Using Support Vector Machine and Logistic Regression Methods to Build Groutability Models for Permeation Grouting with Microfine Cement Grout

本研究之目的為建立粉土質砂層超微粒水泥漿體滲透灌漿可灌性之預測模式。因為本研究區域為富含較高細粒料之粉土質砂層以及所使用之超微粒水泥粒徑遠小於傳統卜特蘭水泥，遂傳統相對粒徑比可灌性經驗公式無法有效預測。因此，本研究藉由蒐集台灣地區（台北及高雄）240筆超微粒水泥漿體現地灌漿資料以支持向量機配合禁忌演算法及羅吉斯迴歸分別建立可灌性預測模式及公式。選擇可能影響可灌性之因子，除了參考傳統相對粒徑比可灌性經驗公式所使用之土壤通過百分比為10%所對應之粒徑大小( )、土壤通過百分比為15%所對應之粒徑大小( )外，亦將細粒料含量(FC)與水灰比(W/C)納入考慮。透過支持向量機配合禁忌演算法建立之模式，以十種不同資料組合數進行驗證，其預測準確率之平均值可達97.75%。再者，由本研究可灌性預測模式良好之預測結果顯示，應用支持向量機配合禁忌演算法搜尋參數建立可灌性預測模式進行預測，為相當可行之方法，亦說明支持向量機在處理複雜且非線性問題上有相當良好之表現。此外，應用羅吉斯迴歸所建立之預測公式與傳統相對粒徑比可灌性經驗公式一樣具有簡單之方程式，方便於工程師使用，也期待能易於廣泛應用在實際工程上。The purpose of this research is to establish the prediction model of the groutability of the silty sand soils using microfine cement grouts in a permeation grouting. Due to the fact that the region covered in this paper consists of the silty sand soils with relatively higher proportion of the fines content(FC) and the particle size of microfine cement used is considerably smaller than the conventional Portland cement, the existing empirical formula with relative particle size ratio is unable to provide effective predictions. Thus, this research derives the prediction model and formula from 240 data in Taiwan (Taipei and Kaohsiung) using Support Vector Machine(SVM) with Tabu Search(TS) and Logistic Regression(LR), respectively. In terms of selecting factors for the groutability, apart from the relative size for particles passing through soil with 10% and 15% permeability that are used in the conventional empirical formula with relative particle size ratio, this research also takes the fines content(FC) and the water-to-cement ratio(W/C) into account. By using SVM with TS, the model established can reach 97.75% precision of prediction. Moreover, the fine results of groutability prediction, not only indicate the feasibility of applying SVM with TS, but also explain the advantages of SVM in dealing with complicated and non-linear scenarios. In addition, the prediction formula derived from LR shares the same simplicity as in the conventional empirical formula with relative particle size ratio. It is hoped that, since engineers can use this formula with ease, it can also be widely used in applications and real-life constructions