營建業在工程初步設計階段，因為擁有資訊較少，對於工期多由過去相似工程之經驗做為依據來預測，雖能快速預測，其精準度卻很差。本研究蒐集國內外工期相關文獻，彙整出工程初設階段的影響因素，並且利用專家問卷、統計分析工具(SPSS)篩選出在工程初設階段顯著影響工程工期的因子，接著應用生物共生演算法最小平方差支持向量機(Symbiotic Organisms Search-Least Squares Support Vector Machines, SOS-LSSVM)建立工程初設階段自調適工期預測模式，藉由模式訓練與測試，找出輸入(影響因子)與輸出(工期)的映射關係，做出合理的工期預測。本研究透過文獻彙整、專家問卷調查與統計分析工具篩選出6個影響因子，接著根據此6個因子蒐集40筆實際工程案例並建立案例庫。將案例資料隨機分成5組，利用交叉驗證準則(Cross Validation)的概念進行訓練與測試。預測結果顯示絕對百分比誤差(Mean Absolute Percent Error, MAPE)值小於10%，屬於精準的預測，有效取代傳統主觀經驗之預測。最後將SOS-LSSVM與其他預測模式相比較，其結果亦優於迴歸分析(Regression)、倒傳類神經(BPNN)、支持向量機(SVM)、最小平方差支持向量機(LS-SVM)、演化式支持向量機推論模式(ESIM)與演化式最小平差支持向量機(ELSIM)，表示本研究應用SOS-LSSVM此模式更能有效且準確地做出預測。

Because there is less information in preliminary design phase, construction industry usually predict project duration by past experience in similar project. Although it can quickly make predictions, accuracy is poor. This research collected several related literatures of project duration, and aggregate Influential factors in preliminary design phase. Further, this research used expert-questionnaire and statistical analysis tools select six Influential factors, collecting forty actual cases based on these factors. Applying Symbiotic Organisms Search-Least Squares Support Vector Machines (SOS-LSSVM) to build Self-Tuned Project Duration Prediction Model. This research assessed model performance by using the K-fold cross validation method. The results showed that the prediction error is less than 10% in MAPE. Compared with other modules, the result is also better than the Regression, Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), Least Squares Support Vector Machines (LS-SVM), Evolutionary Support Vector Machine Inference Model (ESIM)and Evolutionary Least Squares Support Vector Machine (ELSIM). The model of this research can predict more effective and precise.

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