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Design of building construction safety prediction model based on optimized BP neural network algorithm

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Abstract

In order to solve the safety problem of the construction industry, the construction safety prediction model based on the optimized BP neural network algorithm is designed in this study. First, the characteristics of the construction industry were analyzed. As a labor-intensive industry, the construction industry is characterized by numerous factors such as large investment, long construction period and complicated construction environment. Due to the increasingly serious security problem, widespread concern over such problem has been aroused in society. Second, the problem of building construction safety management was summarized, six influencing factors were explored and a building construction safety prediction model based on rough set-genetic-BP neural network was established. Finally, the model was validated by a combination of multiparty consultation, empirical analysis and model comparison. The results showed that the model accurately predicted the risk factors during the construction process and effectively reduced casualties. Therefore, the model is feasible, effective and accurate.

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Authors and Affiliations

  1. Knowledge Science, Japan Advanced Institute of Science and Technology, Nomi City, Ishikawa, Japan

    Tao Shen & Yukari Nagai

  2. Architecture Department, Huzhou University, Huzhou, Zhejiang Province, China

    Chan Gao

Authors
  1. Tao Shen
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  2. Yukari Nagai
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  3. Chan Gao
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Correspondence to Tao Shen.

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Informed consent was obtained from all individual participants included in the study.

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Communicated by Mu-Yen Chen.

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Shen, T., Nagai, Y. & Gao, C. Design of building construction safety prediction model based on optimized BP neural network algorithm. Soft Comput 24, 7839–7850 (2020). https://doi.org/10.1007/s00500-019-03917-4

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