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Construction project risk prediction model based on EW-FAHP and one dimensional convolution neural network

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  • Yawen Zhong
  • Hailing Li
  • Leilei Chen

Abstract

In order to solve the problem of low accuracy of traditional construction project risk prediction, a project risk prediction model based on EW-FAHP and 1D-CNN(One Dimensional Convolution Neural Network) is proposed. Firstly, the risk evaluation index value of construction project is selected by literature analysis method, and the comprehensive weight of risk index is obtained by combining entropy weight method (EW) and fuzzy analytic hierarchy process (FAHP). The risk weight is input into the 1D-CNN model for training and learning, and the prediction values of construction period risk and cost risk are output to realize the risk prediction. The experimental results show that the average absolute error of the construction period risk and cost risk of the risk prediction model proposed in this paper is below 0.1%, which can meet the risk prediction of construction projects with high accuracy.

Suggested Citation

  • Yawen Zhong & Hailing Li & Leilei Chen, 2021. "Construction project risk prediction model based on EW-FAHP and one dimensional convolution neural network," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-20, February.
    • Handle: RePEc:plo:pone00:0246539
    DOI: 10.1371/journal.pone.0246539
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    1. Jianghong Feng, 2022. "An integrated multi-criteria decision-making method for hazardous waste disposal site selection," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(6), pages 8039-8070, June.

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