Machine Learning-Based Decision-Support System for the Adaptive Reuse of Historic Buildings: The Case of Salih Sefa Yazar Mansion

The adaptive reuse of cultural heritage buildings is an important approach that ensures the sustainable preservation of these structures; however, determining appropriate functions requires a systematic evaluation of user preferences. The aim of this study is to identify user preferences regarding the adaptive reuse of cultural heritage buildings and to develop an artificial intelligence-based decision-support model. The evaluation criteria identified through a literature review were validated through a two-round Delphi process involving 30 experts. The final framework consisted of six criteria: social and cultural value, historical value, authenticity value, construction technique, environmental value, and architectural and aesthetic value. Based on these criteria, a survey was conducted on the case of the Salih Sefa Yazar Mansion in Osmaniye, in which 886 participants were reached, and after data cleaning, 844 valid responses were retained to develop a model based on the Random Forest algorithm. The findings indicate that users prioritize social and cultural values and prefer functions that support public use. The model successfully predicted adaptive reuse alternatives such as museums, Art Gallery and cultural house, libraries, cafe-restaurants, and accommodation by evaluating demographic characteristics together with criterion priorities. In addition, scenario-based predictions were conducted using sample user profiles. In conclusion, the study proposes a decision-support approach that integrates expert opinion, user preferences, and artificial intelligence.