近年來教育部每年投入約新臺幣300億元的經費於公立學校建築工程專案（Public school building projects, PSBPs）。在政府財源日益不足及少子化的情況下，如何有效管控專案的執行績效及資源最適化的分配，將成為政府推行公共建設計畫執行成功與否的關鍵。然而，高達95﹪的學校建築工程專案會發生工期延宕與成本追加之情況，造成專案執行績效普遍不佳。本研究分別透過模糊德爾菲法（Fuzzy Delphi Method）、關聯規則（Association Rules）及類神經網路（Artificial Neural Network）的採用，建構一套PSBPs的績效評估與預測系統，並能在專案初期的規劃設計與招標階段，及施工階段導入，以精準預測專案的未來執行績效並達預警功效。研究實際以全國大專院校62個PSBPs作為樣本，研究結果定義出11個影響建築工程專案績效的高度關聯性因子，並發現在廠商能力與經驗不佳、傳統發包方式、最低標、市場缺工等條件下，更容易導致專案績效較差。而類神經網路所建構的績效預測模型，經實證後發現整體預測準確率可高達84.21﹪，未來可提供學校、設計單位及營造廠商能即時掌握專案執行績效，並建立動態追蹤檢討與因應措施，以提升整體專案的滿意度。

In recent years, the Ministry of Education of Taiwan invested about NTD 30 billion a year in public school building projects (PSBP). Under the conditions of increasingly insufficient government revenues and low birth rate, effectively control of project performance and optimization of resource allocation will become the key to the success of the government’s public construction program. For example, in Taiwan, 95% of the school building projects’ durations are extended and incur increased costs, resulting in the generally poor project performance. In this study, a PSBP performance evaluation and prediction system was established by the Fuzzy Delphi Method, Association rules and Artificial Neural Network (ANN), and imported in the planning and design, bidding and construction phases at the beginning of the projects, to accurately predict future projects’ performance and realize early warnings about such projects. 62 PSBPs in Taiwan were used as the samples. According to the study’s results, 11 high correlation factors which influence building project performance were defined. It was found that poor project performance was more likely to occur due to firms’ poor capability and experience, traditional contract-issuing mode, minimum bid, and labor shortage in the market. Moreover, this empirical study shows that the performance prediction model established by ANN had an overall prediction accuracy of up to 84.21%. Hence, in the future, it can be provided to schools, design units and firms, to understand the project performance in real time, and to establish dynamic tracking review and response measures to improve the overall project satisfaction.

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