Conducting a technological study for the Krška vas–Čatež cycling route project and exploring the possibilities of using artificial intelligence to support implementation processes
Construction & Civil Engineering
This thesis investigates the potential integration of artificial intelligence (AI) within the technological study of the Krška vas–Čatež ob Savi cycling route project. The project comprises a 1.6-kilometre cycling connection along the regional road R2-419/1206, located in a sensitive natural environment near the Krka River within the Natura 2000 protected area.
Construction activities are restricted from July to September due to fish spawning protection.
An analysis of the conventional approach reveals major limitations in resource optimisation, reactive problem-solving, and static scheduling. The project is divided into five main construction phases requiring complex coordination of load-bearing structures, cantilever elements, and reinforced embankments within a spatially constrained area.
A review of international case studies on AI in construction demonstrates its potential to reduce project duration and coordination costs. The proposed AI-based framework adopts a four-layer architecture integrating IoT sensors for environmental monitoring, optimisation algorithms, and an application interface.
Core functions include optimising construction sequences using weather forecasts, automated water-quality monitoring, and computer-vision-based quality control.
A comparative analysis of traditional and AI-assisted approaches highlights the benefits of digitalisation in efficiency, transparency, and decision-making speed. The AI-supported system enables dynamic scheduling with daily optimisation and proactive issue prevention, while challenges remain regarding investment costs and technological readiness.
The cost-benefit analysis indicates a positive return on investment for infrastructure projects employing AI technologies. For smaller-scale projects, the study proposes a modular implementation strategy utilising cloud-based solutions and regional AI centres.
Overall, the thesis enhances understanding of digital transformation in construction and provides a practical framework for applying AI technologies in infrastructure project management.
