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Risk management system and intelligent decision-making for prefabricated building project under deep learning modified teaching-learning-based optimization

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  • Huazan Liu
  • Yukang He
  • Qichao Hu
  • Jianfei Guo
  • Lan Luo

Abstract

This study establishes a model of prefabricated building project risk management system based on the Modified Teaching-Learning-Based-Optimization (MTLBO) algorithm and a prediction model of deep learning multilayer feedforward neural network (Backpropagation, BP neural network) to improve the requirements of risk management during the construction of large prefabricated building projects. First, we introduced the BP neural network algorithm based on deep learning. Second, the traditional Teaching-Learning-Based Optimization (TLBO) algorithm was modified by using information entropy, and the modified algorithm was simulated and tested in five test functions. Then, based on the BP neural network and MTLBO algorithm, we established the MTLBO-BP neural network prediction model and tested its performance. Finally, based on the MTLBO-BP neural network prediction model, MATLAB software was used to establish an intelligent model of the risk management system during the construction of prefabricated building projects, and the example verification was performed. In addition, the MTLBO algorithm was verified by test function simulation and established that global searchability is stronger than the TLBO algorithm. Of note, it is not easy to fall into a local optimum. The test results of the MTLBO-BP neural network prediction model revealed that the prediction model converges faster and exerts a better prediction effect. The example verification of the intelligent model of the risk management system during the construction of prefabricated building projects established in this study revealed that the algorithm proposed is more accurate in the reliability and cost prediction of the risk management of prefabricated building projects. Moreover, the algorithm proposed provides theoretical support for intelligent management and decision-making of prefabricated building projects. Overall, this study validates that this algorithm is essential for construction project management, decision-making, and quality assurance.

Suggested Citation

  • Huazan Liu & Yukang He & Qichao Hu & Jianfei Guo & Lan Luo, 2020. "Risk management system and intelligent decision-making for prefabricated building project under deep learning modified teaching-learning-based optimization," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-15, July.
    • Handle: RePEc:plo:pone00:0235980
    DOI: 10.1371/journal.pone.0235980
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    References listed on IDEAS

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    1. Kunjie Yu & Xin Wang & Zhenlei Wang, 2016. "An improved teaching-learning-based optimization algorithm for numerical and engineering optimization problems," Journal of Intelligent Manufacturing, Springer, vol. 27(4), pages 831-843, August.
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    1. Maghsoud Amiri & Mohammad Hashemi-Tabatabaei & Mohammad Ghahremanloo & Mehdi Keshavarz-Ghorabaee & Edmundas Kazimieras Zavadskas & Arturas Kaklauskas, 2021. "Evaluating Life Cycle of Buildings Using an Integrated Approach Based on Quantitative-Qualitative and Simplified Best-Worst Methods (QQM-SBWM)," Sustainability, MDPI, vol. 13(8), pages 1-28, April.
    2. Junlong Peng & Jing Zhou & Fanyi Meng & Yan Yu, 2021. "Analysis on the hidden cost of prefabricated buildings based on FISM-BN," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-20, June.
    3. Zhan-Sheng Liu & Xin-Tong Meng & Ze-Zhong Xing & Cun-Fa Cao & Yue-Yue Jiao & An-Xiu Li, 2022. "Digital Twin-Based Intelligent Safety Risks Prediction of Prefabricated Construction Hoisting," Sustainability, MDPI, vol. 14(9), pages 1-22, April.
    4. Libiao Bai & Kanyin Zheng & Zhiguo Wang & Jiale Liu, 2022. "Service provider portfolio selection for project management using a BP neural network," Annals of Operations Research, Springer, vol. 308(1), pages 41-62, January.
    5. Clyde Zhengdao Li & Mingcong Hu & Bing Xiao & Zhe Chen & Vivian W. Y. Tam & Yiyu Zhao, 2021. "Mapping the Knowledge Domains of Emerging Advanced Technologies in the Management of Prefabricated Construction," Sustainability, MDPI, vol. 13(16), pages 1-31, August.

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