Artificial intelligence has significantly transformed numerous fields in recent years by enabling the efficient analysis of large amounts of data, process automation, and decision-making support. Although its application is rapidly increasing, it remains less widespread in the construction industry than in many other economic sectors. This presents an important opportunity for the development of new approaches that can improve the planning, management, and maintenance of infrastructure assets. Bridges are among the most important elements of road transport infrastructure, and their condition directly affects the safety, reliability, and efficiency of the transport network. Due to ageing, increasing traffic loads, and limited financial resources, the timely prediction of deterioration is becoming increasingly important. The goal of this thesis was to develop an artificial intelligence model for analysing the deterioration rate of bridges based on real-world data. Orange Data Mining software was used for data preparation and for the development and evaluation of predictive models. Based on selected attributes, different prediction models were developed. The results indicate that machine learning methods can effectively support the analysis of bridge deterioration rate and represent an important component of the emerging digital era in the construction industry.
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