价预测

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Abstract Research purposes: The accuracy of the preliminary investment estimation of railway engineering projects plays a vital role in the comparison and selection of railway construction projects and investment control. At present, our country's railway engineering investment estimation is mainly carried out by the unit index method and the budget quota method. However, the whole process is relatively complicated and time-consuming, and the accuracy of the estimation also depends on the working experience and ability of the practitioners. In this paper, machine learning algorithms is used, main tunnel in railway tunnel engineering as the research object, to build a cost prediction model based on support vector machine (SVM) and extreme learning machine (ELM), and collect several tunnel samples to train and test the model. Through actual verification and comparison, a more applicable algorithm is selected, which can use historical data to quickly predict the cost of the main tunnel, thereby improving investment estimation and program comparison precision and efficiency.
Research conclusions: (1) The result shows that SVM has high prediction accuracy and stability, compared with the ELM algorithm, and the mean absolute percentage error (MAPE) of the prediction results is only 3.41%, which meets the accuracy requirements. (2) The research results can provide a new and intelligent data-driven modeling method for the cost evaluation and prediction of main tunnel in railway tunnel engineering, and the simulation results show that the model has good feasibility and applicability.

Key words： support vector machine extreme learning machine railway tunnel construction cost prediction

Received: 01 February 2021

PACS:

U212.32

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	LIU Shaofei
	HOU Dashan

Cite this article:

LIU Shaofei,HOU Dashan. Construction Cost Prediction of Main Tunnel in Railway Tunnel Based on Support Vector Machine[J]. Journal of Railway Engineering Society, 2022, 39(5): 108-114.

URL:

https://tdgcxb.crec.cn/EN/ OR https://tdgcxb.crec.cn/EN/Y2022/V39/I5/108

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