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

Hongqin Fan () and Zhenhua Huang ()
Additional contact information
Hongqin Fan: The Hong Kong Polytechnic University
Zhenhua Huang: The Hong Kong Polytechnic University

A chapter in Proceedings of the 25th International Symposium on Advancement of Construction Management and Real Estate, 2021, pp 1099-1110 from Springer

Abstract: 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.

Keywords: Causal relationships; Bayesian network; Artificial intelligence; Construction resources; Diagnostic analysis (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-16-3587-8_73

Ordering information: This item can be ordered from
http://www.springer.com/9789811635878

DOI: 10.1007/978-981-16-3587-8_73

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().