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

摘要: 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 crucial factor that profoundly affects the restoration of historical buildings. An ineffective control mechanism often causes further damage buildings are facing urgent needs. This study presents new cost estimation concept based on case-based reasoning (CBR) approach instead traditionally intuitive method. In CBR model, two retrieval techniques, 'Inductive Indexing' 'Nearest Neighbor', then applied retrieve relevant cases from knowledge-based database. Two common types Taiwan tested explore implications. The result reveals solution can effectively predict actual cost, solve order change problems, reduce review time. These applications also useful many other countries, especially those seismic belt regions, similar problems regarding restoration.