住宅型建築物之修繕行為常發生於機能使用不便或構造受到損害時，而營造業者很難以過往之修繕資料發現影響修繕項目之因子彼此間關係，因此無法利用修繕資料使其反映至減少修繕問題發生或節省修繕成本上，因此本研究期望透過群集分析法將過往之建築物修繕資料進行分群，並從分群結果分析以預測建築物應修繕時機，本研究預期透過專家訪談方式逐一蒐集台灣地區建築物之詳細修繕紀錄，而為了符合統計學案例蒐集之母體原則，本研究預計於兩年內搜集96筆以上建築物修繕案例，詳細紀錄修繕之項目、時間及金額等，而如此預計蒐集案例數量將可滿足50-50分類比例之95%信賴區間以及10%誤差限制兩項條件。於案例蒐集完成後，本研究將結合SOMO最佳化模式與PSFCM分群演算法以發展SOMOC最佳分群演算法，利用SOMOC將可分群檢視建築物修繕資料之特性，找出影響建築物修繕行為之主要影響因子並協助營造業者透過修繕資料能得到影響修繕行為相關因子與修繕成本之關連性。 ; Building renovations are usually performed as required based on inconvenience or damage that has already taken place. Construction practitioners are seldom aware of the relationships between all the related factors and their corresponding costs. The purpose of this study is to apply a clustering algorithm to building renovations so as predict the timing of renovation. This study plans to collect building renovation data throughout the country. By meeting the statistical requirements of data sampling, it is suggested collecting 96 or more renovation samples under the assumptions of 95% confidence level and 10% limit error in a 50-50 category proportion. The SOMOC algorithm will be utilized to expose the tendency in view of basic building features. The anticipated benefits of the study not only prove the practicability of SOMOC but help the construction practitioners to learn from the past. ; 研究期間 9808 ~ 9907
