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Review of Learning Causal Bayesian Network for Diagnostical Analysis in Construction Resources Management

In: Proceedings of the 25th International Symposium on Advancement of Construction Management and Real Estate

Author

Listed:
  • Hongqin Fan

    (The Hong Kong Polytechnic University)

  • Zhenhua Huang

    (The Hong Kong Polytechnic University)

Abstract

With the increasing scale and complexity of infrastructure and building projects, construction resources must be managed and utilized to nearly full efficiency to meet the project needs and performance criteria. In recent decades, various computerized resource management systems have been deployed by contractors to keep daily records of resources including labor, material, and equipment, for the purposes of book-keeping and decision support. This paper discusses the learning of causal Bayesian network from data for knowledge acquisition, presentation, and decision support for diagnostic analysis of resources. The most important concept of Bayesian network learning, i.e., Markov blanket and d-separation, is studied for discovering the causal relationships among the decision variables and outcomes from data and then creating the Bayesian network for diagnostic analysis. One application is used to illustrate the Bayesian network learning for failure analysis of construction equipment.

Suggested Citation

  • Hongqin Fan & Zhenhua Huang, 2021. "Review of Learning Causal Bayesian Network for Diagnostical Analysis in Construction Resources Management," Springer Books, in: Xinhai Lu & Zuo Zhang & Weisheng Lu & Yi Peng (ed.), Proceedings of the 25th International Symposium on Advancement of Construction Management and Real Estate, pages 1099-1110, Springer.
    • Handle: RePEc:spr:sprchp:978-981-16-3587-8_73
    DOI: 10.1007/978-981-16-3587-8_73
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