Introduction to the framework topic:
The dissertation focuses on the research and development of systems for occupational health and safety (OHS) management and construction quality assurance using artificial intelligence methods. In current practice, OHS and quality control processes are often performed separately, manually, and with limited predictive capability. This results in higher error probability, extended construction time, increased costs, and, in some cases, serious safety incidents. The research includes the development of predictive models based on machine learning algorithms and neural networks for risk identification, non-conformance detection, and the optimization of inspection and control activities.
Research focus:
The research applies machine learning algorithms, neural networks, and simulation modeling for risk prediction, identification of critical points in technological workflows, and optimization of inspection and control processes. Emphasis is placed on multi-criteria evaluation, considering not only safety and quality but also economic impacts, time efficiency, and environmental aspects of construction production.
Expected results and applications:
The aim of the dissertation is to develop an integrated methodological framework that combines OHS and quality management into a unified system supported by artificial intelligence. The outcome is the design and validation of an intelligent management system enabling early risk identification, more efficient quality control, and the reduction of costs associated with defects and safety incidents. The dissertation contributes to the digitalization and modernization of construction, with a strong emphasis on safety, quality, and sustainability.
