Knowledge discovery from databases (KDD) and data mining (DM) belong to the field of artificial intelligence (AI). The integration of AI into various segments of the construction industry is still in its infancy, but it is expected to be used more widely soon, driven by the development of databases and data warehouses. By using Building Information Modelling (BIM) technologies in the planning of new buildings, we will be able to obtain valuable data. The situation is different for old, existing buildings and the building engineering associated with these properties. Civil engineers, renovation planners and architects need knowledge of the building before renovation. This knowledge is much less than the possibilities that exist. Information about the building can be found in provincial archives. For historic buildings, 10%–15% of the plans, drawings, descriptions, or projects are available. The remaining 85% must be researched on site, which is a lengthy and costly process and hinders the construction process. The question arose as to how the findings from the study of buildings based on written and preserved sources can be applied to 85% of buildings for which no data is available. This paper presents the use of the collected data as an idea for an initiative to develop a database and modelling using AI algorithms. The research study investigates the feature “load-bearing wall” for residential buildings with basements and floors built between 1857 and 1948 in the former Austro-Hungarian Empire. The aim of this study is to create a model to predict the characteristics of a building for which no archival material is available. The study is based on the use of AI in the creation of decision trees to help engineers improve their knowledge of historic buildings in the former Austro-Hungarian Empire and building engineering for historic objects.

The role of NIMBY (“Not in My Backyard”) phenomena in sustainable urban development and tourism was analyzed, with a focus on stakeholder dynamics and implications for spatial planning. Based on an analysis of worldwide case studies, the main barriers and opportunities presented by these phenomena were identified, including the impact of project preparation time (t_p) and benefit realization time (t_b). A mixed methods approach was used, combining qualitative analysis of case studies with quantitative evaluation of project timelines. The results show that effective stakeholder engagement significantly reduces t_p, while maximizing t_b increases long-term benefits for communities and the environment. This research emphasizes the systemic interdependence of urban planning, environmental sustainability, tourism, and cultural heritage preservation, offering actionable insights for policymakers and developers. The conclusions emphasize the importance of participatory approaches to ensure equitable and sustainable urban transformation.