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| 題 名 | 應用資料探勘技術暨啟發式演化組合模型預測離散纖維加勁土壤之剪力強度參數=Shear Strength Prediction for Fiber-Reinforced Soils by Data Mining Techniques and Their Ensembles |
|---|---|
| 作 者 | 周瑞生; 楊國鑫; 林婕嫈; | 書刊名 | 中國土木水利工程學刊 |
| 卷 期 | 28:3 2016.09[民105.09] |
| 頁 次 | 頁205-218 |
| 分類號 | 441.12 |
| 關鍵詞 | 纖維加勁土壤; 地工合成材; 尖峰剪力強度; 資料探勘; 人工智慧; Fiber-reinforced soils; Geosynthetics; Peak shear strength; Data mining; Machine learning; Metaheuristic computation; |
| 語 文 | 中文(Chinese) |
| 中文摘要 | 目前預測纖維加勁土壤剪力強度參數的理論或經驗公式皆具有不確定性,因此無法用簡化的公式描述複雜的土壤與纖維互制行為。本研究回顧1983-2015年有關纖維加勁土壤三軸與直剪試驗文獻,擷取纖維加勁土壤屬性與實驗數據,建立一樣本資料庫。接續應用資料探勘技術,包含(1)分類與迴歸法;(2)機器學習法;(3)啟發式演化組合模型法,建構纖維加勁土壤摩擦角預測模式。分析結果發現,主要影響纖維加勁土壤剪力強度預測的因子有纖維含量、纖維長徑比、土壤摩擦角以及應力參數。研究顯示啟發式演化模型組合法中的分類-迴歸層級法Tiering SVM-(SVR/SVR)最優,其預測值與實際值間的相關係數 R = 0.89、平均絕對值誤差率MAPE < 4%、均方根誤差 < 2度、平均絕對誤差MAE < 2度,而各評估指標之改善效能優於理論或經驗公式達13.12 ~ 79.50%。 |
| 英文摘要 | The accuracy of theoretical and empirical models for predicting the shear strength of fiber-reinforced soils (FRS) is questionable because of the difficulty of using these simplified models to describe the complex mechanism of soilfiber interaction. This study compiled a large database of available high quality triaxial and direct shear tests on FRS documented in the literature from 1983 to 2015. The database includes information on the properties of sand, fibers, soilfiber interface, and stress parameters. Data mining technologies were employed to identify factors influencing shear strength and to predict the peak friction angle of FRS. The analysis techniques included (1) classification and regression methods, e.g., linear REGression (REG) analysis, Classification And Regression Tree (CART) analysis, a GENeralized LINear (GENLIN) Model, and CHi-squared Automatic Interaction Detection (CHAID); (2) machine learners, e.g., Artificial Neural Network (ANN) and Support Vector Machine/Regression (SVM/SVR); and (3) meta ensemble models, e.g., Voting, Bagging, Stacking and Tiering. The analytical results indicated that fiber content, fiber aspect ratio, soil friction angle and stress parameter had the largest effects on FRS shear strength. The optimal model obtained after further model training, cross- validation, and testing was the Tiering SVM-(SVR/SVR) method. The correlation coefficient of the prediction values with the measured values in the database was 0.89. The mean absolute percentage error was < 4%, root mean square error was < 2° , and mean absolute error was < 2°. The overall improvement in performance measures was 13.12% ~ 79.50% with respect to theoretical or empirical models. |
本系統中英文摘要資訊取自各篇刊載內容。