Decision support model based on case-based reasoning approach for estimating the restoration budget of historical buildings

Historical building conservation is an important work for most global organizations due to its potential cultural, social, economic, and urban renovation benefits. Accurately allocating budget in a limited time is a crucial factor that profoundly affects the restoration of historical buildings. An ineffective budget control mechanism often causes further damage to historical buildings that are facing urgent restoration needs. This study presents a new cost estimation concept based on the case-based reasoning (CBR) approach instead of a traditionally intuitive estimation method. In CBR model, two retrieval techniques, ‘Inductive Indexing’ and ‘Nearest Neighbor’, are then applied to retrieve relevant cases from the knowledge-based database. Two of the most common types of Taiwan historical buildings are tested to explore the restoration cost implications. The result reveals that the CBR solution can effectively predict the actual restoration cost, solve order change problems, and reduce the budget review time. These applications are also useful for many other countries, especially for those seismic belt regions, that are facing similar problems regarding historical building restoration.