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Analyzing the reasonable service life of buildings is a critical step to evaluate the decision for building utilization, reuse, or disposal. If buildings manifest service value, sustainable refurbishment and reuse methods can be employed to extend their service life. Previous studies on building service life largely focused on physical obsolescence. Few studies have analyzed other aspects. The objective of the present study was to propose a systematic approach to evaluate and predict the reasonable service life of buildings. First, the Fuzzy-Delphi Method (FDM) and analytical hierarchy process (AHP) were adopted to determine the final evaluation criteria and weights. Second, a mathematical model for predicting building service life was developed by combining the evaluation criteria, six obsolescence factors, and diagnostic scores. Finally, the model was applied to four case studies. The results produced by the model were consistent with those determined by an expert panel, verifying its effectiveness as a tool for decision making for formulating favorable suggestions concerning asset disposal, urban renewal, and renovation. Later obsolescence of buildings can be reduced by taking into account the proposed obsolescence criteria in the construction of new buildings to avoid implementing designs that are prone to obsolescence, thereby enhancing building service life.

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Because of global urbanization and sustainable development trends, reusing vacant buildings is a crucial strategy employed in urban development and management. Reusing and adjusting the future service values of unused buildings to extend building life cycles is a sustainable approach that benefits society, the economy, and the environment. However, repurposed spaces are easily re-discarded because a comprehensive system and operational plan for assessing the effects of building reuse remains unestablished. The research framework adopted in this study was based on the seven factors of the AdaptSTAR model; assessment criteria for building reuse were then created. In addition, 62 types of reused building cases in Taiwan were investigated and a decision model for reuse type prediction and business strategy was constructed on the basis of artificial neural networks. The results indicated that the proposed decision model yielded a reuse type accuracy of 89% and a business strategy accuracy of 78%. This systematic approach can be adjusted according to local conditions and applied as an effective decision support tool.