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Optimizing Construction Engineering Management Using Metaheuristic Methods and Bayesian Networks

Abstrakt

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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Wersja publikacji
Accepted albo Published Version
DOI:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.3390/app14114871
Licencja
Creative Commons: CC-BY otwiera się w nowej karcie

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Informacje szczegółowe

Kategoria:
Publikacja w czasopiśmie
Typ:
artykuły w czasopismach
Opublikowano w:
Applied Sciences-Basel nr 14,
ISSN: 2076-3417
Język:
angielski
Rok wydania:
2024
Opis bibliograficzny:
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:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.3390/app14114871
Źródła finansowania:
  • IDUB
  • COST_FREE
Weryfikacja:
Politechnika Gdańska

wyświetlono 18 razy

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