隨著全球都市化與永續發展的趨勢，閒置建築再利用已成為未來都市發展與
管理的重要策略。活化閒置建築並調整未來服務價值，進而延續其生命週期，已
被視為兼顧社會、經濟與環境觀點的永續性作法。然而，閒置建築再利用的成效
常因缺乏一套完整的評估體系與營運規劃而容易產生日後空間二度閒置的情況。
本研究根據AdaptSTAR model七大層面為研究基礎架構，建立建築再利用的評估
準則。進一步調查台灣62個不同類型的建築再利用案例，以類神經網路（Artificial
Neural Network, ANN）為基礎，建構一套預測再利用之類型與經營策略的決策模
型。研究結果顯示，所提出的決策模型，預測再利用類型準確度達到89%，經營
策略準確度達到78%，此一系統化的方法可將配合地區條件的差異進行調整，未
來將可作為有利的決策輔助工具。

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.

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