Zhou, J., Koopialipoor, M., Li, E. and Armaghani, D.J. (2020) Prediction of Rockburst Risk in Underground Projects Developing a Neuro-Bee Intelligent System. Bulletin of Engineering Geology and the Environment, 79, 4265-4279.
https://doi.org/10.1007/s10064-020-01788-w

has been cited by the following article:

• TITLE: Risk Assessment and Prediction of Construction Project Based on 1D-CNN-Attention-BP

  AUTHORS: Yawen Zhong

  KEYWORDS: Construction Project Risk, 1D-CNN-Attention-BP, One Dimensional Convolutional Neural Network, Construction Period Forecast, Risk Identification

  JOURNAL NAME: World Journal of Engineering and Technology, Vol.9 No.4, November 17, 2021

  ABSTRACT: In order to solve the problem of low accuracy of construction project duration prediction, this paper proposes a CNN attention BP combination modelproject risk prediction model based on attention mechanism, one-dimensionalconvolutional neural network (1d-cnn) and BP neural network. Firstly, the literature analysis method is used to select the risk evaluation index value of construction project, and the attention mechanism is used to determine the weight of risk factors on construction period prediction; then, BP neural network is used to predict the project duration, and accuracy, cross entropy loss function and F1 score are selected to comprehensively evaluate the performance of 1d-cnn-attention-bp combined model. The experimental results show that the duration risk prediction accuracy of the risk prediction model proposed in this paper is more than 90%, which can meet the risk prediction of construction projects with high accuracy.