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Abstract
The construction of buildings invariably involves time and costs, and disruptions impact ongoing construction projects. Crisis situations in management strategies, structural confusion, and finan-cial miscalculations often arise due to misguided decision-making. This article proposes a method that combines the learning of Bayesian Networks and heuristic techniques to optimize deci-sion-making processes in construction scheduling. As an innovative approach in order to enhance construction management, the functioning of biological, molecular, and physical objects and nervous systems is considered, applying bionic features to mimic their efficiency and precision, thereby optimizing construction processes and improving coordination and decision-making. Bayesian Networks are used for probabilistic analysis, and heuristic methods guide quick deci-sion-making. The results demonstrate the effectiveness of Bayesian Networks and heuristic methods in data analysis and decision-making in construction engineering. The developed algo-rithm can be successfully applied to both erecting and planning construction projects.
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Authors (3)
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Maryna Poltavets
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Full text
- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.3390/app14114871
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Details
- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
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Applied Sciences-Basel
no. 14,
ISSN: 2076-3417 - Language:
- English
- Publication year:
- 2024
- Bibliographic description:
- Jakubczyk-Gałczyńska A., Siemaszko A., Poltavets M.: Optimizing Construction Engineering Management Using Metaheuristic Methods and Bayesian Networks// Applied Sciences-Basel -,iss. 11 (2024), s.4871-
- DOI:
- Digital Object Identifier (open in new tab) 10.3390/app14114871
- Sources of funding:
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- IDUB
- Free publication
- Verified by:
- Gdańsk University of Technology
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