Optimizing Construction Engineering Management Using Metaheuristic Methods and Bayesian Networks
Source abstract
Abstract
The construction of buildings invariably involves time and costs, and disruptions impact ongoing construction projects. Crisis situations in management strategies, structural confusion, and financial 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 decision-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 decision-making. The results demonstrate the effectiveness of Bayesian Networks and heuristic methods in data analysis and decision-making in construction engineering. The developed algorithm can be successfully applied to both erecting and planning construction projects.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: BIM and Construction Integration · Occupational Health and Safety Research · Infrastructure Maintenance and Monitoring
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Civil and Environmental Engineering, Gdansk University of Technology, 80-233 Gdansk, Poland
Department of Industrial and Civil Construction, Zaporizhzhia National University, 69600 Zaporizhzhia, Ukraine
Funding
Gdansk University of Technology
References (37)
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